Patent Application
20200188733
This disclosure relates to a system and method for: (i) recording data related to a physiological parameter of a person engaged in a physical activity (e.g., an impact experienced by a player engaged in a contact sport), (ii) analyzing the recorded data related to the physiological parameter while the person is engaged in a physical activity (e.g., is the experienced impact greater than a predetermined threshold), and (iii) providing post-physical activity analysis of the recorded data to make suggested changes in how the person engages in the physical activity.
There is a concern in various contact sports, such as football, lacrosse and hockey, of brain injury due to impacts to the head of an individual engaged in playing the contact sport. During such physical activity, the head of the individual is often subjected to contact which results in an impact to the skull and brain of the individual, as well as the movement of the head or body part itself.
While considerable research has been under taken in the scientific community, a fair amount of information regarding the response of the brain to head accelerations in the linear and rotational directions and even less about the correspondence between specific impact forces and injury, particularly with respect to injuries caused by repeated exposure to impact forces of a lower level than those that result in a catastrophic injury or fatality. A considerable amount of what was known is derived from animal studies, studies of cadavers under specific directional and predictable forces (i.e. a head-on collision test), from crash dummies, from human volunteers in well-defined but limited impact exposures or from other simplistic mechanical models. The conventional application of known forces and/or measurement of forces applied to animals, cadavers, crash dummies, and human volunteers limit our knowledge of a relationship between forces applied to a living human head and any resultant severe brain injury. These prior studies also have limited value as they typically relate to research in non-contact sports settings, such as automobile safety area.
The concern for sports-related injuries, particularly to the head, is higher than ever. The Center for Disease Control and Prevention estimates that the incidence of sports-related mild traumatic brain injury (MTBI) approaches 300,000 annually in the United States. Approximately one-third of these injuries occur in football, with MTBI being a major source of lost playing time. Head injuries accounted for 13.3% of all football injuries to boys and 4.4% of all soccer injuries to both boys and girls in a large study of high school sports injuries. Approximately 62,800 MTBI cases occur annually among high school varsity athletes, with football accounting for about 63% of cases. It has been reported that concussions in hockey affect 10% of the athletes and makeup 12%-14% of all injuries.
For example, a typical range of 4-6 concussions per year in a football team of 90 players (7%), and 6 per year from a hockey team with 28 players (21%) is not uncommon. In rugby, concussions can affect as many as 40% of players on a team each year. Concussions, particularly when repeated multiple times, significantly threaten the long-term health of the athlete. The health care costs associated with MTBI in sports are estimated to be in the hundreds of millions of dollars annually. The National Center for Injury Prevention and Control considers sports-related traumatic brain injury (mild and severe) an important public health problem because of the high incidence of these injuries, the relative youth of those being injured with possible long term disability, and the danger of cumulative effects from repeat incidences.
Athletes who suffer head impacts during a practice or game situation often find it difficult to assess the severity of the impact even with the assistance of coaches and trainers. In an attempt to assess the severity of the impact, devices have been developed that attempt to record the acceleration/deceleration of a player's head when the player experiences an impact. Examples of such devices are discussed within patents (e.g., U.S. Pat Nos. 10,105,076, 9,622,661, 8,797,165, and 8,548,768) that are assigned to the current assignee of this application. While these devices may be able to detect and record acceleration/deceleration of a player's head when the player experiences an impact, these devices cannot determine if the impacts experienced are uncommon for the player or if the number/frequency of impacts that the player is experiencing are uncommon for the player's level and/or position. For example, a player may not be using proper tackling form (e.g., leading with the crown of his helmet) or after returning from an injury, the player may improperly alter his form to protect the part of the player's body that was previously injured. In a further example, the player may be experiencing more impacts during a day, week, or season in comparison to players of similar playing levels and/or positions, which may inform a coach or member of the coaching staff that the player needs additional instruction on tackling techniques and/or additional breaks to help ensure that they do not maintain a high head impact exposure (“HIE”) load.
Based on the above, there is a demand for a physiological measuring and reporting system that is designed for post-activity analysis, which takes into account the player's level and/or position in determining if the player is experiencing impacts that are uncommon for the player's level and/or position. Additionally, there is also a demand that the foregoing system not only takes into account the player's level and/or position, but uses algorithms that self-update the thresholds that are utilized to determine if the impacts that the player is experiencing are uncommon for the player's level and/or position. Further, there is a demand for a graphical user interface (“GUI”) that is shown on a display, wherein the GUI includes summary screens and other intuitive screens to increase the usability of the system and efficiently present information to the authorized user (e.g., coach, trainer, equipment manager, other member of the coaching staff, administrator, parent of the player, or the player, or other similar individuals).
This disclosure addresses shortcomings discussed above and other problems and provides advantages and aspects not provided by the prior art of this type. A full discussion of the features and advantages of the present disclosure is deferred to the following detailed description, which proceeds with reference to the accompanying drawings.
The present disclosure provides a system for monitoring at least one physiological parameter of multiple players engaged in a contact sport. The system includes a plurality of monitoring units, each monitoring unit being associated with a specific or target player and having a sensor assembly that actively monitors at least one physiological parameter of the target player while engaged in the contact sport to determine a physiological parameter value. The monitoring unit selectively generates a first alert when the physiological parameter value exceeds a first predetermined threshold based upon a single incidence of the physiological parameter and a second alert when the physiological parameter value exceeds a second predetermined threshold based upon cumulative incidences of the physiological parameter. The system also includes a portable alert unit that receives the first alert and second alert transmitted from a particular monitoring unit and displays information relating to the particular alert to a user of the system.
The system also provides training opportunities geared for the authorized user, typically the coaching staff and/or athletic trainers. The training opportunities are based on comparing an individual player's data, a subset of the team's data, or the team's data against similar collections of data on different scales (e.g., team scale, local scale, regional scale, national scale, or nation-wide/worldwide scale). Specifically, training opportunities for an individual player may be based on comparing a player's recent physiological parameter data against various collections of historical physiological parameter data for other similar players (e.g., players that play at a similar playing level and/or position). For example, various collections of data may include: (A) player's own historical data, (B) team's historical data, (C) historical local player, position, unit or team data, (D) historical regional player, position, unit or team data, or (E) historical national player, position, unit or team data. Other training opportunities for an individual player may also be based on comparing a player's recent physiological parameter data against various collections of recent physiological parameter data for other similar players. For example, various collections of data may include: (A) team's recent data, (B) recent local player, position, unit or team data, (C) recent regional player, position, unit or team data, or (D) recent national player, position, unit, or team data. Alternatively, the system may adjust the algorithms that generate the training opportunities based upon that player's historical data, not solely historical data of other similar players.
Additionally, training opportunities for all players that play a specific position on a team (e.g., all players within a team that primary play one position, such as lineman or running backs) and their historical data may be based on comparing a team's recent positional physiological parameter data against various collections of historical physiological parameter data for that specific position or other similar positions. For example, collections of data may include: (A) position's own historical data, (B) team's historical data, (C) historical local position, unit or team data, (D) historical regional position unit or team data, or (E) historical national position, unit or team data. Other training opportunities for all players that play a specific position on a team may also be based on comparing a team's positional recent physiological parameter data against various collections of recent physiological parameter data for other similar players. For example, various collections of data may include: (A) team's recent data, (B) recent local position, unit or team data, (C) recent regional position unit or team data, or (D) recent national position, unit, or team data.
Further, training opportunities for a unit's (e.g., all players within a team that primarily play in one unit, such as the “offense,” “defense,” kickoff,” “field goal” unit) historical data may be based on comparing a unit's recent physiological parameter data against various collections of historical physiological parameter data for specific units or other similar units. For example, collections of data may include: (A) unit's own historical data, (B) team's historical data, (C) historical local unit or team data, (D) historical regional unit or team data, or (E) historical national unit or team data. Other training opportunities for a unit may also be based on comparing a unit's recent physiological parameter data against various collections of recent physiological parameter data for other similar units. For example, such various collections of similar data may include: (A) team's recent data, (B) recent local unit or team data, (C) recent regional unit or team data, or (D) recent national unit or team data.
Moreover, training opportunities for a team's historical data may be based on comparing a team's recent physiological parameter data against various collections of historical physiological parameter data for other similar teams. For example, various collections of similar physiological data may include: (A) team's own historical data, (B) historical local team data, (C) historical regional team data, or (D) historical national team data. Other training opportunities for a team may also be based on comparing a team's recent physiological parameter data against various collections of recent physiological parameter data for other similar teams. For example, various collections of similar physiological data may include: (A) recent local team data, (B) recent regional team data, or (C) recent national team.
Also, it should be understood that other training opportunities are contemplated by this disclosure.
Additional advantages and novel features will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following and the accompanying drawings or may be learned by production or operation of the examples. The advantages of the present teachings may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations set forth in the detailed examples discussed below.
This patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. The drawing figures depict one or more implementations in accord with the present teachings, by way of example only, not by way of limitation. In the figures, like reference numerals refer to the same or similar elements.
In the following detailed description, numerous specific details are set forth by way of examples in order to provide a thorough understanding of the relevant teachings. However, it should be apparent to those skilled in the art that the present teachings may be practiced without such details. In other instances, well-known methods, procedures, components, and/or circuitry have been described at a relatively high-level, without detail, in order to avoid unnecessarily obscuring aspects of the present teachings.
While this disclosure includes a number of embodiments in many different forms, there is shown in the drawings and will herein be described in detail particular embodiments with the understanding that the present disclosure is to be considered as an exemplification of the principles of the disclosed methods and systems, and is not intended to limit the broad aspects of the disclosed concepts to the embodiments illustrated. As will be realized, the disclosed methods and systems are capable of other and different configurations and several details are capable of being modified all without departing from the scope of the disclosed methods and systems. For example, one or more of the following embodiments, in part or whole, may be combined consistent with the disclosed methods and systems. As such, one or more steps from the flow charts or components in the Figures may be selectively omitted and/or combined consistent with the disclosed methods and systems. Accordingly, the drawings, flow charts and detailed descriptions are to be regarded as illustrative in nature, not restrictive or limiting.
To provide post-physical activity analysis, the multi-functional system 10 utilizes a complex collection of algorithms to analyze data related to at least one physiological parameter (e.g., pressure) for a selected player or group of players to inform the authorized user that the selected player or group of players have experienced physiological parameters that are uncommon or atypical for the selected player or group. This complex collection of algorithms provides an unconventional solution to the problem of trying to understand what the player experiences during the activity. This unconventional solution is rooted in technology and provides information that was not available in conventional systems. This unconventional solution also represents an improvement in the subject technical field otherwise unrealized by conventional systems. Specifically, unlike conventional systems, the multi-functional system 10 determines if the player or group of players experiences, for example: (i) more alertable impacts then other player/groups, (ii) more high magnitude impacts then other player/groups, (iii) more impacts then the player or group of players has experienced in a past interval of time, (iv) more high magnitude impacts then the player or group of players has experienced in a past interval of time, (v) impacts in uncommon locations of a body part (e.g., irregular locations of the head) or patterns in comparison to other player/groups, (vi) impacts in uncommon locations of a body part (e.g., irregular locations of the head) or patterns in comparison to the impacts that the player or group of players has experienced in a past interval of time, and (vii) other determinations that are discussed below.
After system 10 makes these determinations about the player or group of players, the system 10 displays the data using a GUI on a display 28a in a unique and easy to understand format. Conventional devices could not provide this solution for at least the following reasons: (i) the significant processing power required for the in-helmet units, (ii) the considerable data storage requirements for the in-helmet units, (iii) a large enough pool of physiological parameter data to provide accurate thresholds for the algorithms, (iv) algorithms that allow for the thresholds to be self-updated in light of additional data that is added to the pool of physiological parameter data, (v) other hardware and software features that are discussed below, or (vi) other reasons that are known to one of skill in the art based on the disclosure herein.
The complex collection of algorithms are operational linked and tied to the multi-functional system 10, which ensures that the disclosed algorithms cannot preempt all uses of these algorithms beyond the system 10. Also, as detailed below, these algorithms are complicated and cannot be performed using a pen and paper or within the human mind. In addition, the GUI displays the results of the execution of these complex algorithms in a manner that is easily understandable by a human user, sometimes views such results on a small or handheld screen 28a, improves operation of computing devices. Additionally, translation of outcomes from these complex algorithms through the GUI onto images displayed for a user, improves comprehension of considerable quantities of highly processed data. For example, an exemplary algorithm from this complex collection of algorithms requires: taking inputs from multiple sensors, selecting some data provided by the sensors, ignoring some of the data that was provided by the sensors, performing multiple calculations on a selected subset of the data, combining the data from these multiple calculations and then outputting that data within a short amount of time (e.g., preferably less than a minute), all for multiple members on a team.
The exemplary algorithm cannot be performed with a pen and paper or within the human mind because the algorithm requires analyzing millions of data points to find similarities between individuals, grouping individuals that have similarities together, determining the physiological parameters that these similar individuals experience, determining the level of the physiological parameter that a similar individual must experience to place an individual above 95% of these similar individuals, obtaining additional physiological parameter data about the similar individuals, making a new determination about the level of the physiological parameter that a similar individual must experience to place above 95% of the similar individuals in light of the newly obtained data, comparing physiological parameter data from similar individuals against this new threshold, providing the results of this analysis to the authorized user, and then repeating the above steps over a relatively short time period (e.g., every day) and for hundreds of different groups of players. Additional reasons why this complex collection of algorithms cannot be performed with a pen and paper or within the human mind will be obvious to one of skill in the art based on the below disclosure.
Additionally, multi-functional system 10 provides multiple improvements over conventional systems, including improving the efficiency of monitoring players to identify coaching and training opportunities via the GUI that is displayed on a screen 28a. The authorized user can then utilize the information provided by the GUI to proactively identify, coach and adjust player behavior, group behavior, or team behavior through new/different training techniques and practice plans. For example, in the context where the physiological parameter is pressure exerted on the player's head due to a helmet impact, the multi-functional system 10 learns the type of impacts a player experiences and identifies if the player deviates from these impact types over time. Also, the system 10 can determine if the impacts experienced by the player deviate from other similarly situated players. Deviations indicate to the users of the system 10 that: (i) new or different drills should be utilized or (ii) additional coaching within a particular drill should be utilized to train the player in order to alter the number, magnitude, or type of impacts the player is experiencing or may experience during future play of the contact sport.
Additionally, the system 10 learns the type of impacts a subset of players of the team experiences and identifies if the subset of the team deviates from these types of impacts over time. Also, the system 10 can determine if the impacts experienced by the player subset deviate from other similar subsets on that team or other teams. Deviations indicate to the users of the system 10 that new or different drills could be utilized to train the player subset in order to alter the number, magnitude, or type of impacts the player subset is experiencing or may experience during future play of the contact sport.
Further, system 10 learns the type of impacts an entire team experiences and identifies if the team deviates from these types of impacts over time. Also, the system 10 can determine if the impacts experienced by the team deviate from similar teams. Deviations indicate to the users of the system 10 that new or different drills could be utilized to train the team in order to alter the number, magnitude, or type of impacts the team is experiencing or may experience during future play of the contact sport. Moreover, system 10 allows multiple people to collaborate, locally or remotely, about the use of these training techniques and practice plans. Also, the system 10 allows for the tracking of a player's history (e.g., impacts, sizes, medical histories, equipment, etc.) and other relevant information to aid in the monitoring and training of the player.
The present disclosure, as will be discussed in detail below, is capable of monitoring and analyzing data gathered from any body part of an individual but has particular application in monitoring the human head. For example, system 10 could be employed within protective equipment other than helmets to monitor a player's shins, knees, hips, chest, shoulders, elbows, or wrists. Therefore, any reference to a body part is understood to encompass the head and any reference to the head alone is intended to include applicability to any body part. For ease of discussion and illustration, discussion of the prior art and the present disclosure is directed to the human head, by way of example and is not intended to limit the scope of discussion to the human head.
Since most contact sports involve multi-player teams, the system 10 actively monitors, records, analyzes, and transmits data related to the selected physiological parameter(s) for all players on the team throughout the course of play, including a game or practice session. System 10 is especially well suited for helmeted team sports where players are susceptible to head impacts and injuries; for example, football, hockey, and lacrosse. The system 10 could also be applied to helmets for a: baseball player, cyclist, polo player, equestrian rider, rock climber, auto racer, motorcycle rider, motocross racer, skier, skater, ice skater, snowboarder, snow skier and other snow or water athletes, skydiver.
The IHU or monitoring unit 22 includes a sensor assembly 120 and a control module 130. The IHU 22 may be specifically designed and programmed to: (i) measure and record data related to a physiological parameter, (ii) analyze the recorded data using the algorithm shown in
In an alternative embodiment, the IHU 22 may be morphed to fit within a mouthguard. It should be understood that this alternative embodiment is not preferred because it requires placing a battery and other functional components within a user's mouth. Additionally, this configuration may create a device that exceeds the FCC transmission limits associated with devices that are placed within a player's mouth when the mouth guard sends an alert to the receiving device 24. Nevertheless, it may be desirable to be able to record data when a player is not wearing his helmet 20 or is involved in an activity that does not utilize a helmet 20. Thus, the designer may omit some of the components from the IHU 22 in order to overcome some of the limitations associated with this morphed embodiment. For example, the designer may create a mouth guard that only records impact data and does not transmit alert data. This mouth guard configuration can still be utilized by the system 10 because the impact data that it collects can be analyzed by the complex collection of algorithms to provide training opportunity determinations. In further alternative embodiments and as described above, the IHU 22 may be placed in other equipment that is worn on the player's body, such as shins, knees, hips, chest, shoulders, elbows, and wrists. For example, the IHU 22 may be morphed to fit into a set of shoulder pads, a shin guard, jersey, pants, girdle, elbow pads, shoes, knee pads, gloves, jackets, boots, life vest, or other types of equipment that is worn by the player.
Other components that may be included within the system 10 include: (i) the receiving device 24, which may be an alerting unit 26, (ii) a remote terminal 28, (iii) a team database 32, and (iv) national database 38. Different configurations of these other components (e.g., the combination of some of these components into a single combination component) are discussed in connection with
It should be understood that system 10 may include additional or fewer components. For example, system 10 may include additional database(s) that is external to the system and the databases 32, 38 therein. This external database(s) may store player/team data, such as: (i) videotape of the games, scrimmages, or practices, (ii) activity levels of the player/team, or (iii) historical information about the player/team. In another embodiment, the system 10 may include the ability to connect other external systems in order to pull the data from these other external systems into this system 10 for analysis. For example, in this embodiment, the system 10 may be able to connect to a 3rd party external impact detection system in order to pull in the data that was recorded within that external impact detection system in order to analyze the data using the algorithms contained within the system 10 disclosed herein. In another example, these external systems may include a register's database at the school that includes the grades of the player. This information can be pulled into the system 10 disclosed herein to inform the authorized user of whether the player is authorized to play that week. In a further example, these external systems may include a weather database that can be used to record the weather during games or practice sessions. Also, this weather database could be used to send an alert to the receiving device 24 to inform the authorized user that the game or practice should be canceled or moved indoors. It should be understood that these are just examples of other components that may be added into the system 10 to provide the authorized user with additional information about the player and these examples are non-limiting.
a. Sensor Assembly Contained within the Helmet
Referring back to
In a slightly different implementation, the sensor assembly 120 is positioned adjacent to the innermost portion of the overliner 21, such that when the overliner 21 is positioned within the player's helmet 20, there substantially no padding material that is positioned between the player's head and the sensor assembly 120. In this implementation, the sensor assembly 120 is positioned extremely close to the player's head, but is still not directly touching the player's head because the extent of the overliner 21 is still placed between the player's head and the sensor assembly 120. In yet another implementation, the sensor assembly 120 may be placed in direct contact with the player's head when the helmet is worn by the player.
Alternatively, the sensor assembly 120 may be integrally formed as part of the energy attenuation assembly 20c. For example, the skin of each member of the energy attenuation assembly 20c or the lattice structure contained within each member of the energy attenuation assembly 20c may be coated with or integrally formed with a material that can act as a sensor. Specifically, the skin or the lattice structure contained within the energy attenuation assembly 20c may be composed of a material that includes carbon nanotubes blended within a light sensitive polyurethane. Additional information about this material and other possible materials are discussed within N. Hu, et al. Investigation on sensitivity of a polymer/carbon nanotube composite strain sensor. Carbon, 48 (3) (2010), pp. 680-687 and Radeti, M. & Cortes, Pedro & Kubas, George & Cook, Jim & Gade, Ravi & Oder, T. (2016). The Sensing Properties of Fuzzy Carbon Nanotube Based Silica Fibers: Ceramic Transactions, Volume CCLX. 10.1002/9781119323624.ch13, both of which are fully incorporated herein by reference. It should be understood that a combination of the above described sensor placements may be utilized within a helmet, wherein one sensor within the sensor assembly 120 may be placed within an energy attenuation member and another sensor within the sensor assembly 120 may be integrally formed within the energy attenuation assembly 20c. It should be further understood that the sensor assembly 120 may be placed in other locations within the helmet 20 and may be coupled to other structures within the helmet 20.
Although the IHU 22 is shown and described to include five sensors 120a-e within the sensor assembly 120, one of ordinary skill in the art recognizes that the IHU 22 may have more or fewer sensors (e.g., between 1 sensor and 100 sensors). The number of sensors may depend on the application and the information that is required to meet the needs of the application. For monitoring at least one physiological parameter of player engaged in a sports activity, (e.g., American football), the location of pressure applied by an impact is useful in determining the severity of the impact. In another application, the location may not be as important and in these applications a single sensor may be used. For example, a single sensor may be sufficient, if the IHU 22 is utilized to determine when a single impact helmet should not be worn after experiencing an impact with a large enough magnitude.
i. Sensors Contained within the Sensor Assembly
In one exemplary embodiment, the sensors 120a-120e of the sensor assembly 120 are formed from an electret film, which has a unique, strong electromechanical response to an impact(s) to the helmet 20. The film is based on a polyolefin material manufactured in a continuous biaxial orientation process that stretches the film in two perpendicular directions (machine direction and the transverse direction). Further the film is expanded in thickness at high-pressure gas-diffusion-expansion (GDE) process. The structure of electret film consists of flat voids separated by thin polyolefin layers. Typically the electret film is 70-80 μm thick. The voids are made by compounding small particles, which function as rupture nuclei and form closed lens like cavities to the film during the biaxial orientation. The voids are enlarged at with the GDE process, which more than doubles the thickness and elasticity of the film by increasing the size of air-voids inside it. Electromechanical response with GDE processed film is over 10-fold compared to non-swelled film. A permanent electric charge is injected into the material by corona charging it in a high electric field. This causes electric breakdowns to occur inside the material, thus charging the void interfaces inside the film in order to form an electret material capable of interacting with its environment. Thin metal electrodes are, for example, arranged by screen-printing them first to 75-100 μm polyester film and laminating together with electret film. Vacuum evaporation to both surfaces of the film is also possible for actuator purposes. Other typical ways to arrange electrodes are using aluminum-polyester laminate and etching the electrode pattern prior to laminating with electret film. In another implementation, the sensors 120a-120e are made of piezoelectric material (e.g., Polyvinylidene Flouride (PVDF) and Lead Ziconate Titanate (PZT)). It should be understood that other materials or configurations of materials may be utilized to form the sensors within the sensor assembly.
b. Control Module Contained within the Helmet
i. Signal Conditioner and Filter
The signal conditioner 130a and a filter 130b are utilized by the control module 130, as necessary, to condition the signals that are received from the sensors 120a-120e. For example, the signal conditioner 130a and filter 130b may be utilized to filter out low and high-frequency noise that is generated by the sensors 120a-120e. Such noise may be introduced into the system due to environmental conditions, miss-alignment of the sensors 120a-120e within the helmet 20, or other similar factors. It should be understood that in some embodiments, a signal conditioner 130a and filter 130b may not be utilized or only one of these components may be utilized.
ii. Microcontroller
The microcontroller 130c may be a processor that is specifically designed for use within the IHU 22. The microcontroller 130c includes memory that store the algorithms described in
It should also be understood that at least an extent of the data collected by the sensor assembly 120 of the system 10 must be analyzed by the microcontroller 130c within the helmet 20 and this data cannot be sent to a person for performing the necessary calculations within their head or a central computer that is remotely located from the helmet 20. For example, a hypothetical system that is designed to transfer all of the data collected by the sensor assembly 120 to a central computer for analysis would not be able to function as the system 10 described herein for at least the following reasons. First, the hypothetical system's in-helmet unit would not know when to wake up and record the data because the hypothetical system would not know if the impact was over the noise threshold. Second, the hypothetical system would take too long to transfer and process this data on a remote computer or attempt to have a person perform these calculations, which would cause the same problems identified above. Third, increasing the amount of data that is transferred out of the helmet 20 will increase the power requirements of the hypothetical system's in-helmet unit, which in turn will require the use of a larger battery to maintain the same length of operational time between charges. However, increasing the battery size is not permissible due to the space and footprint constraints that apply to the hypothetical system's in-helmet unit. The IHU 22 of the multi-functional system 10 can last multiple weeks and even over one year without needing to be recharged. This is because: (i) in normal operation (e.g., continuous monitoring for alertable impacts), the IHU 22 may consume about 12-20 uA, (ii) in a deep sleep state (e.g., everything is off except timekeeping), the IHU 22 may consume about 8 uA, and (ii) in an alert state (e.g., the IHU 22 is trying to send an alert to the alert unit 26) the IHU 22 may consume about 1-5 mA. Increasing the size of the battery to maintain these operational times will undesirably: (i) add weight to the IHU 22, (ii) make the IHU 22 more expensive to manufacture, and (iii) require additional analysis to determine if it is even possible to place a larger battery within the helmet 20 without requiring alterations to the helmet's design.
iii. Telemetry Module
The IHU 22 uses the telemetry module 130d to wirelessly connect and transmit physiological parameter data to the receiving unit 24 via communication links 40, 50, 52, 54, 56 (shown in
An example of a custom designed wireless communication technology that is specifically designed for this application is described below. This wireless communication technology is based on a time division multiple access (TDMA) approach. Approximately every 9.6 seconds, the receiving unit 24 broadcasts a ping 75 times every 30 ms. After each ping, the receiving unit 24 listens for an IHU 22 that is scheduled to respond at a specific ping set and time slot. There are two-time slots per ping where an IHU 22 can respond. The ping plus time slot listen period is a “superframe.” During setup, the IHU 22 is paired with the receiving unit 24 to align the wireless communication settings (e.g. channel, PAN ID, etc) as well as a timeslot within a superframe between these devices. Since communications are happening asynchronously and the actual communication time is in a small window, the IHU 22 wakes up periodically at some multiple of ping windows (the 75 superframes). If it hears a ping from it's receiving unit 24, the IHU 22 calculates the time required to wakeup on the next ping cycle (9.6 sec+an offset it calculates to wakeup right before the appropriate superframe). After an IHU 22 checks in with the receiving unit 24, the receiving unit 24 responds with an acknowledgment. Included in this acknowledgment are the player's 2nd, 3rd and 4th thresholds. The IHU 22 monitors the impacts on the player wearing the IHU 22 and reports an alert to the receiving unit 24, if the impacts exceed the 3rd or 4th thresholds. Is should be understood that other multiplexing techniques may be used instead of TDMA, such as code division multiple access (CDMA), or frequency division multiple access (FDMA) technology. Also, system 10 may analyze the operating environment and pick a channel that has the least amount of noise that is within the operating range of the radios/transmitters. Once a new frequency has been identified, the receiving unit 24 instructs all or a subset of the telemetry modules 130d contained within the IHUs 22 to switch over to this new frequency.
iv. Encoder and Power Source
The encoder 130e is designed to encode the alert data and/or the impact data before the telemetry modules 130d transmit this data. Specifically, the encoding of this data includes adding a unique identifier to the data, such as player number, name, the serial number of the IHU 22, the location of the player on the team roster, and etc. While the encoder 130e is shown as separate from the telemetry element 130d, the encoder 130d can be integrated within the telemetry element 130d or the microcontroller 130c. In certain embodiments, encoder 130e can encrypt the data to increase the security of the data. The power source 130e may be designed to last at least as long as the activity, preferably as long as a week, more preferably more than a week. To meet this design requirement, the system 10 can utilize a non-removable rechargeable battery, removable rechargeable battery, or a removable non-rechargeable battery.
v. Activity Sensing Module
The activity sensing module 130g may be used to turn the IHU 22 ON or OFF based on the movement of the helmet 20 that it is installed therein. For example, moving the helmet over a predefined amount of time, hitting and/or shaking the IHU 22 will turn ON. If the helmet has not moved for another predefined amount of time (e.g., 7 minutes) or has not been hitting and/or shaking for a predefined amount of time the IHU 22 will turn OFF. It should be understood that the activity sensing module 130g will default to the one position even when the movement of the helmet is extremely low, as this helps ensure that the IHU 22 is activated during the activity. Alternatively, the helmet 20 may have control buttons, such as a power button and a configuration button. In a further alternative, the IHU 22 may turn ON and OFF based on a shaking pattern, proximity to a radio beacon (e.g., Wi-Fi beacon, Bluetooth, etc.), a timer, a completion of a circuit based on the connection of the player's chin strap, pressure on an extent of the sensor assembly 120, a combination of the above, or other similar methods of turning ON and OFF an electronic circuit in close proximity to a player's head.
c. IHU Performs the Algorithms Shown in
As discussed above, the IHU 22 and the algorithms shown in
i. Threshold Values/Ranges Utilized by the IHU
To determine the impact magnitude value, multiple threshold values/ranges are programmed into the memory of the IHU 22. Some of these threshold values/ranges are standardized across all IHUs 22. In other words, the same or non-custom threshold value is programmed into each and every IHU 22. For example, a predetermined noise threshold and a 1st threshold/an impact matrix threshold are standardized values that are the same across all IHUs 22. These values can be standardized and do not need to be tailored to a player who is going to utilize the IHU 22 because these values are only used to determine if an impact occurred and if that impact has a magnitude that is high enough to warrant analysis.
Other threshold values/ranges are not standardized across all IHUs 22. In other words, different or custom threshold values/ranges may be programmed into the IHU 22. The non-standardized or custom threshold values/ranges are based upon information that is entered or obtained from the player who is going to utilize the IHU 22. In particular, tailoring the IHU 22 to the specific player is desirable because the pressure that is applied to a player's head during the course of the activity varies between playing levels, such as the pressure levels seen at the youth level in comparison to the levels seen at the NFL professional level. Tailoring the IHU 22 to the specific player by inputting non-standardized or custom threshold values/ranges within the IHU 22 creates a specialized machine with specialized functionality to determine the impact magnitude value for impacts that the specific player(s) experiences during the activity. This specialized IHU 22 provides more accurate information, thereby providing a significant technical improvement to the system 10. Additionally, the accuracy of the information provided by the specialized IHU 22 improves the monitoring capabilities of the system 10 and the efficiency of the authorized user's ability to make suggested changes in how the monitored player(s) engages in the physical activity.
The non-standardized or custom threshold values/ranges in this embodiment are: (i) 2nd threshold or high magnitude impact threshold, (ii) 3rd threshold or single impact alert, (iii) 4th threshold or a cumulative impact alert threshold and (iv) ranges of impact magnitude values that are associated with the severity values. The system 10 determines the values of these non-standardized threshold values/ranges based on the player's position (e.g., offensive line, running backs, quarterback, wide receivers, defensive linemen, linebackers, defensive backs and special teams) and level (e.g., youth, high school, college and professional players). For example, the system 10 may have between 5 and 100 different values for each threshold/value, preferable between 20 and 60 different values for each threshold/value, and most preferably between 25 and 40 different values for each threshold/value. To select the proper custom thresholds/values, a player or authorized user will set up the IHU 22 by programming the player's level and position into the remote terminal 28. The remote terminal 28 pulls the custom thresholds/values from the team/national database 32, 38 that correspond to the player's level and position. In particular, these corresponding values were previously determined based on a statistical analysis of data that have been collected using the proprietary technologies owned by the assignee of the present Application and are disclosed in U.S. Pat. Nos. 10,105,076, 9,622,661, 8,797,165, and 8,548,768. After the custom thresholds/values have been determined, these thresholds/values are sent to the receiving device 24 in order to relay this information to the IHU 22. Overall, it should be understood that a player who plays offensive line at the youth level will have non-standarized or custom threshold values/ranges that are different than a player who plays running back at the NCAA level.
In an alternative embodiment, system 10 may select a set of custom thresholds/values based on the player's level and position that is entered into the system 10 using the remote terminal 28. After the player has experienced over a predefined number of impacts (e.g., over 50 impacts and preferably over 100 impacts), the system 10 may adjust the custom thresholds/values or may select a different set of custom thresholds/values from the team/national database 32, 38. This adjustment or the selection of a different set of custom thresholds/values will aid the system 10 in providing more accurate information to the authorized user. For example, a player who plays two different positions (e.g., quarterback and linebacker) can only select one of these positions during the initial setup, which in turn may lead to over/under-reporting of alerts because the selection of custom thresholds/values were not specifically tailored to the individual player's playing style. In another example, a player who plays quarterback may not experience impacts that a quarterback would typically experience based on the player's playing style. For example, the quarterback may run the ball an unusual amount of times, which causes the quarterback to experience impacts that are more similar to a running back than a quarterback. In a further example, a player who has a medical condition may be susceptible to high magnitude impacts; therefore, the custom thresholds/values should be adjusted to account for these medical conditions. Thus, this adjustment or the selection of a different set of thresholds/values can use the information from the experienced impacts to help ensure that the proper custom thresholds/values are selected for the player.
Adjustments to the selected custom thresholds/values may be made using a learning algorithm that takes into account at least a plurality of the following: (i) the player's position and level, (ii) the impacts the player has experienced, (iii) the preset custom thresholds/values contained within the team/national database 32, 38, (iv) player's medical condition(s), and/or (v) videotape analysis. Alternatively, the adjustments to the selected custom thresholds/values may be made by reducing or increasing the selected thresholds/values be a certain percentage that is based on the percentage over the mean impact levels that are experienced by players who play at a similar level and position. For example, if the player is experiencing impact levels that are 10% over the mean impact levels for other similar players, then the player's thresholds/values are increased by 10%. It should be understood that these adjustments to the thresholds/values or selection of a different set of thresholds/values (e.g., running back values instead of quarterback values) may be provided to the authorized user to ensure that the authorized user desired to make this change or may be automatically changed without the authorized user's input or knowledge. Also, the authorized user may request that the system 10 perform the above analysis on any player, even if the system does not detect or suggest such a change. This may be beneficial if an authorized user believes that the player is not experiencing enough alertable impacts.
ii. IHU Detects an Impact
The IHU 22 includes five distinct sensors 1220a-e for five distinct regions (e.g., top, left, right, front, and back) of the player's head. Specifically,
iii. IHU Performs Head Impact Exposure Algorithm
After the microcontroller 130c determines the impact magnitude value, the microcontroller 130c will simultaneously perform both algorithms shown in
Each of these severity values (e.g., 1st, 2nd, 3rd, 4th or 5th) corresponds to a range of impact magnitude values that are programmed into the IHU 22. The 1st range may include impact magnitude values between the impact matrix threshold and the 50th percentile of historical impact magnitude values for players of similar position and level, the 2nd range may include impact magnitude values between the 51st percentile and the 65th percentile of historical impact magnitude values for players of similar position and playing level, the 3rd range may include impact magnitude values between the 66th percentile and the 85th percentile of historical impact magnitude values for players of similar position and playing level, the 4th range may include impact magnitude values between the86th percentile and the 95th percentile of historical impact magnitude values for players of similar position and playing level, and the 5th range may include impact magnitude values above the 95th percentile of historical impact magnitude values for players of similar position and playing level.
Once the microcontroller 130c has added the impact magnitude value to the impact matrix in step 532, as shown in
iv. IHU Performs the Algorithms Shown in
While the IHU 22 is performing the HIE algorithm 500, the IHU 22 is also performing the alert algorithm 502. First, the microcontroller 130c will calculate an impact value by weighting the impact magnitude value based on the location of the impact and other factors in step 538. In one exemplary embodiment, the impact value is calculated by first determining the linear acceleration, rotational acceleration, head injury criterion (HIC), and the Gadd severity index (GSI) for the given impact. The algorithms used to calculate these values are described in Crisco J J, et. al. An Algorithm for Estimating Acceleration Magnitude and Impact Location Using Multiple Nonorthogonal Single-Axis Accelerometers. J BioMech Eng. 2004; 126(1), Duma S M, et. al. Analysis of Real-time Head Accelerations in Collegiate Football Players. Clin J Sport Med. 2005; 15(1):3-8, Brolinson, P. G., et al. Analysis of Linear Head Accelerations from Collegiate Football Impacts. Current Sports Medicine Reports, vol. 5, no. 1, 2006, pp. 23-28, and Greenwald R M, et., al. Head impact severity measures for evaluating mild traumatic brain injury risk exposure. Neurosurgery. 2008; 62(4):789-798, the disclosure of which is hereby incorporated by reference in its entirety for all purposes. Once the linear acceleration, rotational acceleration, head injury criterion (HIC), and the Gadd severity index (GSI) are calculated for a given impact, these scores are weighted according to the algorithm set forth in Greenwald R M, et., al. Head impact severity measures for evaluating mild traumatic brain injury risk exposure. Neurosurgery. 2008; 62(4):789-798, the disclosure of which is hereby incorporated by reference in its entirety for all purposes. This resulting weighted value is the impact value. In this exemplary embodiment, the impact value is also equal to what has been called a HITsp value. While not diagnostic of injury, HITsp values are more sensitive and specific to diagnose concussions than any of the component measures alone.
In other exemplary embodiment, the impact value may be equal to only the linear acceleration for the given impact. In a further exemplary embodiment, the impact value may be equal to only the HIC score for the given impact. In another exemplary embodiment, the impact value may be equal to only the rotational acceleration for a given impact. In another exemplary embodiment, the impact value may be equal to the linear acceleration weighted by a combination of impact location and impact duration. In another exemplary embodiment the impact value may be equal to the weighted combination of linear acceleration, rotational acceleration, HIC, GSI, impact location, impact duration, impact direction. In another exemplary embodiment, the impact value may be equal to a value that is determined by a learning algorithm that is taught using historical data and diagnosed injuries. In even a further exemplary embodiment, the impact value may be equal to any combination of the above.
Once the impact value is calculated in step 538 by the microcontroller 130c, the impact value is compared against a 2nd threshold or high magnitude impact threshold in step 540. This high magnitude impact threshold may be set to the 95th percentile for impacts recorded by players of similar playing level and similar position. If the impact value is less than the high magnitude impact threshold, than the microcontroller 130c will not perform any additional steps. However, if the impact value is greater than the high magnitude impact threshold, than the impact value will be added to the cumulative impact value in step 542 and compared against a 3rd threshold or single impact alert threshold in step 544. This single impact alert threshold may be set to the 99th percentile for impacts recorded by players of similar playing level and position.
If the impact value is greater than the single impact alert threshold, than the control module 130 of the IHU 22 transmits alert data that is associated with the single impact alert to the receiving device 24 (e.g., an alert unit 26, a controller 30, a network element 34, or a combination of these items and other items) in step 546. The alert data may include, but is not limited to,: (i) the impact value, (ii) impact location, (iii) impact time, (iv) impact direction, (v) player's unique identifier, (vi) alert type, (vii) player's heart rate, (viii) player's temperature, (ix) impact magnitude and/or (ix) other relevant information (e.g., activity information). As will be discussed in greater detail below, the alert data may be displayed on the alert unit 26 in a graphical (e.g., on a representative image of a player) manner or in a non-graphical (e.g., numerical value) manner. If the impact value is less than the single impact alert threshold, than the microcontroller 130c will not perform any additional steps along this path of the algorithm 502.
While the microcontroller 130c is determining whether the impact value is greater than the single impact alert threshold in step 544, the microcontroller 130c further calculates a weighted cumulative impact value that includes this new impact value, in step 548. Specifically, the weighted cumulative impact value is calculated based on a weighted average of every relevant impact value that is over a 2nd threshold or high magnitude impact threshold. To determine this weighted average, every impact value that is over a 2nd threshold is weighted by a decaying factor. For example, an impact that was recorded 4 days ago may be multiplied by 0.4 decaying factor, thereby reducing the magnitude level of this impact. After the weighted impact values are determined, these values are summed together to generate the weighted cumulative impact value. It should be understood that the microcontroller 130c will exclude irrelevant impact values that are old enough to cause their weighted impact value to be zero due to the decaying factor. For example, if the decaying factor for an impact that is over 7 days old is 0; then regardless of the impact value, this impact is irrelevant to this calculation and will not be included within this calculation. One skilled in the art recognizes that weighting variables (e.g., time window, decay function, input threshold) can be adjusted to values other than then values disclosed herein. For example, the decaying factor for an impact that is over 14 days old is 0, while the decaying factor for an impact that is over 7 days old is in 0.5. In a further example, the decaying factor for an impact over a set time period may be non-linear.
Once the weighted cumulative impact value has been calculated in step 548, this value is compared against a 4th threshold or a cumulative impact alert threshold in step 550. This cumulative impact alert threshold may be set to the 95th percentile for weighted cumulative impact values recorded by players of similar playing level and position. If the weighted cumulative impact value is less than the cumulative impact alert threshold, than the microcontroller 130c will not perform any additional steps. However, if the weighted cumulative impact value is greater than the cumulative impact value threshold, than the control module 130 of the IHU 22 transmits alert data that is associated with a cumulative impact alert to the receiving device 24 (e.g., an alert unit 26, a controller 30, a network element 34, or a combination of these items and other items) in step 552. Upon the completion of this decision, the IHU 22 has finished performing the alert algorithm 502.
The system 10 is also configured to monitor impacts and process data from players who experience multiple impacts on the same play. A person of skill in the art of designing sophisticated monitoring equipment for contact sports recognizes that many football players, including running backs, offensive lineman and defensive lineman, experience multiple impacts on a single play. For example, when a running back experiences multiple impacts while carrying the football (e.g., a rushing play), the algorithm in
Unlike conventional systems (e.g., the systems disclosed within U.S. Pat. Nos. 10,105,076, 9,622,661, 8,797,165, and 8,548,768), the multi-functional system 10 disclosed herein performs multiple algorithms within the IHU 22 contained within the player's helmet 20 in order to monitor that player and provide the authorized user with information about the monitored player's recently recorded physiological parameter data, including how that data may differ from the monitored player's previously recorded physiological parameter data or how the physiological parameter data that has been recorded in connection with other players. This functionality is beneficial because it provides information to the authorized user about what the player is actually experiencing on-field, in comparison to his past playing experiences and history and other similar players, which in turn allows the authorized user-coaching staff or athletic trainers to identify and correct the player's playing techniques or provide aid to the player. Additionally, the multi-function system 10 provides information that the authorized user can share with the player to provide support for what the authorized user is telling the player. Overall, this multi-function system 10 provides many significant advantages over other conventional systems, which enable the system 10 to improve how the authorized user trains, practices, and handles players, practice sessions and games.
Instead of or in addition to monitoring and recording physiological parameter data related to pressure on a player's head as a result of an impact, the IHU 22 may monitor and record data related to other physiological parameters. For example, another physiological parameter that may be analyzed by the system 10 is the player's temperature. The IHU 22 may measure the player's temperature by including at least one temperature sensor within the sensor assembly 120 of the IHU 22. Specifically, the temperature sensor may be a thermistor, which comprises resistive circuit components having a high negative temperature coefficient of resistance so that the resistance decreases as the temperature increases. Alternatively, the temperature sensor may be a thermal ribbon sensor or a band-gap type integrated circuit sensor. Using one of these temperature sensors, the player's temperature may be recorded when an impact is detected and that impact resulted in either: (i) the impact magnitude being included in the impact matrix, (ii) a transmission of alert data based on a single alert, or (iii) a transmission of alert data based on a cumulative alert. In this example, the number of columns in the impact matrix may be doubled to include a temperature column that is located adjacent to each of the impact locations (e.g., front, back, left, right, and top). This would allow for the player's temperature to be recorded within these cells. If multiple impacts were experienced that have the same severity level and location, the temperature measurements for those specific impacts may be averaged together to create an average temperature for that specific severity level and location.
In an alternative example, a player's temperature may be actively monitored to determine if the player's temperature exceeds or drops below a threshold. Specifically, an alert may be transmitted to the receiving device 24 to inform sideline personal that a player is overheating and the player should be pulled out of the activity to allow the player to cool down. In a further example, the player's temperature may be periodically recorded and transmitted to the receiving device 24. These recordings can then be later analyzed by sideline personal to suggest different equipment configurations or different workout regiments in order to optimize the player's thermal management.
Instead of or in addition to monitoring and recording physiological parameter data related to: (i) pressure on a player's head as a result of an impact or (ii) the player's temperature, the IHU 22 may also monitor and record data related to other physiological parameters. For example, another physiological parameter may be the player's heart rate and/or blood pressure. The IHU 22 may measure the player's heart rate and blood pressure by including at least one microelectromechanical system (MEMS) type sensors that use auscultatory (e.g., listening to the internal sounds made by the body) and/or oscillometric (e.g., oscillations of the arterial pulse) within the sensor assembly 120 of the IHU 22. Using one of these heart rate and/or blood pressure sensors, the player's heart rate and/or blood pressure may be recorded when an impact is detected and that impact resulted in either: (i) the impact magnitude being included in the impact matrix, (ii) a transmission of alert data based on a single alert, or (iii) a transmission of alert data based on a cumulative alert. In this example, the number of columns in the impact matrix may be increased by 3 to include a heart rate column and a blood pressure column that is located adjacent to each of the impact locations (e.g., front, back, left, right, and top). This would allow for the player's heart rate and blood pressure to be recorded within these cells. Once multiple impacts were experienced that have the same severity level and location, the heart rate and blood pressure measurements for those specific impacts may be averaged together to create an average heart rate or blood pressure for that specific severity level and location.
In an alternative example, a player's heart rate and/or blood pressure may be actively monitored to determine if the player's heart rate and/or blood pressure exceeds or drops below a threshold. Specifically, an alert may be transmitted to the receiving device 24 to inform a trainer or a coach than a player's heart rate is too high and the player should be pulled out of the activity to allow the player to lower their heart rate. In an alternative example, the player's heart rate and/or blood pressure may be periodically recorded and transmitted to the receiving device 24. These recordings can then be later analyzed by a trainer or a coach to suggest different equipment configurations or different workout regiments in order to optimize the player's management of their heart rate and/or blood pressure.
Instead of or in addition to monitoring and recording physiological parameter data related to: (i) pressure on a player's head as a result of an impact, (ii) the player's temperature, (iii) the player's heart rate or (iv) the player's blood pressure, the IHU 22 may also monitor and record data related to the player's balance. The IHU 22 may measure small movements by the player by using at least one low acceleration (low G) accelerometer within the sensor assembly 120 of the IHU 22. Using this low G accelerometer, small movements by the player may be measured to detect balance issues. Specifically, the IHU 22 may include an algorithm that calculates and observes a player's balance between plays or during extended stoppages in play, such as when a penalty is being assessed or a timeout. When a player assumes the ready position prior to the commencement of the play, for example a three-point stance, the low G accelerometers and the algorithm would detect player movements indicative of balance issues. If the IHU 22 detects that the player has a balance issue, then an alert can be sent from the IHU 22 to the receiving device 24 to alert the authorized user of this balance issue.
Instead of or in addition to monitoring and recording physiological parameter data related to: (i) pressure on a player's head as a result of an impact, (ii) the player's temperature, (iii) the player's heart rate or (iv) the player's blood pressure, (v) player's balance, the IHU 22 may also monitor and record data related to the player's O2 saturation (Sp02%) or molecular quantiles of certain substances contained within the player's blood (e.g. lactic acid, blood sugar). The IHU 22 may measure O2 saturation or quantiles of certain substances contained within the player's blood using optical sensing such as PPG (photoplethysmogram). Specifically, the IHU 22 may include an algorithm that calculates and observes a player's O2 saturation or quantiles of certain substances contained within the player's blood. If anyone of these values is measured to be outside of the 95% for similar players, then an alert can be sent from the IHU 22 to the receiving device 24 to alert the authorized user of the issue. Alternatively, these values could be recorded upon the detection of an alerting event and may be tracked to gain additional insight into these levels during an alerting event.
Instead of or in addition to monitoring and recording physiological parameter data related to: (i) pressure on a player's head as a result of an impact, (ii) the player's temperature, (iii) the player's heart rate, (iv) the player's blood pressure or (v) player's balance, (vi) player's O2 saturation (Sp02%) or molecular quantiles of certain substances contained within the player's blood (e.g. lactic acid, blood sugar), the IHU 22 may also monitor and record data related to other physiological parameters. For example, the activity sensing module 130g may include multiple hardware components (e.g., (i) accelerometer, (ii) gyroscope, (iii) GPS sensor/indoor location sensors, (iv) a magnetometer, and/or (v) a heart rate monitor, such as the one discussed above) to determine the activity levels and events that occurred during the game or activity. Specifically, information for these sensors can be processed to determine a player's: (i) acceleration of the player, (ii) deceleration of the player, (iii) velocity of the player, (iv) direction the player is running (e.g., forward, laterally, backward, etc.), (v) when a player left the ground, (vi) sharp changes in direction while running, and/or (vi) other general strength and condition metrics.
Using the above-identified information that was derived from data collected by the activity sensing module 130g, the system 10 could determine player and/or team metrics, which include: (i) when a practice started, (ii) what drills likely occurred during the practice, (iii) how hard the player is working during practice (e.g., duration of time the player is standing, duration of time the player is jogging, and duration of time the player is running), (iv) how accurate where the player's routes he ran, (v) number of times the QB to a 3 step drop, 5 step drop or scrambled from the pocket and (vi) other desirable metrics. Examples of how the system 10 could use the information from the sensors contained within the activity sensing module 130g to determine when practice started and ended could be done by setting a threshold number of player's (e.g.,25% of the team) that have registered a minimal amount of movement. Additionally, the system 10 could determine the drills that likely occurred during the practice by recording at least a plurality of the following parameters: heart rate, movement of the players, impact data, and/or alert data. Then a learning algorithm may be utilized to compare these recently recorded values with values that have been previously associated with certain drills. Based on this comparison, the system 10 can make a prediction of what the drills that the player/team is likely doing at a given time.
Further, determining how hard a player is working during a workout could be based on his heart rate level, acceleration/deceleration levels, velocity levels, distance traveled, or a combination of one or more of these levels. Moreover, determining the accuracy of the routes a player ran could be done by analyzing the data that is gathered by the accelerometer, gyroscope, GPS sensor/indoor location sensors, a magnetometer and comparing that against a set of identified plays. For example, the authorized user may input a drill into the practice planner that sets out 10 plays that will be run during that drill period. The data gathered by the above sensors can then determine the location of the player during the drill period and it can compare the player's location against a predicted location. The difference between the predicted and actual locations will provide accuracy measurement. From these examples it should be clear to one of skill in the art that other information may be derived from the data that is generated by the activity sensing module 130g to provide additional information to the authorized user.
The player/team metrics can be used by the system 10 to help provide a more complete picture of what the player is experiencing on the field, which can be used by the coaching staff to improve the training of the player/team. For example, system 10 may inform the authorized user that a certain set of drills carry a high impact load and only a few players have a high activity level during this drill, which may suggest to the coach that a different drill should be utilized. Additionally, after the system 10 informs the authorized user that a wide receiver has a poor route quality, the coaching staff can use this information to suggest drills that will improve the player's routes. Overall, providing additional actionable data in a usable format to the authorized user will improve the coach's ability to improve his player's playing level and help ensure that his players are in the best position to be successful.
It should be understood that other contextual data or data that is not related to the player's physiological parameter data may be recorded by the IHU 22 or an external device. For example, a temperature sensing device could be utilized to determine the relative heat index during the monitoring session relating to practice or game play. This data may suggest that an authorized user move training to an alternate location or time that has less risk exposure to harmful conditions, such as heat stroke. Other contextual data that may be monitored and recorded by the system 10 includes general fatigue, travel, sleep, blood/spit work, prescribed drugs (e.g., insulin, blood thinners). This data may be utilized to make suggested changes to coaching plans, such as practice plans.
In the embodiment shown in
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In an embodiment shown in
a. Alert Unit
As described above, the alert unit 26 is hand-held, portable and is typically carried by a person that is: (i) positioned proximate (e.g., within 50 yards) to the field or location that the physical activity is taking place and (ii) is not engaged in the physical activity (e.g., sideline personnel, which may be a trainer). Specifically, the alert unit 26 in this embodiment may be a PDA, a cellular phone (e.g., iPhone or Samsung Galaxy), a watch (e.g., iWatch or Android-based watch), a tablet (e.g., iPad or Android-based tablet), or a custom designed device specifically designed to be a portable alert unit 26. For example, the alert unit 26 may be an iPhone or a custom designed device that is carried within the trainer's pocket. In these exemplary embodiments, the alert unit 26 includes various components, such as a display, a processor, a memory, peripheral devices, a radio/transmitter/receiver, sensors, and other components.
In
Once sideline personnel (e.g., trainer), who are typically not engaged in the physical activity, have received an alert on the alerting unit 26, they can employ a method for evaluating and treating the player in question. Specifically, evaluating and treating the player in question is described within U.S. Pat. No. 8,548,768, which is fully incorporated herein by reference. For example, the alert unit 26 be programmed with interactive software that assures best practices are followed in the treatment and documentation of injuries, such as mild traumatic brain injuries (MTBI). The interactive software may include a bundle of team management programs that enable the signaling device to store all team data, including medical histories and testing baselines. The interactive software also provides the signaling device with an active response protocol for guiding sideline personnel through appropriate examination procedures and recording the results. For example, when the alert unit 26 receives an alert and the relevant player is brought to the sideline for evaluation, the alert unit 26 can display the individual's head-injury history, the results of previous evaluations and other pertinent medical data. With the assistance of the interactive software, the signaling device prompts the medical staff member to conduct the appropriate sideline examination, records the responses, compares the results to established baselines and prompts either further testing or a play/no-play decision. The interactive software may also include a session manager program that allows the authorized user to document incidents as they occur during a practice or a game. The appropriate information about the team, players and conditions is entered at the beginning of each session. Then, as injuries occur, the interactive software provides a template for recording injury data on a per-player basis. The data and results stored on the device can be uploaded to the team database 32 wherein authorized users can access the same for team management and player evaluation functions.
As shown in
In another embodiment shown in
National database 38 stores all the data or a subset of the data that is stored in each of the team databases 32 around the nation or world. Specifically, the team databases 32 uploads a copy of the data to the national database 38 via communications link 62 after a predefined amount of time has passed since the team database 32 was last uploaded to the national database 38. Additionally, after the new data from the team database 32 is uploaded to the national database 38, the team database 32 may download new thresholds from the national database 38 via communications link 62. The data that may be contained within the national database 38 may include, but is not limited to: (i) alert data for each player across the nation/world, which includes single and cumulative alerts for each player across the nation/world, (ii) impact matrix for each player across the nation/world, (iii) other data related to the recorded physiological parameters for each player across the nation/world, (iv) equipment assignments and profiles of each player across the nation/world (e.g., relevant sizes, type of shoes, type of helmet, type of helmet padding, type of chin strap, type of faceguard, and etc.), (v) medical data for each player across the nation/world (e.g., medical histories, injuries, height, weight, emergency information, and etc.), (vi) statistics for each player across the nation/world, (vii) workout regiments for each player across the nation/world, and (viii) other player data across the nation/world (e.g., head and helmet scans, contact information). It should be understood that in certain embodiments a team may elect not to contribute certain information from the team database 32 to the national database 38.
The display 28a of the remote terminal 28 permits an authorized user to view via the GUI, remotely or locally, the data contained within the team/national database 32, 38 via communications link 60. This allows the authorized user to remotely keep abreast of changes in a player's status or check to see if the team has equipment components to replace the equipment that was lost or damaged during the physical activity. For example, the authorized user can view comparisons that may include: (i) recently recorded data contained within the team database 32 against historical data contained within the team database 32, (ii) recently recorded data contained within the team database 32 against historical data contained within the national database 38, and (iii) recently recorded data contained within the team database 32 against recently recorded data contained within the national database 38. Each comparison can provide information that can be used to make suggested changes in how the player or team engages in physical activity. For example, if it is determined that the current quarterback for Team A is currently experiencing more single alerts then other historical quarterbacks for Team A, the authorized user may need to look at the playing style of the current quarterback of Team A or may need to review the techniques of his lineman.
The remote terminal 28 may be an internet enabled device, such as aPDA, a cellular phone (e.g., iPhone or Samsung Galaxy), a tablet (e.g., iPad or Android-based tablet), a laptop, a desktop computer, or a custom device specifically designed to be a remote terminal. Unlike the alert unit 26, the remote terminal 28 is not specifically configured to be carried by a person that is: (i) positioned proximate (e.g., within 50 yards) to the field or location that the physical activity is taking place and (ii) is not engaged in the physical activity. While typically being a portable device that can be transported from one location to another location, the remote terminal 28 is typically configured to be positioned in a trainer's office, the press box, locker room, home of the player or other similar locations. It should be understood that in some embodiments, the remote terminal 28 and the alert unit 26 may be combined into a single device, as shown in
In certain embodiments, such as those shown in
In any of the above embodiments, the team/national database 32, 38 may be remotely accessed over the internet by an authorized user using the remote terminal 28. To ensure that the user is an authorized user, the system 10 requires that the user provide some type of information to identify the user. For example, the remote terminal 28 may request a connection with the team/national database 32, 38 from an internet enabled device. Once the team/national database 32, 38 receives this request from the remote terminal 28, the team/national database 32, 38 may ask the authorized user to enter their user name and password using the remote terminal 28. The user will then enter this information using the remote terminal 28 and the information will be sent to the team/national database 32, 38 over the internet. The team/national database 32, 38 then verifies this information by confirming the user name and password that was provided matches the user name and password that is stored within the team/national database 32, 38. After verification is complete, remote terminal 28 is given access, according to the authorized user's access level (e.g., admin, full access to team information, only access to a single player, access to another subset of the data, etc.), to the information contained within the team/national database 32, 38. It should be understood that other types of identifying information (e.g., RSA tokens, information derived from the BIOS of a computer, etc.) may be provided by the user in alternative embodiments. This allows the authorized user to remotely keep abreast of changes in the player's status or check to see if the team has equipment components to replace an equipment component that was lost or damaged during the physical activity.
It should be understood an authorized user who can view data contained within the team database 32 is not typically granted full access to view data contained within the national database 38. Typically, the only users that have full access to this national database 38 are the database administrators. This helps ensure that the data from the player's around the nation/world cannot be accessed by other authorized users. In certain embodiments, where the national database 38 and/or team database 32 is cloud-based and is accessible via the internet, a partial authorized user (e.g., a parent) may have restricted access to an extent of the national database 38 and/or team database 32 may be accessed by the remote terminal 28 via communications link 60. For example, this restricted access allows the partial authorized user and/or remote terminal 28 to view a specific player's recently recorded physiological parameter data.
It should be understood that the disclosure contained herein discusses that the remote terminal 28 performs the algorithms (e.g., 504, 506, 508, 510, 512, 514, 516, and 518) discussed below and thus is performing the comparison of the datasets (e.g., data contained within the team database 32 with data contained within the national database 38). In an alternative embodiment, another device (e.g., remote cloud server) that is connected to the team database 32 and the national database 38 may perform these algorithms described below, while the remote terminal 28 merely displays the outcomes of these algorithms in the GUI.
This alternative embodiment allows the remote terminal 28 to operate more efficiently because it does not have to perform all of these algorithms, which also improves the efficiency of the system 10 as it reduces the amount of data that system 10 must simultaneously monitor and process. For example, the algorithms that are described above can be performed during times when the system 10 is not being used (e.g., late at night) during practice or game play, which in turn allows the system 10 to focus on requests made by authorized users that are actively logged into the system 10. Additionally, this alternative embodiment centralizes the processing of the algorithms, which allows for specialized devices to rapidly perform the algorithms Further, centralization of the algorithms reduce the cost of running the system 10 because the system can tailor its power usage (e.g., when it performs the above described algorithms) to select times when power is less expensive and by providing the reports the authorized user does not have to log into the system to pull this information therefrom. Moreover, this centralization allows for the remote terminal 28 to have less processing power and storage then would otherwise be required. This allows for the system 10 to provide access to a greater number of authorized users in a greater number of location; thus, improving the usability and efficiently of the system 10.
d. Training Opportunities
As discussed above, the system 10 provides post-physical activity analysis of the recorded data to make suggested changes in how the person engages in the physical activity using a unique set of rules or algorithms Specifically, these suggestions are based on specific training opportunities.
i. Overview
Examples of training opportunities that the system 10 can determine are shown in
Examples of other training opportunities that the system 10 may determine are shown in
Similarly,
Similarly,
Similarly,
As discussed above in connection with the IHU 22, certain threshold values/ranges that are utilized by the algorithms are not standardized across all players. In other words, different or custom threshold values/ranges may be utilized by the algorithms for each player. Also, like above, the non-standardized or custom threshold values/ranges are based upon information that is entered or obtained from the player whose data is going to be analyzed by the algorithm. In particular, tailoring the algorithms to the specific player by using non-standardized or custom threshold values/ranges creates a specialized multi-function system 10 that determines training opportunities for the specific player. This specialized multi-function system 10 provides more accurate information, including monitoring and training opportunities to the authorized user. This in turn improves the efficiency of the system 10 and the authorized user's ability to make suggested changes in how the person engages in the physical activity.
The non-standardized or custom threshold values/ranges in this embodiment are: (i) 5th threshold or number of alertable impacts threshold, (ii) 6th threshold or number of high magnitude impacts threshold, (iii) 10th threshold or number of impacts threshold, (iv) 13th threshold or over baseline average number of impacts threshold, (v) 14th threshold or impact load threshold, (vi) 17th threshold or over baseline average load threshold, and (vii) 19th threshold or location threshold. Similar to the above described process, system 10 determines the values of these non-standardized or custom threshold values/ranges based on the player's position and level. To select the proper thresholds/values, the remote terminal 28 pulls the thresholds/values from the team/national database 32, 38 that were associated with the player during the setup of the IHU 22. Also, as described above, these non-standardized threshold values/ranges may be adjusted or a different set of thresholds/values may be selected in a manner that is similar to the above described processes. It should be understood that additional threshold values/ranges may be added to the above disclosed list of threshold values/ranges or threshold values/ranges may be subtracted.
Training opportunity algorithm 504 is shown in
Specifically, the remote terminal 28 performs the steps described in algorithm 504. First, in step 560, the remote terminal 28 sums up the total number of single and cumulative impacts that the specific player experienced over a 2nd predefined time period or an alertable time period. The 2nd predefined time period or an alertable time period may be set to any amount of time, preferably set between 2 days and 90 days, and most preferably set to 7 days. Once the remote terminal 28 has determined the total number of alertable impacts experienced by the specific player over the alertable time period in step 560, this total number is compared with a 5th threshold or a number of alertable impacts threshold in step 562. The number of alertable impacts threshold may be set to the 95th percentile of the number of alertable events that have historically occurred over the alertable time period for similarly situated players in terms of playing level and/or position. If the total number of alertable impacts over the alertable time period is less than the number of alertable impacts threshold, then remote terminal 28 performs no additional steps. However, if the total number of alertable impacts over the alertable time period is greater than the number of alertable impacts threshold, then the 15th training opportunity is triggered in step 564. This 15th training opportunity informs an authorized user that the specific player is experiencing more alerts than other similarly situated players in terms of playing level and/or position. Accordingly, the authorized user should review the game tape with this player at the timestamps that each alertable incident occurred to determine how the specific player may change their playing style to reduce the number of alertable impacts in the future.
Training opportunity algorithm 504 may be used to generate another training opportunity by altering the data the algorithm 504 compares. Instead of comparing a specific player's data to national data, the training opportunity algorithm 504 may compare a specific player's data to team data. The 9th training opportunity or the high number of alertable impacts for specific player v. team training opportunity may be generally determined by comparing a value that is derived from a specific player's alert data with a value that is derived from the team's alert data. In other words, the 9th training opportunity may be generally determined by comparing a specific player value derived from data contained within the team database 32 with a team value derived from data contained within the team database 32 using algorithm 504. Specifically, values and data used in this 9th training opportunity include:
This 9th training opportunity uses the same data flow that is described above in connection with the 1st training opportunity, except for the fact that it utilizes different data sets. This 9th training opportunity informs an authorized user that the specific player is experiencing more alertable impacts than other team players that play the same or similar positions. Accordingly, the authorized user should review the game tape at the alertable time periods with this specific player to determine how to correct their playing style to match other players that play the same or similar positions.
Training opportunity algorithm 504 may further be used to generate another training opportunity by altering the data the algorithm 504 compares. Instead of comparing a specific player's data to national data, the training opportunity algorithm 504 compares a team's data to national team data. The 17th training opportunity or the high number of alertable impacts for team v. national team data training opportunity may be generally determined by comparing a value that is derived from a specific team's alert data with a value that is derived from national alert data. In other words, the 17th training opportunity may be generally determined by comparing a specific team value derived from data contained within the team database 32 with a value derived from data contained within the national database 38 using algorithm 504. Specifically, values and data used in this 17th training opportunity include:
This 17th training opportunity uses the same data flow that is described above in connection with the 1st training opportunity, except for the fact that it utilizes different data sets. This 17th training opportunity informs an authorized user that the specific team is experiencing more alerts than other teams that play at a similar level. Accordingly, a wholesome review of the specific teams playing style should be reviewed.
Training opportunity algorithm 506 is shown in
Specifically, remote terminal 28 performs the steps described in algorithm 506. First, in step 566, the remote terminal 28 calculates an impact value for each impact that is contained within the specific player's physiological parameter data over the 3rd predefined amount of time or the high magnitude time period. These impact values may be calculated using any of the methods discussed above in connection with
Once the remote terminal 28 has determined the total number of high magnitude impacts experienced by the specific player over the high magnitude time period in step 570, this total number is compared with a 6th threshold or a number of high magnitude impacts threshold in step 572. The number of high magnitude impacts threshold may be set to the 95th percentile of the number of high magnitude impacts that have historically occurred over the high magnitude time period for similarly situated players in terms of playing level and/or position. If the specific player's total number of high magnitude impacts over the high magnitude time period is less than the number of high magnitude impacts threshold, then the remote terminal 28 performs no additional steps. However, if the specific player's total number of high magnitude impacts over the high magnitude time period is greater than the number of high magnitude impacts threshold, then the 2nd training opportunity is triggered in step 574. This 2nd training opportunity informs an authorized user that the specific player is experiencing more high magnitude impacts than similarly situated players in terms of playing level and/or position. Accordingly, the authorized user should review the game tape with this specific player to determine how the player may change their playing style to avoid these high magnitude impacts in the future.
Training opportunity algorithm 506 may also be used to generate another training opportunity by altering the data the algorithm 506 compares. Instead of comparing a specific player's data to national data, the training opportunity algorithm 506 may compare a specific player's data to team data. The 10th training opportunity or the high number of high magnitude impacts for specific player v. team training opportunity may be generally determined by comparing a value that is derived from a specific player's recorded impact values with a value that is derived from the team's recorded impact values. In other words, the 10th training opportunity may be generally determined by comparing a specific player value derived from data contained within the team database 32 with a team value derived from data contained within the team database 32 using algorithm 506. Specifically, values and data used in this 10th training opportunity include:
This 10th training opportunity uses the same data flow that is described above in connection with the 2nd training opportunity, except for the fact that it utilizes different data sets. It should be understood that instead of using all players of similar player level and position to determine the 2nd threshold, this 2nd threshold may be based on teammates that play similar positions. This 10th training opportunity informs an authorized user that the specific player is experiencing more high magnitude impacts than other team players that play the same or similar positions. Accordingly, the authorized user should review the game tape with this specific player to determine how to correct their playing style to match other players that play similar positions.
Training opportunity algorithm 506 may be used to generate another training opportunity by altering the data the algorithm 506 compares. Instead of comparing a specific player's data to national data, the training opportunity algorithm 506 may compare a specific team's data to national data. The 18th training opportunity or the high number of high magnitude impacts for specific team v. nation training opportunity may be generally determined by comparing a value that is derived from a team's recorded impact values with a value that is derived from the national recorded impact values. In other words, the 18th training opportunity may be generally determined by comparing a specific team value derived from data contained within the team database 32 with a value derived from data contained within the national database 38 using algorithm 506. Specifically, values and data used in this 18th training opportunity are described below:
This 18th training opportunity uses the same data flow that is described above in connection with the 2nd training opportunity except for the fact that it utilizes different data sets. This 18th training opportunity informs an authorized user that the specific team is experiencing more high magnitude impacts than other teams that play at a similar playing level. Accordingly, a wholesome review of the specific teams playing style should be review.
Training opportunity algorithm 508 is shown in
Threshold: set to 1% over the baseline average number of high magnitude impacts, which was calculated in step 584, preferably set between 5% and 50% over the baseline average number of high magnitude impacts, which was calculated in step 584, and most preferably between 10% and 40% over the baseline average number of high magnitude impacts, which was calculated in step 584.
Specifically, remote terminal 28 performs the steps described in algorithm 508. Also,
Next, in step 582, the remote terminal 28 determines if the specific player's physiological parameter data contains enough high magnitude impacts to perform the calculations involved with this training opportunity. This helps ensure that this training opportunity is not unnecessarily suggested when there is not enough data for this training opportunity to be accurately presented to the authorized user. The 7th threshold or historical high magnitude impact threshold may be set to require at least 2 datasets, preferably at least 5 datasets, and most preferably at least 10 datasets. If the specific player has not played long enough to record data over the required number of historical high magnitude impacts threshold, then the remote terminal 28 performs no additional steps. However, if the specific player has recorded data over the required number of historical high magnitude impacts, then, in step 584, the remote terminal 28 determines the baseline average number of high magnitude impacts for the specific player.
Next, in step 586, the remote terminal 28 compares the baseline average number of high magnitude impacts to an 8th threshold or a baseline number of high magnitude impacts threshold. The 8th threshold may be set above 0.1 average number of high magnitude impacts, preferably above 0.05 average number of high magnitude impacts, and most preferably above 1 average number of high magnitude impacts. If the specific player's baseline average number of high magnitude impacts is not over the baseline number of high magnitude impacts threshold, then the remote terminal 28 performs no additional steps. However, if the specific player's baseline average number of high magnitude impacts is over the baseline number of high magnitude impacts threshold, then, in step 588, the remote terminal 28 determines the recent average number of high impacts. The remote terminal 28 then compares the recent average number of high impacts against the 9th threshold or the over baseline average number of high magnitude impacts threshold in step 590. The 9th threshold may be set to 1% over the baseline average number of high magnitude impacts, which was calculated in step 584, preferably set between 5% and 50% over the baseline average number of high magnitude impacts and most preferably between 10% and 40% over the baseline average number of high magnitude impacts. If the recent average number of high magnitude impacts is not over the 9th threshold, then the remote terminal 28 performs no additional steps. However, if the recent average number of high magnitude impacts is over the 9th threshold, then the training opportunity is triggered in step 592. This 3rd training opportunity informs an authorized user that the specific player is experiencing, on average, more high magnitude impacts than the specific player has previously experienced. Accordingly, the authorized user should review the specific player's form to see what has recently changed with the specific player. For example, did the specific player recently come back from an injury and is now favoring that side, which is causing the specific player to have additional high magnitude impacts.
Training opportunity algorithm 508 may be used to generate another training opportunity by altering the data the algorithm 508 compares. Instead of comparing a specific player's recent data to the specific player's historical data, the training opportunity algorithm 508 may compare a specific team's recent data to the specific team's historical data. The 15th training opportunity or the increased number of high magnitude impacts for specific team v. specific team's history training opportunity may be generally determined by comparing a value that is derived from a specific team's recent recorded impact values with a value that is derived from the specific team's historical impact values. Specifically, values and data used in this 15th training opportunity are described below:
Threshold: set to 1% over the baseline average number of high magnitude impacts, which was calculated in step 584, preferably set between 5% and 50% over the baseline average number of high magnitude impacts, which was calculated in step 584, and most preferably between 10% and 40% over the baseline average number of high magnitude impacts, which was calculated in step 584; and
This 15th training opportunity uses the same data flow that is described above in connection with the 3rd training opportunity except for the fact that it utilizes different data sets. This 15th training opportunity informs an authorized user that the specific team is experiencing an increase in high magnitude impacts in comparison to the specific team's history. Thus, a review of the recent drills that the team is performing or other changes in coaching style should be reviewed.
Training opportunity algorithm 510 is shown in
Specifically, the remote terminal 28 performs the steps described in algorithm 510. First, in step 594, the remote terminal 28 sums up the total number of impacts that the specific player experienced over a 5th predefined time period or an impact time period. The 5th predefined time period may be set to any amount of time, preferably set between 2 days and 90 days, and most preferably set to 7 days. Specifically, this is done by adding together every impact matrix contained within the 5th predefined time period to generate a summed impact matrix. An example of how matrixes can be added together is shown in
Once the remote terminal 28 has determined the total number of impacts experienced by the specific player over the impact time period in step 594, this total number is compared with a 10th threshold or a number of impacts threshold in step 596. The number of impacts threshold may be set to the 95th percentile of the number of impacts that have historically occurred over the impact time period for similarly situated players in terms of playing level and/or position. If the total number of impacts over the impact time period is less than the number of impacts threshold, then remote terminal 28 performs no additional steps. However, if the total number of impacts over the impacts time period is greater than the number of impacts threshold, then the 4th training opportunity is triggered in step 598. This 4th training opportunity informs an authorized user that the specific player is experiencing more impacts than other similarly situated players in terms of playing level and/or position.
As described above, training opportunity algorithm 510 may be used to generate another training opportunity by altering the data the algorithm 510 compares. Instead of comparing a specific player's data to national data, the training opportunity algorithm 510 may compare a specific player's data to team data. The 11th training opportunity or the high number of impacts for specific player v. team training opportunity may be generally determined by comparing a value that is derived from a specific player's physiological parameter data with a value that is derived from the team's physiological parameter data. In other words, the 11th training opportunity may be generally determined by comparing a specific player value derived from data contained within the team database 32 with a team value derived from data contained within the team database 32 using algorithm 510. Specifically, values and data used in this 10th training opportunity include:
This 11th training opportunity uses the same data flow that is described above in connection with the 4th training opportunity, except for the fact that it utilizes different data sets. This 11th training opportunity informs an authorized user that the specific player is experiencing more impacts than other team players that play similar positions. This training opportunity may inform the authorized user that the individual specific player's playing style needs to be reviewed because they are experiencing impacts that are different than their teammates.
Training opportunity algorithm 510 may be used to generate another training opportunity by altering the data the algorithm 510 compares. Instead of comparing a specific player's data to national data, the training opportunity algorithm 510 compares a team's data to national team data. The 19th training opportunity or the high number of impacts for team v. national training opportunity may be generally determined by comparing a value that is derived from a team's physiological parameter data with a value that is derived from national physiological parameter data. In other words, the 19th training opportunity may be generally determined by comparing a specific team value derived from data contained within the team database 32 with a value derived from data contained within the national database 38 using algorithm 504. Specifically, values and data used in this 19th training opportunity include:
This 19th training opportunity uses the same data flow that is described above in connection with the 4th training opportunity, except for the fact that it utilizes different data sets. This 19th training opportunity informs an authorized user that the specific team is experiencing more impacts than other teams that play at a similar level. Accordingly, a wholesome review of the specific teams playing style should be reviewed.
Training opportunity algorithm 512 is shown in
Specifically, the remote terminal 28 performs the steps described in algorithm 512. Also,
Next, in step 602, the remote terminal 28 determines if the specific player's physiological parameter data contains enough impact data to perform the calculations involved with this training opportunity. This helps ensure that this training opportunity is not unnecessarily suggested when there is not enough data for this training opportunity to be accurately presented to the authorized user. The 11th threshold or historical impacts threshold may be set to require at least 2 datasets, preferably at least 5 datasets, and most preferably at least 10 datasets. If the specific player has not played long enough to record data over the required number of historical impacts threshold, then the remote terminal 28 performs no additional steps. However, if the specific player has recorded data over the required number of historical impacts threshold, then, in step 604, the remote terminal 28 determines the baseline average number of impacts for the specific player.
Next, in step 606, the remote terminal 28 compares the baseline average number of impacts to a 12th threshold or a baseline number of impacts threshold. The 8th threshold may be set above 0.1 average number of impacts, preferably above 8 average number of impacts, and most preferably above 15 average number of impacts. If the specific player's baseline average number of impacts is not over the baseline number of impacts threshold, then the remote terminal 28 performs no additional steps. However, if the specific player's baseline average number of impacts is over the baseline number of impacts threshold, then, in step 608, the remote terminal 28 determines the recent average number of impacts. Specifically, this is done by adding together every impact matrix contained within one day to generate a recent summed daily impact matrix. An example of how matrixes can be added together is shown in
In step 610, the remote terminal 28 then compares the recent average number of impacts against the 13th threshold or the percent change over the baseline average number of impacts threshold. The 13th threshold may be set to 95 percentile of historical change based on a specific player's position and playing level. If the recent average number of impacts is not over the 13th threshold, then the remote terminal 28 performs no additional steps. However, if the recent average number of impacts is over the 13th threshold, then the training opportunity is triggered in step 612. This 5th training opportunity informs an authorized user that the specific player is experiencing more impacts than the specific player has previously experienced based on an average of their impact history. Accordingly, the authorized user should review the specific player's form to see what has recently changed with the specific player. For example, did the specific player recently come back from an injury and is now favoring that side, which is causing the specific player to have additional impacts.
Training opportunity algorithm 510 may be used to generate another training opportunity by altering the data the algorithm 510 compares. Instead of comparing a specific player's recent data to the specific player's historical data, the training opportunity algorithm 510 may compare a team's recent data to the team's historical data. The 15th training opportunity or the increased number of impacts for team v. team's history training opportunity may be generally determined by comparing a value that is derived from a team's recent recorded impact values with a value that is derived from the team's historical impact values. Specifically, values and data used in this 15th training opportunity are described below:
This 15th training opportunity uses the same data flow that is described above in connection with the 5th training opportunity except for the fact that it utilizes different data sets. This 15th training opportunity informs an authorized user that the team is experiencing an increase in impacts in comparison to the team's history. Accordingly, a wholesome review of the specific teams playing style should be review.
Training opportunity algorithm 514 is shown in
Specifically, the remote terminal 28 performs the steps described in algorithm 514. First, in step 614, the remote terminal 28 sums up the total load the specific player experienced over a 7th predefined time period or an impact load time period. The 7th predefined time period may be set to any amount of time, preferably set between 2 days and 90 days, and most preferably set to 7 days. Specifically, this is done by adding together every impact matrix contained within the 7th predefined time period to generate a summed impact load matrix. An example of how matrixes can be added together is shown in
Once the remote terminal 28 has determined the total impact load experienced by the specific player over the impact load time period in step 614, this total impact load is compared with a 14th threshold or impact load threshold in step 616. The impact load threshold may be set to the 95th percentile of the load that has historically been experienced over the impact load time period for similarly situated players in terms of playing level and/or position. If the total impact load experienced by the specific player over the impact load time period is less than impact load threshold, then the remote terminal 28 performs no additional steps. However, if the total impact load experienced by the specific player over the impact load time period is greater than the impact load threshold, then the 6th training opportunity is triggered in step 618. This 6th training opportunity informs an authorized user that the specific player is experiencing a higher impact load than other similarly situated players in terms of playing level and/or position.
As described above, training opportunity algorithm 514 may be used to generate another training opportunity by altering the data the algorithm 514 compares. Instead of comparing a specific player's data to national data, the training opportunity algorithm 514 may compare a specific player's data to team data. The 12th training opportunity or the high impact load for specific player v. team training opportunity may be generally determined by comparing a value that is derived from a specific player's physiological parameter data with a value that is derived from the team's physiological parameter data. In other words, the 12th training opportunity may be generally determined by comparing a specific player value derived from data contained within the team database 32 with a team value derived from data contained within the team database 32 using algorithm 514. Specifically, values and data used in this 12th training opportunity include:
a. Training Opportunity #12 or High Impact Load for Specific Player v. Team
This 14th training opportunity uses the same data flow that is described above in connection with the 6th training opportunity, except for the fact that it utilizes different data sets. This 14th training opportunity informs an authorized user that the specific player is carrying a high impact load than other team players that play similar positions.
Training opportunity algorithm 514 may also be used to generate another training opportunity by altering the data the algorithm 514 compares. Instead of comparing a specific player's data to national data, the training opportunity algorithm 514 compares a team's data to national data. The 20th training opportunity or the high impact load for team v. national training opportunity may be generally determined by comparing a value that is derived from a team's physiological parameter data with a value that is derived from national physiological parameter data. In other words, the 20th training opportunity may be generally determined by comparing a team value derived from data contained within the team database 32 with a value derived from data contained within the national database 38 using algorithm 504. Specifically, values and data used in this 20th training opportunity include:
This 21st training opportunity uses the same data flow that is described above in connection with the 6th training opportunity, except for the fact that it utilizes different data sets. This 21st training opportunity informs an authorized user that the team is experiencing more impacts than other teams that play at a similar level. Accordingly, a wholesome review of the specific teams playing style should be reviewed.
Training opportunity algorithm 516 is shown in
Specifically, the remote terminal 28 performs the steps described in algorithm 516. Also,
Next, in step 622, the remote terminal 28 determines if the specific player's physiological parameter data contains enough data to perform the calculations involved with this training opportunity. This helps ensure that this training opportunity is not unnecessarily suggested when there is not enough data for this training opportunity to be accurately presented to the authorized user. The 15th threshold or historical impact load threshold may be set to require at least 2 datasets, preferably at least 5 datasets, and most preferably at least 10 datasets. If the specific player has not played long enough to record data over the required historical impact load threshold, then the remote terminal 28 performs no additional steps. However, if the specific player has recorded data over the required historical impact load threshold, then, in step 624, the remote terminal 28 determines the baseline average impact load for the specific player.
Next, in step 626, the remote terminal 28 compares the baseline average impact load to a 16th threshold or a baseline impact load threshold. The 16th threshold may be set above 0.1 average impact load, preferably above 8 average impact load, and most preferably above 15 average impact load. If the specific player's baseline average impact load is not over the baseline impact load threshold, then the remote terminal 28 performs no additional sets. However, if the specific player's baseline average impact load is over the baseline impact load threshold, then, in step 628, the remote terminal 28 determines the recent average impact load. Specifically, this is done by adding together every impact matrix contained within one day to generate a recent summed daily load matrix. An example of how matrixes can be added together is shown in
In step 630, the remote terminal 28 then compares the recent average impact load against the 17th threshold or the percent change over the baseline average impact load threshold in step 630. The 17th threshold may be set to the 95th percentile of historical change based on a specific player's position and playing level. If the recent average impact load is not over the 17th threshold, then the remote terminal 28 performs no additional steps. However, if the recent average impact load is over the 17th threshold, then the training opportunity is triggered in step 632. This 7th training opportunity informs an authorized user that the specific player has a higher impact load than the specific player has previously experienced based on an average of their impact history. Accordingly, the authorized user should review the specific player's form to see what has recently changed with the specific player. For example, did the specific player recently come back from an injury and is now favoring that side, which is causing the specific player to carry additional impact load.
Training opportunity algorithm 516 may be used to generate another training opportunity by altering the data the algorithm 516 compares. Instead of comparing a specific player's recent data to the specific player's historical data, the training opportunity algorithm 516 may compare a team's recent data to the team's historical data. The 16th training opportunity or the increased impact load for team v. team's history training opportunity may be generally determined by comparing a value that is derived from a team's recent recorded impact values with a value that is derived from the team's historical impact values. Specifically, values and data used in this 15th training opportunity are described below:
This 16th training opportunity uses the same data flow that is described above in connection with the 7th training opportunity except for the fact that it utilizes different data sets. This 16th training opportunity informs an authorized user that the team is experiencing an increased impact load in comparison to the team's history. Accordingly, a wholesome review of the teams playing style should be review.
Training opportunity algorithm 518 is shown in
Specifically, the remote terminal 28 performs the steps described in algorithm 518. First, in step 634, the remote terminal 28 determines if the specific player's physiological parameter data contains enough impact data to perform the calculations involved with this training opportunity. This helps ensure that this training opportunity is not unnecessarily suggested when there is not enough data for this training opportunity to be accurately presented to the authorized user. The 18th threshold or a minimum number of impacts for location analysis threshold may be set to require at least 1 impact, preferably at least 10 impacts, and most preferably at least 15 impacts. If the specific player has not played long enough to record data over the required minimum number of impacts for location analysis threshold, then the remote terminal 28 performs no additional steps. However, if the specific player has recorded data over the required minimum number of impacts for location analysis threshold, then, in step 636, the remote terminal 28 determines the summed location impact matrix over the 9th predefined amount of time or location time period. The 9th predefined amount of time may be set between 2 days and 90 days, and most preferably set to 7 days. Specifically, this is done by adding together every impact matrix contained within the 9th predefined time period to generate a summed location impact matrix. An example of how matrixes can be added together is shown in
Once the remote terminal 28 has determined the summed location impact matrix experienced by the specific player over the impact load time period in step 636, this number is compared with a 19th threshold or location threshold in step 638. The location threshold may be set to the 95th percentile of the chi-squared value based on impact matrixes that have historically occurred over the 9th predefined amount of time for similarly situated players in terms of playing level and/or position. Specifically, the table is shown in
As described above, training opportunity algorithm 518 may be used to generate another training opportunity by altering the data the algorithm 518 compares. Instead of comparing a specific player's data to national data, the training opportunity algorithm 518 may compare a specific player's data to team data. The 13th training opportunity or the uncommon impact location for specific player v. team training opportunity may be generally determined by comparing a value that is derived from a specific player's physiological parameter data with a value that is derived from the team's physiological parameter data. In other words, the 13th training opportunity may be generally determined by comparing a specific player value derived from data contained within the team database 32 with a team value derived from data contained within the team database 32 using algorithm 518. Specifically, values and data used in this 13th training opportunity include:
This 13th training opportunity uses the same data flow that is described above in connection with the 8th training opportunity, except for the fact that it utilizes different data sets. This 13th training opportunity informs an authorized user that the specific player has experienced impacts in uncommon locations when compared to other team players that play similar positions.
Training opportunity algorithm 518 may be used to generate another training opportunity by altering the data the algorithm 518 compares. Instead of comparing a specific player's data to national data, the training opportunity algorithm 518 compares a team's data to national data. The 21st training opportunity or the uncommon impact location for team v. national training opportunity may be generally determined by comparing a value that is derived from a team's physiological parameter data with a value that is derived from national physiological parameter data. In other words, the 21st training opportunity may be generally determined by comparing a team value derived from data contained within the team database 32 with a value derived from data contained within the national database 38 using algorithm 518. Specifically, values and data used in this 21st training opportunity include:
This 21st training opportunity uses the same data flow that is described above in connection with the 8th training opportunity, except for the fact that it utilizes different data sets. This 21st training opportunity informs an authorized user that the specific team is experiencing impacts in uncommon locations when compared to other teams that play at a similar level. Accordingly, a wholesome review of the specific teams playing style should be reviewed.
It should be understood that the system 10 may include only some of the above described algorithms or it may contain additional algorithms that were no discussed above. For example, the system 10 may include training opportunities that are based on one or more tools/approaches: (i) Bayes Theorem, (ii) standard t-tests or ANOVA, (iii) changes in kurtosis, (iv) Kruskal Wallis non-parametric distribution testing, (v) machine learning using various neural network topologies including RNN and LSTM, (vi) pattern detection using Support Vector Machines (SVM), (vii) principal component Analysis (PCA), (viii) Independent Component Analysis (ICA), (ix) clustering approaches including k-nearest neighbor or (x) other similar techniques.
As described above, each training opportunity is determined by the system 10 based on comparing datasets to one another using various algorithms Each of these algorithms contains at least one threshold value (e.g., 1st threshold-19th threshold) and some of these algorithms contain predefined amounts of time (e.g., 1st predefined amount of time-9th predefined amount of time). These threshold values and the predefined amounts of time can be updated by the system administrator. Specifically, the system administrator could update these values by pushing an update to the individual remote terminals 28 of the system 10. Alternatively, if all of the calculations are remotely performed within a cloud server, then the system administrator can update the values within that server.
Alternatively, the system 10 may utilize self-updating threshold values in connection with certain algorithms These self-updating threshold values are different than the threshold values that can be manually updated (e.g., the threshold values and predefined amounts of time) because they do not require human (e g , system administrator) intervention. These self-updating thresholds provide a significant advantage over to the system 10 over conventional systems because it allows the system 10 to adapt to how the activity is currently being played. For example, changes to the rules of the activity and/or improvements in the protective equipment worn by specific players may significantly affect what a specific player experiences (e.g., impacts) during the activity. These significant alterations in what the specific player experiences (e.g., impacts) may alter the physiological parameter data (e.g., magnitude of impacts) that is recorded from these activities. Specifically, football helmets that were recently developed are testing over 20% better on the NFL's Helmet Laboratory Testing in comparison to football helmets that were developed fifteen years earlier. Without using self-updating threshold values, some of the above described training opportunities may not be accurately monitored and triggered and thus are not useful for the authorized user. Additionally, self-updating thresholds allow the system 10 to selectively tailor the amount of data that is the system 10 monitors and process in connection with each algorithm. Specifically, tailoring of the amount of data that is processed is accomplished by selecting narrower threshold values when necessary or broader threshold values when necessary. This selective balancing reduces processing requirements, increases efficiencies, decreases battery/power consumption, and provides other likely benefits to the system 10.
In certain embodiments, the following threshold values can be self-updating: (i) 2nd threshold or high magnitude impact threshold, (ii) 3rd threshold or single impact alert threshold, (iii) 4th threshold or a cumulative impact alert threshold, (iv) 5th threshold or number of alertable impacts threshold, (v) 6th threshold or number of high magnitude impacts threshold, (vi) 10th threshold or number of impacts threshold, (vii) 13th threshold or over baseline average number of impacts threshold, (viii) 14th threshold or impact load threshold, (ix) 17th threshold or over baseline average load threshold and (x) 19th threshold or location threshold. Specifically,
In addition to the above described steps, an alternative embodiment may perform an additional step to ensure that the self-updating threshold values are not being replaced too frequently. To help ensure this, the self-updating threshold values are only replaced if they are significantly different than the current self-updating threshold value. Significantly different in this context may mean where the recalculated self-updating threshold value is greater than 5% different than the current self-updating threshold value. Alternatively, significantly different could mean where the recalculated self-updating threshold value is greater than 15% different than the current self-updating threshold value.
Instead of calculating the self-updating threshold values from all data contained within the databases (e.g., team database 32 and national database 38) that is associated with the self-updating threshold value, the self-updating threshold values may be calculated based on a subset of data contained within the databases (e.g., team database 32 and national database 38) that is associated with the self-updating threshold value. Specifically, in this alternative exemplary embodiment, the self-updating threshold values may be calculated based on a weighting of all relevant data. To determine this weighted self-updating threshold values, all relevant data is weighted by a decaying factor. For example, data recorded 5 years ago may be multiplied by 0.5 decaying factor, thereby reducing the value of this data. It should be understood that certain data will be excluded from this calculation because is old enough to cause its weighting value to be zero due to the decaying factor. For example, if the decaying factor for data that is over 10 years old is 0; then regardless of the value of the data, this data is irrelevant to this calculation and will not be included within this calculation. One skilled in the art recognizes that weighting variables (e.g., time window and decay function) are adjustable. Calculating the self-updating threshold values in the manner described within this alternative embodiment may be beneficial because it excludes data that may be skewing the self-updating threshold values. For example, data that was recorded prior to significant rule changes and/or significant improvements in protective equipment.
Instead of calculating the self-updating threshold values using a weighted average, the system 10 may calculate the self-updating threshold values by simply excluding relevant data from the calculation that occurred before a predetermined amount of time. For example, relevant data that was collected over 15 years ago may be excluded. As described above, calculating the self-updating threshold values in this manner may be beneficial because it excludes data that may be skewing the self-updating threshold values. In a further embodiment, the self-updating threshold values may be calculated using a combination of the above techniques or methods.
i. GUI
Below is a high level description of the user interface that may be shown on the display 28a of the remote terminal 28 to inform the user of the training opportunities, other information based on the recorded physiological parameter data and/or other information that has been determined by the system 10 or information that the system 10 obtained from another source. The remote terminal 28 may also peripheral devices 28b that allow the authorized user to interact with the GUI.
The quick list 1060 is designed to show a subset of the information (e.g., identifier 1062, name 1064, position 1066, single impact alerts 1068, multiple impacts alerts 1070, and training opportunities 1072) about a subset of the players to allow the authorized user keep abreast of these players without going through multiple screens contained within the GUI 1000. The authorized user can add players to the quick list 1060 using the select player feature 1074 and can remove players from the quick list 1060 by pressing the “X” 1076 that is associated with the player. The player report 1080 section allows the authorized user to generate a report about a specific player. Additional details about these reports will be discussed in connection with
It should be understood that this dashboard 1002 provides a significant improvement in the efficiency of using the system 10 by bringing together and effectively visually presenting a limited list of high priority information without requiring the user to navigate through multiple screens in order to obtain this information. This in turn improves the efficiency of using the system 10 because it saves the user form navigating to a selected screen, manipulating the data associated with that screen, and then trying to interpret the resulting data. These factors tangibly improve the functionality of the system 10, particularly the user interface, and more particularly effectively displaying the user interface on a remote terminal 28 that has a small screen (e.g., mobile phone).
An authorized user can leave the dashboard 1002 and navigate to a first screen 1100 contained within the coaches tool 1110.
The impact trend chart 1150 shown in
The alert chart 1170 shown in
The training opportunities chart 1190 shown in
An authorized user can leave the first screen 1100 contained within the coaches tool 1110 and navigate to a second screen 1200 contained within the coaches tool 1110.
The practice planner 1150 shown in
Once the authorized user is finished editing the practice plan using screen 1300, the system 10 will calculate the total number of projected full contact minutes 1350. This number can be utilized by the authorized user to players who are not subject to too many full contact minutes per week/per month. Additionally, the drills 1320 in combination with the total number of projected full contact minutes 1350 may be utilized by the system to project how many impact alerts and training opportunities will occur during the practice. Specifically, the system 10 may utilize a learning algorithm that studies the team's alert data and impact matrixes that are generated during various drills in 1320. This projection of the number of impact alerts and training opportunities may also be used by the authorized user to help ensure that the players are not placed in a position to experience many alerts/training opportunities per week/ month. It should be understood that the authorized user may make the determination of how many full contact minutes and or projected alerts/training opportunities are acceptable per week by setting a 20th threshold value or acceptable threshold within the system 10. In this embodiment, the system 10 will compare the projections against this acceptable threshold and will provide warnings to the user, if the user deigns a practice plan that exceeds this projected threshold. Alternatively, system 10 may determine the 20th threshold value or acceptable threshold based on an analysis of the data associated with teams that play at a similar level. Like the above embodiment, the system 10 will provide the user with a warning to the user, if the user deigns a practice plan that exceeds this projected threshold.
Once the authorized user is satisfied with the practice plan, the authorized user can save the plan by selecting the save button 1305. Selecting the save button 1305 will send the user back to screen 1200. However, the updated version of the practice plan will replace the previously displayed version of the practice plan upon the user's arrival at screen 1200. Once back at screen 1200, the user can email 1202, print 1204 the practice plan, or provide additional notes 1206 about the practice plan. It should be understood that the system 10 may provide other options (e.g., request comments from another user, publish a practice plan to players, etc.) to the user in connection with the practice plan.
Also, this team daily view 1250 of the alert chart 1170 shows the player identifier (e.g., number) 1254, player name 1256, player position 1258, alert time 1260, alert type (e.g., single or cumulative) 1262 and alert location 1264 that the team recorded during the selected day (e.g., September 16). Instead of viewing the alert chart 1170, the authorized user can view the training opportunities chart 1190 by selecting button 1298, shown in
From the second screen 1200, the authorized user can select the unit button 1406 or select the arrow button 1410. The selection of either one of these buttons 1406, 1410, replaces the team daily view 1230 of the impact chart 1150 with a unit daily view 1430 of the impact chart 1150. In particular, the unit screen 1400 shows the impacts that the selected unit (e.g., offense) 1412 recorded during the selected day (e.g., Sep. 6, 2017). Specifically, this unit daily view 1430 of the impact chart 1150 shows the total number of impact that occurred during the day within the selected unit 1434, the total number of specific player's that are within the selected unit and experienced an impact during the day 1436, the time periods that correlate to the practice plan 1438, number of impacts broken down over a time period that is based on the practice plan 1440, magnitude of the impacts 1442 that the unit recorded during the selected day (e.g., Sep. 6, 2017).
Also, in connection with
From the second screen 1200, the authorized user can select the position button 1508. Alternately, the authorized user may select the arrow button 1511 from the screen 1400 shown in
From the second screen 1200, the authorized user can select the position button 1609. Alternately, the authorized user may select the arrow button 1613 from the screen 1600 shown in
As shown in
The specific player report 1700 for Jesse Katz, shown in
Similar to the transitional functionality between
In contrast to the data shown in
Referring to
Alternatively, the authorized user may select icon or indictor 1199 that is associated with VanderBerg Austin, the specific player training opportunity screen or player report 3300 is displayed for the selected training opportunity is displayed in
Further, the authorized user may select icon or indicator 1198 that is associated with Rex Bruce, the specific player training opportunity screen or player report 3400 is displayed for the selected training opportunity is displayed in
In addition to generating player reports, as shown in
Referring to
The report shown in
As is known in the data processing and communications arts, a general-purpose computer typically comprises a central processor or other processing device, an internal communication bus, various types of memory or storage media (RAM, ROM, EEPROM, cache memory, disk drives etc.) for code and data storage, and one or more network interface cards or ports for communication purposes. The software functionalities involve programming, including executable code as well as associated stored data. The software code is executable by the general-purpose computer. In operation, the code is stored within the general-purpose computer platform. At other times, however, the software may be stored at other locations and/or transported for loading into the appropriate general-purpose computer system.
A server, for example, includes a data communication interface for packet data communication. The server also includes a central processing unit (CPU), in the form of one or more processors, for executing program instructions. The server platform typically includes an internal communication bus, program storage and data storage for various data files to be processed and/or communicated by the server, although the server often receives programming and data via network communications. The hardware elements, operating systems and programming languages of such servers are conventional in nature, and it is presumed that those skilled in the art are adequately familiar therewith. The server functions may be implemented in a distributed fashion on a number of similar platforms, to distribute the processing load.
Hence, aspects of the disclosed methods and systems outlined above may be embodied in programming Program aspects of the technology may be thought of as “products” or “articles of manufacture” typically in the form of executable code and/or associated data that is carried on or embodied in a type of machine-readable medium. “Storage” type media include any or all of the tangible memory of the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives and the like, which may provide non-transitory storage at any time for the software programming. All or portions of the software may at times be communicated through the Internet or various other telecommunication networks. Thus, another type of media that may bear the software elements includes optical, electrical and electromagnetic waves, such as used across physical interfaces between local devices, through wired and optical landline networks and over various air-links. The physical elements that carry such waves, such as wired or wireless links, optical links or the like, also may be considered as media bearing the software. As used herein, unless restricted to non-transitory, tangible “storage” media, terms such as computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution.
It is to be understood that the invention is not limited to the exact details of construction, operation, exact materials or embodiments shown and described, as obvious modifications and equivalents will be apparent to one skilled in the art. Accordingly, the invention is therefore to be limited only by the scope of the appended claims. While the specific embodiments have been illustrated and described, numerous modifications come to mind without significantly departing from the spirit of the invention, and the scope of protection is only limited by the scope of the accompanying Claims.
This Application claims the benefit of U.S. Provisional Patent Application Ser. No. 62/778,559, entitled “Systems And Methods For Providing Training Opportunities Based On Data Collected From Monitoring A Physiological Parameter Of Persons Engaged In Physical Activity,” filed on Dec. 12, 2018, all of these applications which are incorporated herein by reference and made a part hereof. U.S. Pat. No. 10,105,076 entitled “Systems And Methods For Monitoring A Physiological Parameter Of Persons Engaged In Physical Activity,” filed on Sep. 4, 2012, U.S. Provisional Patent Application Ser. No. 61/530,282 entitled “System & Method For Monitoring A Physiological Parameter Of Persons Engaged In Physical Activity,” filed on Sep. 1, 2011, and U.S. Provisional Patent Application Ser. No. 61/533,038 entitled “System & Method For Monitoring A Physiological Parameter Of Persons Engaged In Physical Activity,” filed on Sep. 9, 2011, the disclosure of which is hereby incorporated by reference in its entirety for all purposes. U.S. Pat. No. 9,622,661 entitled “Impact Monitoring System For Players Engaged In A Sporting Activity,” filed on Oct. 7, 2013 and U.S. Provisional Patent Application Ser. No. 60/239,379 entitled “Multi-Directional Head Acceleration System,” filed on Oct. 11, 2000, the disclosure of which is hereby incorporated by reference in its entirety for all purposes. U.S. Pat. No. 8,797,165 entitled “System For Monitoring A Physiological Parameter Of Players Engaged In A Sporting Activity,” filed on Sep. 13, 2005 and U.S. Provisional Patent Application Ser. No. 60/609,555 entitled “System For Measuring And Monitoring Acceleration Of A Body Part,” filed on Sep. 13, 2004, the disclosure of which is hereby incorporated by reference in its entirety for all purposes. U.S. Pat. No. 8,548,768 entitled “System And Method For Evaluating And Providing Treatment To Sports Participants,” filed on Jan. 9, 2005, and U.S. Provisional Patent Application Ser. No. 60/642,240 entitled “System And Method For Evaluating And Providing Treatment To Sports Participants,” filed on Jan. 7, 2005, the disclosure of these are hereby incorporated by reference in their entirety for all purposes. U.S. patent application Ser. No. 16/691,436 entitled “Football Helmet with Components Additively Manufactured to Manage Impact Forces,” filed on Nov. 21, 2019, U.S. Design patent application Ser. No. 29/671,111, entitled “Internal Energy attenuation assembly of a Protective Sports Helmet,” filed on Nov. 22, 2018 and U.S. Provisional Patent Application Ser. No. 62/770,453, entitled “Football Helmet With Components Additively Manufactured To Optimize The Management Of Energy From Impact Forces,” filed on Nov. 21, 2018, the disclosure of these are hereby incorporated by reference in their entirety for all purposes. U.S. patent application Ser. No. 16/543,371 entitled “System And Method For Designing And Manufacturing A Protective Helmet Tailored To A Selected Group Of Helmet Wearers,” filed on Aug. 16, 2019 and U.S. Provisional Patent Application Ser. No. 62/719,130 entitled “System and Methods for Designing and Manufacturing a Protective Sports Helmet Based on Statistical Analysis of Player Head Shapes,” filed on Aug. 16, 2018, the disclosure of these are hereby incorporated by reference in their entirety for all purposes. U.S. patent application Ser. No. 15/655,490 entitled “System And Methods For Designing And Manufacturing A Bespoke Protective Sports Helmet,” filed on Jul. 20, 2017, U.S. Pat. No. 10,159,296 entitled “System and Method for Custom Forming a Protective Helmet for a Customers Head,” filed on Jan. 15, 2014, U.S. Pat. No. 9,314,063 entitled “Football Helmet with Impact Attenuation System,” filed on Feb. 12, 2014, U.S. Design Pat. D764,716 entitled “Football Helmet,” filed on Feb. 2, 2012, U.S. Pat. No. 9,289,024 entitled “Protective Sports Helmet,” filed on May 2, 2011, and U.S. Design Pat. D603,099 entitled “Sports Helmet,” filed on Oct. 27, 2009, the disclosure of these are hereby incorporated by reference in their entirety for all purposes. Crisco J J, et. al. An Algorithm for Estimating Acceleration Magnitude and Impact Location Using Multiple Nonorthogonal Single-Axis Accelerometers. J Bio Mech Eng. 2004; 126(1), Duma S M, et. al. Analysis of Real-time Head Accelerations in Collegiate Football Players. Clin J Sport Med. 2005; 15(1):3-8, Brolinson, P. 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| Number | Date | Country | |
|---|---|---|---|
| 62778559 | Dec 2018 | US |
