Patent Application
20210228942
This application claims priority of Finnish patent application number FI20205071 which was filed on Jan. 23, 2020 and the contents of which is incorporated herein by reference.
The present invention relates generally to stretching optimization of a person doing fitness or physical training activity.
Stretching is a method and activity to heal and refresh human body, and especially muscles and tendons. There are many ways to stretch and different kinds of definitions to stretching activities. Wikipedia article “Stretching” teaches the following:
Stretching is a form of physical exercise in which a specific muscle or tendon (or muscle group) is deliberately flexed or stretched in order to improve the muscle's felt elasticity and achieve comfortable muscle tone. The result is a feeling of increased muscle control, flexibility, and range of motion. Stretching is also used therapeutically to relieve cramps and muscle pains.
Increasing flexibility through stretching is one of the basic tenets of physical fitness. It is common for athletes to stretch before for warming up and after exercise for reducing risk of injury and for increasing performance.
Stretching can be harmful or injurious when performed incorrectly. There are many techniques for stretching in general, but depending on which muscle group is being stretched, some techniques may be ineffective or detrimental, even to the point of causing hypermobility, instability, or permanent damage to the tendons, ligaments, and muscle fiber. The physiological nature of stretching and theories about the effect of various techniques are not well known. There are different opinions and statements for pros and cons of stretching in different situations.
For example, static stretching as a part of some warm-up routines, a study indicated that it weakened muscles. So dynamic stretching is recommended before exercise, while static stretching helps to reduce muscle soreness afterwards.
According to the Wikipedia article “Stretching” there are five different types of stretching: ballistic, dynamic, SMF (i.e. Self-Myofascial Release) stretching, PNF (i.e. Proprioceptive Neuromuscular Facilitation) stretching, and static stretching. Ballistic stretching is a rapid bouncing stretch in which a body part is moving with momentum that stretches the muscles to a maximum. Dynamic stretching is a walking or movement stretch. PNF is a type of stretch for a particular muscle and its specific job, so resistance should be applied, and then the muscle should be relaxed. Static stretching is a type of stretch where a person stretches the muscle until a gentle tension is felt and then holds the stretch for thirty seconds or until a muscle release is felt, without any movement or bouncing. The SMF stretching can be performed by using e.g. a tennis or a golf ball, or a foam roller as an assisting tool, and this type of stretching works by targeting soft connective tissue.
Although many people engage in stretching before or after the exercise, the medical evidence has shown that this has no meaningful benefits in preventing muscle soreness.
Stretching does not appear to reduce the risk of injury during the exercise. There is some evidence that pre-exercise stretching may increase range of movement for the athletes.
It is known that stretching will be beneficial in many cases whereas there are cases when it does not seem to give any benefits and being even harmful. The problem to a fitness exerciser is that there is no tool to tell if it makes sense to stretch and if so, when it is an optimal time to do that, and further, which kind of stretching is optimal and how much.
Patent application publication US 2011/0184247 (“Contant”) discloses a health guidance system which provides suggestions to the user based on current monitored activity or length of time since a previous event, and the system can alert the user and provide routine exercise or activity suggestions for good health. Contant is however very generic and it does not guide to optimal stretching according the activity done before or follow up the stretching which has been performed and follow effects of stretching.
Patent application publication US 2017/0206795 (“Kaleal”) discloses a method to provide a virtual coach for a user based on biochemical data and physiological state. The virtual coach (i.e. avatar) will generate a program and guidance for a user and follow if the user is deviating from the program. Kaleal does not discuss about optimal stretching related to previous activities or disclose any link between physical training and stretching. Also, it does not disclose optimal timing or activity-based stretching level and volume and feedback related to the performed stretching.
Thus, prior art documents present clear problems which need to be tackled.
The present invention introduces a method for providing optimal stretching guidance to a user (102) by analyzing physical activities of the user (102), in a first aspect of the present invention. The method is characterized in that the method comprises the steps of:
In an embodiment of the present invention, the stretching index comprises at least one of the following: an optimal stretching type, an optimal amount or duration of stretching and an optimal stretching time of the day.
In an embodiment of the present invention, an optimal amount of stretching and an optimal stretching time of the day depend on an activity type, and activity volume or activity intensity during the last 24 hours.
In an embodiment of the present invention, the optimal stretching type depends on the activity type done.
In an embodiment of the present invention, the method further comprises the steps of:
In an embodiment of the present invention, the set of measurement data related to the user (102) is measured by a wearable electronic device (104) and transmitted by a mobile communication device (106) to a server (108) for analysis, or the measurement and transmission are both performed by a wearable electronic device (104) and a mobile communication device (106) as a combined device.
In an embodiment of the present invention, the mobile communication device (106) is a smartphone or a tablet, and the wearable electronic device (104) is a wrist device, a ring-type of a device placeable in a finger, or a chest-attachable device.
In an embodiment of the present invention, the method further comprises the step of:
In an embodiment of the present invention, the method further comprises the steps of:
In an embodiment of the present invention, the method further comprises the step of:
In an embodiment of the present invention, the method further comprises the steps of:
In an embodiment of the present invention, the method further comprises the steps of:
In an embodiment of the present invention, the method further comprises the step of:
In an embodiment of the present invention, the collected set of information comprises at least one of weight, height, fitness level, main activity type, and training or sport type of the user (102).
In a second aspect of the present invention, there is presented a system for providing optimal stretching guidance to a user (102) by analyzing physical activities of the user (102), wherein the system comprises:
The system is characterized in that
In an embodiment of the present invention, the system further comprises the mobile communication device (106) which is configured to transmit the set of measurement data related to the user (102) to the server (108) for analysis, or the measurement and transmission are both configured to be performed by the wearable electronic device (104) and the mobile communication device (106) as a combined device.
In an embodiment of the present invention, the mobile communication device (106) is a smartphone or a tablet, and the wearable electronic device (104) is a wrist device, a ring-type of a device placeable in a finger, or a chest-attachable device.
In an embodiment of the present invention, the wearable electronic device (104) comprises at least one of the following: a heart rate sensor, a light sensor, an activity sensor, a temperature sensor, a rechargeable battery, an optional sensor, a microprocessor (MCU), a memory, an output indicator comprising a piezo and/or LED indicator, and a communication unit comprising wireless and/or Bluetooth transmission.
In an embodiment of the present invention, the mobile communication device (106) comprises at least one of the following: an input device comprising at least one of a touchpad, a touch display, a microphone, a camera and a battery; an output device comprising at least one of a display, a piezo element and a speaker; a rechargeable battery; an optional sensor comprising at least one of a light, location, GPS, and motion sensor; a microprocessor (MCU); a memory; a wireless communication unit to the wearable electronic device (104); and a wireless communication unit to a network (110).
In an embodiment of the present invention, a network (110) comprises at least one of the following: a microprocessor (MCU); a memory; an output indicator comprising a piezo and/or LED indicator; a wireless communication unit to the mobile communication device (106); and a wireless or wired communication unit to the server (108).
In an embodiment of the present invention, the server (108) comprises at least one of the following: an input device comprising at least one of a touchpad, a touch display, a microphone, a camera and a battery; an output device comprising at least one of a display, a piezo element and a speaker; a power unit; a microprocessor (MCU); a memory; a wireless or wired communication unit to the network (110); and a database.
In a third aspect of the present invention, there is presented a computer program product for providing optimal stretching guidance to a user (102) by analyzing physical activities of the user (102), wherein the computer program product comprises program code, which is executable when run in a processor. The computer program product is characterized in that the computer program product is configured to execute the steps of:
The following detailed description illustrates embodiments of the present invention and exemplary ways in which they can be implemented.
The present invention introduces a method for providing optimal stretching guidance to a user by analyzing physical activities of the user. A corresponding system and a computer program product are introduced as well.
The present invention gives a solution to the above-mentioned problems. It will collect information about the user by measuring and analyzing data and by providing instructions to stretch in a substantially optimal way.
The present invention will be a tool to collect information and feedback about workloads and activity before stretching, about the stretching done, and status, feelings, recovery and readiness after the stretching. The data collected from a user and further, from multiple users will be used to analyze and optimize stretching methods, to guide to do correct and proper stretching activities, and movements, stretching time, and stretching amount or volume by stretching at the right time for that particular user. By volume, it is meant the stretching movements or repetitions multiplied by the time used for stretching.
In other words, the general system structure is illustrated in
In other words, referring still to
Optionally, the wearable electronic device 104 of the system 100 can be a ring configured to be suitably worn at a finger, such as e.g. an index finger, of the user 102. However, in an embodiment, the system 100 may be associated with other wearable electronic devices, such as a device adapted to be worn at wrist, chest or any suitable body part of the user 102, from where physiological data of the user 102 can be measured. In such an instance, the wearable electronic device 104 may be configured to have a size to be suitably worn at such a body part of the user 102.
In an embodiment, the wearable electronic device 104 comprises means for measuring a set of measurement data related to the user 102. Specifically, the set of measurement data may comprise one or more of the following: heart rate, movement of the user, temperature of the user's skin. The wearable electronic device 104 may comprise at least one sensor as means for measuring the set of measurement data related to the user 102. Furthermore, the at least one sensor may be selected from a group consisting of an accelerometer, a gyroscope and a magnetic field sensor (i.e. a magnetometer), for measuring user's movements. Furthermore, the heart rate may be measured using a photon (for example infrared, IR) source and a photon detector also arranged on an inner surface of the wearable electronic device 104. Additionally, the wearable electronic device 104 may comprise a light sensor arranged on an outer surface of the wearable electronic device 104 for measuring ambient light. A temperature sensor for measuring the temperature of the user 102 is preferably arranged against the skin of the user, for example on the inner surface side of the ring. One temperature sensor can be arranged to measure ambient temperature by arranging the sensor to be on the outer surface of the ring. The measured sensor data from the group of sensors, such as the data of the motion sensor, the optical electronics, the light sensor, the skin temperature sensor, and the ambient temperature sensor, associated with the user 102 and measured by the wearable electronic device 104 may be further analyzed to obtain the set of measurement data.
The wearable electronic device 104 comprises means for measuring a circadian rhythm and duration of sleep of the user 102. It also comprises means to measure user's activity and activity type and activity duration. Specifically, the circadian rhythm may refer to physical, mental, and behavioral changes in the user 102 that follow a daily cycle of the user. More specifically, the user 102 may experience a peak in energy levels at specific durations of time in a day. Similarly, the user 102 may also experience a drop in energy levels at specific durations of time in a day. Such changes in the user 102 may influence an overall sleep pattern thereof. Furthermore, such changes in the user 102 may be measured to estimate the circadian rhythm of the user 102.
Optionally, the duration of sleep is measured as a time between the moment of falling to sleep and the moment of waking up, wherein said moments are determined based on at least one of pre-defined changes in the heart rate and pre-defined changes in body temperature of the user 102. For example, the duration of sleep of the user may be derived from a hypnogram. Alternatively, the duration of sleep of the user may be measured with the data from the motion sensor (i.e. when the user went to bed and when the user woke up), which should be static or comprise minute variations (due to no physical provocations). Therefore, based on the data from the motion sensor, how long the user 102 slept can be determined. Furthermore, the data from the motion sensor and the hypnogram may be correlated to measure the duration of sleep.
In an embodiment, the circadian rhythm may be measured using various sensor data. Furthermore, the circadian rhythm of the user 102 may be affected by a chronotype of the user 102. As mentioned above, the wearable electronic device 104 comprises the light sensor capable of measuring illumination level as well as “colour space”. The colour space refers to visible frequencies of the light. For example, if the light sensor detects blue light then the light sensor considers the light to be day light. This can be used to determine if the ambient light is from artificial light or natural light. Further, the light sensor can be used to detect illumination conditions during the sleeping time and corrected therewith. Therefore, based on the data from the light sensor, the temperature sensor and the sleeping pattern measurements, a circadian rhythm of the user can be measured. The circadian rhythm may comprise information such as at around 2 AM the user 102 gets the deepest sleep, at 4:30 AM the user 102 has the lowest body temperature, at around 6:45 AM the user 102 has the sharpest (i.e. highest) blood pressure, and so forth. These are merely exemplary times for a certain user.
The circadian rhythm can be further extended to describe a typical day of the user 102. A typical day is described in
According to an embodiment, the wearable electronic device 104 also comprises electronic components configured to collect and analyze data from the at least one sensor. For example, as shown in
The mobile communication device 106 is operable to collect a set of information related to the user 102. Specifically, the first set of information may comprise information such as height, weight, age, gender, location and so forth related to the user 102. Optionally, the set of information may comprise physiological performance related information based on an external data input by the user 102. Optionally, the set of information may comprise activity habits, typical activity or sports, or typical training types and amounts, possible training plan or so. Optionally, the set of information can be automatically or semi-automatically received or grabbed by a software or application running in the mobile communication device 106. The application can for example read the electronic calendar, emails, messages etc. to find a schedule for training and activity sessions, and possibly also, which type of activity is planned and scheduled. Optionally, the set of information can comprise stretching routines or habits of the user. This may comprise, which kind of movements are familiar or already in use, and how much stretching has been done and intended to be done, at which day and/or time they have been done and how long is a single stretching session. Specifically, the user 102 may manually input information related thereto in the mobile communication device 106. Furthermore, the physiological performance related information may be derived from the physiological data (or parameters) of the user 102 measured by the wearable electronic device 104, such as heart-rate variability, a respiration rate, a sleeping pattern of the user, a hypnogram, user's stress level, activity amount and type, and so forth. Additionally, but optionally, the physiological performance related information can be biased or influenced by some external data (or factor), which is different from the internal data, such as the biological signals or physiological data associated with the user 102.
In an embodiment, the external data comprises at least one of travel information, time zone, calendar, working schedule, and holidays. The external data may be received from the user 102 as user input with the help of the mobile communication device 106. For example, the mobile communication device 106 may be provided with various user interfaces associated with such external data allowing the user 102 to make selection for the external data. Furthermore, the mobile communication device 104 may comprise sensors, such as a location sensor (e.g. for GPS) to determine the location of the user 102, i.e. if the user 102 has travelled some distance and moved out of his city/country. Further, the travel may be of such a nature which may influence sleep of the user 102. For example, this may be a travel plan which requires travelling at night, travelling to different time zones, or travelling in difficult conditions, such as in rough terrain. Additionally, the information of the travels may be such that they may influence the physiological state (parameters or data) of the user 102, when associated with the current travel. In an example, the information of the travels may be comparatively recent (for example few days ago, or a week or a month), such that when the user takes the current travel (or a new travel), the information of the past travels and the future travels may influence the sleep of the user.
The information of travels can be taken into consideration in user's circadian rhythm and the description of the day schedule of the user 102, such as the one shown in
In an embodiment, the mobile communication device 106 and the server (arrangement) 108 (see
Furthermore, the mobile communication device 106 and the server arrangement 108 are configured to perform analysis of the activity and inactivity data to recognize activity or inactivity periods, their duration, activity type and amount. The analysis of activity and inactivity data comprises information about activity type (for example running, walking, muscle training, Pilates or yoga, sitting, or standing or lying. Furthermore, the mobile communication device 106 and the server arrangement 108 are configured to perform further analysis of the activity and inactivity data to form a stretching index. The stretching index comprises a suitable stretching type and amount and stretching time related activity and user's circadian rhythm. The stretching time is for example optimally selected to be prior to the user's go-to-bed time or in the morning or in the afternoon.
For example, the analysis may be performed partly by the mobile communication device 106 and partly by the server arrangement 108. Alternatively, the entire analysis may be performed by the mobile communication device 106.
Throughout the present disclosure, the term “server” or “server arrangement” relates to a structure and/or a module which includes programmable and/or non-programmable components configured to store, process and/or share information. Optionally, the server arrangement 108 comprises any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks. Furthermore, it should be appreciated that the server arrangement 108 may be either a single hardware server or a plurality of hardware servers operating in a parallel or distributed architecture. In an example, the server may comprise components such as a memory, a processor, a network adapter and the like, to store, process and/or share information with other computing components, such as the mobile communication device 106. Optionally, the server 108 is implemented as a computer program which provides various services, such as a database service.
The server 108 block diagram is presented in
The server arrangement 108 is operable to receive the set of information related to the user 102 from the mobile communication device 106, and receive the set of measurement data related to the user 102, the measured circadian rhythm and the duration of sleep of the user 102 from the wearable electronic device 104 or from the mobile communication device 106. The circadian rhythm may be also defined in the server 108 based on the measurement data from the wearable electronic device 104. The server arrangement 108 is operable to determine so-called sleep scores for a predefined number of days. Specifically, the server arrangement 108 is operable to determine a sleep score for each of the predefined number of days from the first set of information, the set of measurement data, the circadian rhythm and the duration of sleep of the user 102, in an embodiment. More specifically, the server arrangement 108 may be operable to analyze the received parameters of the user 102 to determine a sleep score of a sleep of the user 102, circadian rhythm, daily schedule, activity type, activity amount, activity duration and activity time of the user 102, and stretching index of the user 102.
As the technology evolves with larger memories and faster processing capabilities in mobile communication devices, it is technically possible that the server's functions and tasks can be realized wholly or partially in a mobile communication device.
The activity of the user 102 can be measured by a motion sensor in a wearable electronic device 104. It is however possible to measure activity with a mobile communication device 106 with its motion sensor or location sensor. An activity chart can be presented, for example, as motions per minute. The sensor or measuring electronics may have a threshold for a motion signal which can be, for example, an acceleration signal of 0.05*g, i.e. appr. 0.5 m/s2. When the acceleration signal exceeds the threshold, a counter for motions/minute is added by one. After every minute that cumulative count is recorded and the counter is reset for counting motions for the next minute.
In an example, activity counts for one day are shown in a chart shown in
Typical motions/minute values can be used as criteria to detect the activity period of the user 102. A clock time can be used to make rules to analyze the activity type. For example, during afternoon a high activity level (more than 200 motions/minute) means training; between 22-08 o'clock a low activity level (less than 20 motions/minute) means sleeping, and between 8-16 o'clock a low activity level (less than 20 motions/minute) means sitting or inactivity.
Training type can be defined from activity level, time of activity, duration of activity, and possibly from heart rate and temperature, or it can be input by a user.
Other sensor information can be used for analysis. For example, temperature is elevated during a strenuous exercise as it can be seen between 18-19 o'clock in
The heart rate (HR) can be used as an indicator, too. It is known that HR responds very consistently to the amount of exercise. The chart of
Training type can be defined from activity level, time of activity, duration of activity, and possibly from heart rate and temperature, or it can be input by a user.
Different rules can be set to analyze and differentiate different activity types.
For example, following rules can be used:
The rules can be tuned and personalized in different embodiments. Also advanced machine learning and artificial intelligence (AI) tools can be used to enhance the system with more accurate analysis methods.
The optimal stretching time depends on different things. At some day it might be reasonable to do the stretching at the same day as the exercise, and sometimes it is fine to do the stretching the next day after the exercise. The stretching time can be in the morning, in the afternoon or in the late evening. However, it is important to adapt the stretching time to the user's schedule so that it is fitting to the other daily routines of the user and it is not disturbing, for example, the go-to-sleep time or the sleeping itself.
In many embodiments, the system will guide to do stretching close to the go-to-bed time, typically 2-3 hours before that. This will help to relax at the same time and to give muscles optimal time to recover and also this helps to fall into sleep quickly. However, other times such as late afternoon or morning time can be proposed and scheduled depending on user's daily schedule.
Different exercises load muscles in different ways. Stretching needs to vary according to the exercise done and also concerning the duration of the proposed stretching. A table in
In this exemplary table, the listed possible activity types are walking, running, tennis, badminton, whole body muscles, upper body muscles, lower body muscles, yoga and Pilates. Stretching amounts per muscle may vary between 10 seconds and 50 seconds there. The total stretching time may vary between 10 and 25 minutes there.
For example, after tennis exercise the stretching amount for a female user can be 30 seconds per muscle for a total of 20 minutes. A further instruction may be, for example, that muscles to be stretched are arms, wrists, back, shoulders, hamstrings, and legs. Each muscle will be stretched for 30 seconds and then kept in 10 seconds' rest, and then continued to the next muscle repeating until the total stretching time is full.
Age rule(s) can tune a general rule (in the actual table) to a more convenient one for an older user. An age rule can be that if the user is younger than 55 years, he/she is guided to use the stretching amounts in the table of
A table in
In this exemplary table, the listed possible activity types are walking, running, tennis, badminton, whole body muscles, upper body muscles, lower body muscles, yoga and Pilates and also, an inactivity type of sitting. The stretching types are listed in the table according to an embodiment, and the body parts to be stretched there are named among the lower body, legs, full body, arms, upper body, neck and shoulders, and pelvis.
In an embodiment, the server arrangement 108 is further operable to store the measured circadian rhythm, daily schedule, the measured duration of sleep, and activity type, activity amount, activity duration and activity time of the user 102, and stretching index of the user 102. For example, the measured circadian rhythm, activity type and the measured duration of sleep may be stored in a database of the server arrangement 108. In an embodiment, the operation and working of the server arrangement 108 and the database can be implemented with a dedicated computer system, a cluster of computers and/or a cloud service (or any combination thereof). Furthermore, the stored information combined to an input by the user 102 can be used in a step of re-calibration of the wearable electronic device 104. As mentioned above, the input by the user 102 may be associated with the external data, which comprises at least one of travel information, time zone, calendar, working schedule, and holidays.
In the present disclosure, the term “sleep score” may relate to a score provided to a sleep that the user may obtain in a span of a day (namely, a duration of 24 hours). Specifically, the sleep score of the sleep of the user may be based on at least one of: a time of falling asleep (namely, a go-to-bed time), wake-up time, circadian rhythm and physical parameters of the user, quality of the sleep (namely, sleep efficiency), sleep onset latency and so forth. In an example, the sleep score may be a numerical score or an alphabetic or an alphanumeric grade. Specifically, the numerical score may be determined on a scale of zero to 100. In such an example, a numerical score higher than 80 may be indicative of an optimum sleep duration. Similarly, in such an example, a numerical score lower than 70 may be indicative of a low sleep duration. Furthermore, the user 102 may obtain a high sleep score by sleeping an adequate number of hours at times coinciding with the circadian rhythm of the user 102.
In an embodiment, the server arrangement 108 is operable to measure the sleep efficiency of the user 102. Specifically, the sleep efficiency is indicative of a quality of sleep of the user 102. More specifically, the sleep efficiency may be based on factors such as movements of the user 102 while asleep (indicative of restlessness and wake-ups while in bed; and more deeper causes can be stress or use of alcohol in the previous day), duration of sleep in a deep sleep stage, rapid eye movement (i.e. REM) data, hypnogram and so forth. Furthermore, the sleep efficiency may be a numerical grade. Optionally, the server arrangement 108 is operable to determine the sleep efficiency based on the set of measurement data received from the wearable electronic device 104.
The server arrangement 108 is operable to analyze the sleep score to determine an optimum bedtime window for the user 102. Specifically, the server arrangement 108 is operable to determine a length of the optimum bedtime window, and a start time and an end time of the optimum bedtime window.
The system will generate a practice stretching guide and respective alert to the user 102. An example of the alert and stretching guide is shown in
Finally, as a fourth step, the system determines a stretching index for the user based on the set of information, and measurement data related to activity of the user 1308.
The advantages of the present invention are that the presented processes and presented system for intelligent stretching guidance for users makes the recovery from an exercise quicker for professional and recreational sports exercisers. Also, the sleep quality may improve, and the results in the sports activities themselves may well enhance by the application of the present invention. General quality of life can get better with the present invention, because it allows for optimized work/free time balance, and also for optimized training/recovery time balance. The present invention thus enables the user to be fresher and less tired during the daytime as well, supporting an efficient worktime for normal taxpaying citizen and/or efficient training time for professional/recreational athletes.
The present invention is not restricted merely to the embodiments presented in the above but the present invention may vary within the scope of the claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 20205071 | Jan 2020 | FI | national |
