VELOCITY BASED SAFETY SYSTEM AND METHOD FOR FITNESS MACHINES

Information

  • Patent Application
  • 20210322830
  • Publication Number
    20210322830
  • Date Filed
    April 16, 2021
    3 years ago
  • Date Published
    October 21, 2021
    3 years ago
  • Inventors
    • Blaszczyk; Yvonne
    • Gießl; Sandro
    • Dunstan; Mark
    • Bridges; Jason
    • Weinberger; Andreas
    • Grabisch-Mikula; Andreas
    • Shirley; Alexander Thomas
  • Original Assignees
Abstract
Training machine and a method of controlling a training machine comprising a resistance component configured to provide at least a resistance during training, a control configured to control the resistance component during training and a safety component configured to monitor the training machine on the basis of velocity and/or any derivative thereof.
Description
FIELD

This invention is directed to a system and a method to increase safety for persons using automated fitness machines.


BACKGROUND

Many strength- or exercise machines nowadays are highly automated and driven by various characteristics that are stored either in a computer or are adapted by proper selection of parts that provide sufficient safety at locations that may be critical. Such a critical position or location may be a point where a motion conducted by the exercising person is at or close to an end position. Risk of joint lock may occur with highly harmful consequences.


Other than a joint lock, also an overstretching of limbs or muscles may be prevented from occurrence.


DE 10 2016 015 109 (B3) relates to a resistance source for applying a training resistance force (F) along a training path of a strength training device, comprising a control unit by means of which the resistance force to be applied is adjustable during training, characterized in that the control unit is arranged to limit the force which can be applied by means of the resistance source to a predetermined maximum at a turning point (x) or another risk point (x, α, α) of the training path.


Further, JP 2009 225870 (A) discloses a training machine for enabling the exerciser to exercise under a load appropriate for the individual exercise capability and physical function of the exerciser. The exerciser (E) enters a desired velocity-load characteristic into a load characteristic input device, and the velocity-load characteristic is stored in the load characteristic memory device. A load instruction value is determined according to the velocity-load characteristic and to the velocity inputted from a velocity calculation means into the load characteristic memory device and transmitted to a control means. The control means rotates a servomotor with a torque instruction value corresponding to the load instruction value. A movement mechanism converts the rotation into linear movement to move a movable unit. With this, the exerciser can carry out training of reciprocal movement.


In the U.S. provisional application 61/961,304 (P) a bi-directional resistance exercise apparatus designed to offer resistance to opposing muscle groups is disclosed. The apparatus provides continuous and sequential resistance during the extension and flexion phases. The apparatus includes a controller including a graphic display. Resistance is provided by a variable resistance element such as a magnetic brake. A chair or other body supporting devices support the user depending on the exercise being performed. Force is applied by the user to a rotatable limb or torso retaining member and is transferred to the shaft of the magnetic brake. The brake applies variable resistance determined by the software instructions issued by the controller. The resistance levels can be pre-programmed or specifically selected by the user. The brake force varies instantaneously during the flexion and extension phases based on the angular position of the input shaft. A record of the exercise activity can be stored and displayed by the microprocessor controller.


US 2018 326,242 (A1) discloses a device for an exercise apparatus that includes a linear adjustment system and a sensor coupled to the linear adjustment system. The exercise apparatus includes a loading interface and a frame coupled to the loading interface for performing an exercise. The linear adjustment system fixes the loading interface of the exercise apparatus at any one of a plurality of functional positions in a functional range of the loading interface. The sensor measures the force exerted on the linear adjustment system. A correlating mechanism is used to correlate the force exerted on the linear adjustment system with the force exerted on the loading interface. The device allows exercisers to exert high or maximum loads in any one of a plurality of positions throughout their entire range of motion without first passing through a weak range of motion.


In KR 2011 0036066 (A) a simulator obtains muscle activities and joint contact forces by a computer simulation according to an operating condition for moving a support base of a passive exercise machine. A condition limiting unit finds intermediate conditions corresponding to desirable muscle activities and joint contact forces from muscle activities and joint contact forces obtained with the simulator according to different operating conditions. A motion simulator moves the support base according to the intermediate conditions. A myoelectric measurement device measures myoelectric potential of a subject supported by the support base. An evaluation device selects an operating condition corresponding to a larger muscle activity quantity from measurement results of muscle activity to define it as an operating condition of the passive exercise machine.


SUMMARY

The goal underlying the present invention is to provide an improved or ameliorated system and method of a safety device and process to prevent from unwanted overstretching of muscles or tendons. Also, joint lock situations shall be prevented. The goal can be reached by the subject matter of the present invention and as further exemplified by the description and the claims.


The subject matter in accordance with the claims, embodiments and/or the description attains this problem.


This invention preferably provides a system and a method to equip and/or operate an isokinetic training machine with a provision to prevent injuries of a training person while exercising on such a machine.


A training machine can comprise a resistance component configured to provide at least a resistance during training. The resistance can assist the training person to push and/or pull with a counter-force and/or torque, such as or similar to weights that are operated in old-fashioned training machines. The machine can also comprise a control configured to control the resistance component during training. The control can be connected and/or affiliated to a resistance source in order to control that source of resistance. Further, a safety component can be configured to monitor the training machine. In accordance with the invention this can be provided on the basis of velocity and/or any derivative thereof. The derivation can be a derivation over time.


The acceleration may be defined as a derivation over time of the velocity that is determined by a sensor in or at the machine. Further, also the jerk is a derivation of the acceleration and a second derivation of the velocity. The time as referenced herein is infinitesimally small.


Any derivation of the velocity may be fed into a control and can there cause a reaction that may initiate an action at the resistance component.


The reaction of the resistance component may be an emergency stop, switching the entire training machine or parts of it powerless, withdraw the last motion of an actuator that is in tactile contact with a trainee and/or reduce the resistance quickly so that the user feels the resistance being reduced before the idle state. Other actions may be initiated by the safety component as well or alternatively.


The resistance component may comprise a motor, such as a DC or AC motor. The motor may be a controllable actuator that can be the source of a motion. Besides an electric motor, also a pneumatic or a hydraulic actuator may be provided. The motor may be configured as a conventional rotating device but also be configured to execute a linear motion.


The motor may be controlled, supervised or managed by a motor control that can comprise one or more electrical circuits; a micro control may be provided. The control may comprise one or more input channels that receive data from various sources. These values may comprise information about the location or position of an actuator that can be moved by the resultant force of the trainee countered by the resistance component. Further, additionally or alternatively, the location of the resistance component may be featured.


As values of the input data, other than the position or location also a velocity or an acceleration value may be input into the control. The velocity and/or the acceleration values may be originating from calculated values from the position; however, also one or more sensors may detect their readouts directly from specialized sensors, such as velocity- and/or acceleration sensors. Also, indirect determination of the values may be fed into the control, like they could comprise laser detectors, camera observations or ultrasonically harvested data.


The values may comprise velocity data and/or acceleration data.


The safety component may also be configured to determine values like the velocity or the acceleration and can further also detect a jerk motion during training. The safety component may embrace one or more detectors as disclosed and may either deliver their directly acquired data, their derived data or data from external control measures to the control that in turn then gives control orders to the motor.


The training machine may be fitted with one or more measurement components that can be controlled and/or monitored by the safety component. The measurement component may comprise the sensor(s) that can deliver the data and values that can supervise the training progress. Further to linear motions, also rotational motions can be detected by appropriate sensors, like velocity and/or acceleration values.


The safety component and/or the measurement component may be configured to derive velocity and/or acceleration data from positional data derived over time. Also, as a time derivative may be calculated from distance over time or velocity and/or acceleration data.


The training machine may comprise one or more training mechanism(s). Such training mechanism may comprise one or more levers, pulley arrangements, or similar. The training mechanism may comprise arrangements to train certain parts of the trainee, such as muscles, groups of muscles, joints, groups of joints or combinations thereof. In one embodiment, the training machine is arranged for the execution of isokinetic exercises.


The training machine may comprise the training mechanism integrally with the other components disclosed herein. However, it should be understood that the training machine may actuate a training mechanism that is not integrally incorporated into the training machine, but may be connected to the training mechanism. The resistance component may be configured to drive the training mechanism.


The training machine may comprise a sensor that may be configured to deliver at least one signal that may be used by the safety component and/or to the measurement component that can further be adapted to be used as a monitoring signal by the training machine based on the position over time and/or by the derivate(s) of the position or the velocity over time and thus can represent a value of the velocity, the acceleration and/or the jerk.


The training machine may also be configured to sense the movement of the resistance component. That is, instead of the sensor sensing the movement of the trainee at the lever position, additionally or alternatively, the movement data may be detected at the resistance component. Set this may be a motor, the one or more sensor(s) may directly receive their values from the output of the resistance unit. Further, both data sources, the source at the lever side and at the resistance component side, may be used to compare consistency between the motion that is delivered by the sensor at the resistance component and the motion-sensor at the lever position where the trainee applies his/her exercise(s).


The sensor(s) may directly or indirectly detect their data. That is, a sensor may be configured to directly detect the data (velocity, acceleration) or derive the data by a differentiating method over time.


The sensor may further be configured to detect rotational data; this may be inevitable at a rotating exit of a motor or a gearbox. It may further be advisable at an arm training machine where rotational motions are exercised. However, a motor can also comprise linear movement(s) and at a leg press or a weight lifting machine, for instance, a linear motion may be detected by a sensor. Hence, the expression “sensor” may interchangeably be assumed for a linear or rotational sensor or even a combination of both. This means, for instance, the rotational output from a resistance component may need a rotational sensor, while the lever at a leg press machine may need a sensor that detects linear motion. The sensors in this case may deliver values that may not directly be comparable. The training machine however may comprise a definite relation of the one value to the other value that can then be compared in a computational unit. Such a computational unit may be part of the measuring component, of the safety component or both. Also, this computation can also be performed in the control for controlling the resistance component.


The detected values of the sensor may alternatively or additionally comprise directly derived values but also be calculated by a differentiating algorithm from a distance over time, a velocity or an acceleration.


The safety component may be configured to analyze actual value(s) of velocity or acceleration against respective target values. Such a vis-à-vis of data may be analyzed by the safety device and taken as a basis for a decision whether the data are within a certain range of allowance or whether a safety measure must be activated.


The values for the training machine delivered by the actual data may again be directly received from the appropriate sensor. But it is also possible that a differentiation must be carried out to get derived data like velocity from the position over time, acceleration from the velocity or jerk from the acceleration.


The training machine may receive their actual value(s) directly by a sensor that is adapted to deliver the data without calculations or the data may be derived by a differentiating calculation from data like the position over time, the velocity or the acceleration.


The actual value(s) may be based on positional data over time or on speed data. Further, if appropriate, also acceleration data can be taken as a basis for further use in the safety component or in the measurement component.


The target values may be trained by a sequence of raw data that may be fed into an artificial intelligence algorithm that is adapted to carry out machine learning process(es). The algorithm may be stored in the safety component, the measurement component or in an external device. This device may be located locally at the site of the training machine but also be situated remotely in an external computer or a cloud solution.


The safety component or the measurement component may analyze one or more actual value(s) of velocity, acceleration and/or jerk and bring them into relation to vis-à-vis target data that may be directly stored or have been acquired by a machine learning and/or artificial intelligence method. The values may again be derived directly from the sensor or be calculated by differentiating the function of detected data.


Not just the actual values, but also the target values may have been directly derived or been calculated by mathematical calculations like differentiation over time, the first, the second or the third derivation of the velocity or the acceleration.


The safety component may be configured to initiate measures like the deactivation of the resistance component as a whole or in part. This may be activated in case if measured versus target value(s) differ farther than a certain band of tolerance. Other reasons for such a deactivation of the resistance component may be initiated externally, for instance by a supervisor, the owner or the manager of the training machine. Further, if the trainee exceeds the scope of the training session that he/she is entitled to exercise, a shutdown of the training machine may be needed.


A shutdown or deactivation of the resistance component may not just be a “powerless” status of the resistance component. Also, a slight back-motion from the last motion may be carried out to release the trainee from a possibly painful or uncomfortable position. Also, a freezing status of the resistance component may be selected by, for instance, short-circuiting the motor.


The actual values at or around the change situation where the concentric movement into the eccentric movement is accomplished may be analyzed. This is a velocity where risk situations are likely if the training machine is not adjusted properly. This is a velocity where risk situations are likely if the training machine is not adjusted properly to the trainee's body geometry prior to the training. However, risk may be independent from specific risk points but occur at any location and are dependent from the change of velocity and not necessarily at the change of the direction of the motion.


The actual value(s) at and/or around the change of concentric and eccentric movement can be analyzed and a deactivation of the resistance component may be triggered when the actual value(s) of the velocity is/are lower in absolute terms than the range of target value(s) of the velocity.


The safety component may be configured to force the resistance component into a safe-status like one of an idle status, a powerless status, an end position or a blocked position. Further, a slight back-movement of the last vectorially interpreted motion may be initiated, that is, the resistance component may reverse the last motion that has occurred shortly before the reason for a safe-status has been detected.


Further to the initiating of a safe-status, a signal may be indicated. An alarm may be sounded or a flashlight may alert supervising personnel that a safe-status has been initiated. Such a signal may also be silent and be forwarded wirelessly or per wired communication means. Also, a protocol of the Training sequence up to the initiation of the safe-status may be stored onto a local, a central or a cloud server.


The safety component may be a part of the control that controls the motor of the resistance component.


The exemplified training machines may be addressed as one or a combination of leg training machines, arm training machines, strength training machines, rehabilitation machines or similar configurations. Many other training machines are also addressed by the present invention.


A method is disclosed of controlling a training machine that can comprise the steps of providing a resistance during training by a resistance component. It can control a resistance component during training. A control component may incorporate or communicate with a safety component that may monitor the training machine on the basis of velocity or any derivative of the velocity of a training lever or by the resistance component. While the resistance component may be a motor, or a motor equipped with a gear, the velocity may be either taken from the motor directly or after a gear at the entry to a lever arrangement.


Data that is delivered to the control component, to the safety component, a measurement component or any combination of it may be the velocity value or a derivative of the velocity to result in an acceleration and/or a jerk.


The safety component may comprise a differentiator that determines a first derivative of the velocity over time which is the acceleration. A second derivative of the velocity may be the jerk that may occur during training. The safety component may communicate and/or control the control component. The control may be the motor control or the control of the resistance component.


Further, a separate or integrated measurement component can provide the safety component with a measuring data. Such measuring data may comprise velocity, acceleration or jerk on the basis of distance that is travelled by the lever. Other than the lever, also the velocity of rotation, the acceleration of rotation or a jerk at the exit of the resistance component may be fed into the measuring and/or safety component. The feeding of both data, the motion of the lever and the motion of the resistance component may further improve the performance of a training machine because any inconsistency between the data that should be related to each other would point to a possible safety risk.


It may be assumed that both may be comprised, linear or rotational velocity or its derivatives.


The method further may comprise the providing of a training mechanism that enables the user to train certain parts of the body, such as muscles, groups of muscles, joints, groups of joints or any combination thereof. Other body portions may be trained, either for enhancing strength, movability, range of movability, improvement of speeds of movement and so forth.


The method may comprise the resistance component driving the training mechanism.


The method can further comprise a sensor, a group of sensors or various sensors determining various values. Such values can be the velocity of the lever or handle. Further, the velocity at the exit of the resistance component or at a gear can be determined by a sensor. The sensor can further be configured to sense or determine an acceleration value or acceleration values at different locations of the training machine.


The value(s) of the sensor(s) may be delivered to the control component, to the safety component, a measurement component or a combination thereof.


The values that can be derived by the sensor(s) may be the velocity at the specific location, an acceleration or the jerk. A position value may additionally be supplied to detect an end point or a point that are mechanically or electrically limiting the range of the training machine.


The values that are derived from the sensor may be compared to target value(s) that have previously been stored in a computer storage. The values, both the measured values and the target values may be fed into the control component and/or to the safety component. The comparison of these data may indicate whether the range of safe values is determined. Should the range of safe values be extended, the range of safe values be left, the safety component and/or the control component may execute whatever measures that may be desired. This could be a shutdown of the training machine, the reduction of resistance force, initiating a signal or much more.


As a further step the safety component may analyze one or more actual value(s) of velocity, acceleration and/or jerk and compare the respective measured value vis-à-vis one or more respective target value(s) and vis-à-vis one or more respective target value(s).


It may be that a motor is provided to perform the function(s) of the resistance component. A motor may be controlled by a motor control and the velocity is received from the motor control. Further, a target model may represent the relevant data for comparison with the measured data. The motor may further be controlled from a model that may represent the properties of the resistance component and/or of the path of the training machine.


The measured data, like velocity, acceleration and/or jerk may be fed into a machine learning algorithm that may be housed in a local computing device, a centralized computer and/or in a cloud computer system. The machine learning algorithm may be configured to train significant data and detect data that may be found beyond limits that are determined by earlier training sequences.


The method further may provide signal data to the control component, the safety component or to the measurement component. This may be independent from detected incompatibility between acquired data.


The method may further comprise the determination of velocity, acceleration and/or jerk during training by the safety component. The safety component may further communicate with the control.


Further, the safety component may comprise a measurement component that can be integrated into the safety component or can be located separately and may be in communication with the safety component. The measurement component may be configured to determine velocity, acceleration and/or jerk values. These values may be derived from the path of the lever and/or from the resistance component.


Further, the method may comprise the determination of rotational velocity, acceleration or jerk. This can even be determined at different locations within the training machine, for instance at the path of the training lever and at the resistance component.


It may further be that differentiated data may be comprised that can be acquired from differentiation of one or more sensor(s) that determine(s) at least one of velocity, acceleration or jerk.


A training mechanism may be comprised by the training machine. The training mechanism may be configured to enable a user to train his/her body. Training the body may comprise training of the entire body or parts of it. Such partial training can be directed to the training of muscles or groups of muscles. Further, also joints or groups of joints may be trained. Any combination of the aforementioned training exercises may be comprised.


The training method may be comprised wherein the training mechanism is integrated into the method. However, also a method may be comprised where the method is connected to a training mechanism and further, also the method driving the training mechanism may be comprised.


The method may comprise a gear, or more generally, a transmission arrangement may be arranged between the resistance component and the training mechanism.


The method may further comprise a sensor that is configured to deliver a signal. This signal may be used by the safety component and/or by the measurement component. The monitoring of the method on the basis of at least one of velocity, acceleration and/or jerk may be performed.


The method may further comprise that a sensor is configured to sense or determine the movement of the resistance component, that may comprise a linear or a rotational motion.


It may further be noted that the sensed data may by the method further be differentiated to receive data that are comparable to target data or may be relevant to determine whether a control signal may be transferred to the control. At the location of the resistance component, other than a linear component may sense values but also rotational values may be determined.


The motor may be one of an electric, a pneumatic or a hydraulic device that may represent the resistance component. The sensor may provide values of its rotational velocity, acceleration and/or jerk. If a linear motor is comprised, the appropriate linear data may be determined.


A further sensor may provide data that can be derived from the training mechanism, wherein the sensed values further to the directly derived values, may be values that can be differentiated over time.


The safety component may be configured to analyze one or more actual values of velocity, acceleration and/or jerk. Further, respective target values may be encountered into the analysis and interpreted appropriately. The values may be directly derived from the respective sensor or be differentiated values over time. This may apply to either or both, the determined values and the target values.


The sensor that may determine the actual value(s) may be based on a speed signal that is provided by a rotational speed sensor at the resistance component.


The target value(s) provided to the safety and/or control component may have been trained by a machine learning algorithm on the basis of training data. Such data may have been fed into the target-database by an artificial intelligence algorithm. However, also manually determined values may have been found at the time of earlier training sequences that may be desired for safe operation of a training machine.


The target value(s) and the actual value(s) may be used by the safety component in the form of velocity data, acceleration data and/or jerk data. The comparison of these both data sources may also be derived as a derivative over time of velocity or acceleration data or both. The vis-a-vis of the relating data may result in signaling to the safety component or the control component or both.


All data, the measured values and the target values, may be provided, or have been provided, by a range of values of velocity, acceleration and/or jerk over time.


The safety component and/or the control component may comprise the method to deactivate the resistance component at least in part. The deactivation in part may comprise a significant reduction of forward motion but also a calm back movement. Further, a freezing status may be initiated inhibiting any further motion of the training machine or at least the training component. Further, the deactivation may be understood as a gradual reduction of the resistance to a minimum or zero.


The method may further comprise the analysis of values around or at the change from concentric to eccentric movement and/or vice versa.


The method further may comprise the analysis of the actual value(s) vis-a-vis the target value(s) around the change from eccentric to concentric movement and vice versa. A deactivation signal to the resistance component may be triggered as a result of a vis-a-vis when the actual value(s) of the velocity is/are lower in absolute terms than the range of target value(s) of the velocity.


The method may further comprise the safety component being configured to force the resistance component into a safe-status. The safe-status may be at least one of an idle status, a powerless status, an end position, a blocked position, a significantly reduced velocity of the training component and/or a reverse motion of the training component. A signal may be indicated by the safety component prior and/or during a training sequence. The signal may be at least one of sound, visual, haptic, electronic.


Where the word “position” is referenced, a distance over time is addressed if not specified otherwise explicitly.





BRIEF DESCRIPTION OF THE FIGURES


FIG. 1 provides a schematic of a computing device in accordance with the present invention;



FIG. 2 depicts an example of a resistance component integrated in a training machine. in accordance with the present invention;



FIG. 3 shows an example of an arrangement where risk points are exemplified in accordance with the present invention;



FIG. 4a conceptual value measurements of a single isokinetic training repetition;



FIG. 4b depicts three conceptual value measurements for a single isokinetic training repetition in the case of a hypothetical injury



FIG. 4c depicts a possible development of the repetition shown in FIG. 4b, assuming that there is a velocity-based safety component.



FIG. 5 exemplifies a relation between position, velocity, acceleration and jerk; and



FIG. 6 depicts an example of a velocity at a velocity turn point.





EMBODIMENTS

While the disclosure has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and non-restrictive; the disclosure is thus not limited to the disclosed embodiments. Variations to the disclosed embodiments can be understood and effected by those skilled in the art and practicing the claimed disclosure, from a study of the drawings, the disclosure, and the appended claims.


As used herein, including in the claims, singular forms of terms are to be construed as also including the plural form and vice versa, unless the context indicates otherwise. Thus, it should be noted that as used herein, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.


The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to fulfill aspects of the present invention. The present technology is also understood to encompass the exact terms, features, numerical values or ranges etc., if in here a relative term, such as “about”, “substantially”, “ca.”, “generally”, “at least”, “at the most” or “approximately” is used in this specification, such a term should also be construed to also include the exact term. That is, e.g., “substantially straight” should be construed to also include “(exactly) straight”. In other words, “about 3” shall also comprise “3” or “substantially perpendicular” shall also comprise “perpendicular”. Any reference numerals in the claims should not be considered as limiting the scope.


In the claims, the terms “comprises/comprising”, “including”, “having”, and “contain” and their variations should be understood as meaning “including but not limited to”, and are not intended to exclude other components. Furthermore, although individually listed, a plurality of means, elements or method steps may be implemented. Additionally, although individual features may be included in different claims, these may possibly advantageously be combined, and the inclusion in different claims does not imply that a combination of features is not feasible and/or advantageous. In addition, singular references do not exclude a plurality.


Whenever steps were recited in the above or also in the appended claims, it should be noted that the order in which the steps are recited in this text may be the preferred order, but it may not be mandatory to carry out the steps in the recited order. That is, unless otherwise specified or unless clear to the skilled person, the order in which steps are recited may not be mandatory. That is, when the present document states, e.g., that a method comprises steps (A) and (B), this does not necessarily mean that step (A) precedes step (B), but it is also possible that step (A) is performed (at least partly) simultaneously with step (B) or that step (B) precedes step (A). Furthermore, when a step (X) is said to precede another step (Z), this does not imply that there is no step between steps (X) and (Z). That is, step (X) preceding step (Z) encompasses the situation that step (X) is performed directly before step (Z), but also the situation that (X) is performed before one or more steps (Y1), . . . , followed by step (Z). Corresponding considerations apply when terms like “after” or “before” are used.


It will be appreciated that variations to the foregoing embodiments of the invention can be made while still falling within the scope of the invention can be made while still falling within scope of the invention. Features disclosed in the specification, unless stated otherwise, can be replaced by alternative features serving the same, equivalent or similar purpose. Thus, unless stated otherwise, each feature disclosed represents one example of a generic series of equivalent or similar features.


Use of exemplary language, such as “for instance”, “such as”, “for example” and the like, is merely intended to better illustrate the invention and does not indicate a limitation on the scope of the invention unless so claimed. Any steps described in the specification may be performed in any order or simultaneously, unless the context clearly indicates otherwise.


All of the features and/or steps disclosed in the specification can be combined in any combination, except for combinations where at least some of the features and/or steps are mutually exclusive. In particular, preferred features of the invention are applicable to all aspects of the invention and may be used in any combination.


Reference numbers and letters appearing between parentheses in the claims, identifying features described in the embodiments and illustrated in the accompanying drawings, are provided as an aid to the reader as an exemplification of the matter claimed. The inclusion of such reference numbers and letters is not to be interpreted as placing any limitations on the scope of the claims.


The term “resistance” is intended to comprise a counter-force or counter-torque against movements of a user in a fitness machine. The movements of a user can be concentric or eccentric. The resistance component of the machine is usually providing the counter-force or counter-torque during the concentric and/or the eccentric movements of a user.


The term “derivative” is intended to embrace a differentiation or derivation in term of time. Thus, the first derivative of velocity is acceleration and the second derivative is jerk. The verb “derive” stands for a differentiation with respect to time. The word “derivative” stands for the result of one or more deriving processes.


The abbreviation ROM stands for “Range of Motion” or “Range of Movement”. It embraces the range of the movement from a start point, to an extremum and back to the position of a start point.


The word “lever” is meant to comprise a handle or, more generally, an actuator that is in tactile communication with a trainee. The actuator in this document is addressed as a linkage between a resistance component and the trainee.


The words velocity and speed shall be understood to represent the same physical value. Velocity is specified as the velocity of a lever or handle. Velocity can be linear or rotational, or with respect to a movement, integrated over time, on a path within a coordinate space.


The present invention also refers to a method. The respective method is intended to perform steps in line with the capabilities of the machine and all its features described before and below and as claimed.


Below is a list of training machine assembly embodiments. Those will be indicated with a letter “T”. Whenever such embodiments are referred to, this will be done by referring to “T” embodiments.

  • T1. Training machine comprising:
    • a resistance component (20) configured to provide at least a resistance during training;
    • a control (30) configured to control the resistance component (20) during training;
    • a safety component (40) configured to monitor the training machine on the basis of velocity and/or any derivative thereof.
  • T2. Training machine according to the preceding embodiment wherein the derivative of velocity comprises acceleration and/or jerk.
  • T3. Training machine according to any of the relevant preceding embodiments wherein the resistance component (20) comprises a motor.
  • T4. Training machine according to any of the relevant preceding embodiments, wherein the resistance component (20) comprises an electric motor.
  • T5. Training machine according to any of the preceding embodiments, wherein the motor is controlled by a motor control and the velocity is received from the motor control.
  • T6. Training machine according to any of the preceding embodiments, wherein the motor is controlled by a motor control and the velocity is received from a control model of the motor control.
  • T7. Training machine according to any of the relevant preceding embodiments wherein the safety component (40) is configured to determine velocity, acceleration and/or jerk during training and to communicate with the control (30).
  • T8. Training machine according to any of the relevant preceding embodiments wherein the safety component (40) is configured to determine velocity, acceleration and/or jerk.
  • T9. Training machine according to any of the relevant preceding embodiments wherein the safety component (40) comprises a measurement component that is configured to measure velocity along a path, acceleration along a path and/or jerk along a path.
  • T10. Training machine according to any of the relevant preceding embodiments wherein the safety component (40) comprises a measurement component that is configured to measure and/or to monitor rotational velocity, acceleration and/or jerk.
  • T11. Training machine according to any of the relevant preceding embodiments wherein the safety component (40) comprises a derivation component for deriving velocity, acceleration and/or jerk on the basis of distance.
  • T12. Training machine according to any of the relevant preceding embodiments further comprising a training mechanism (200).
  • T13. Training machine according to any of the relevant preceding embodiments wherein the training mechanism (200) is configured to enable a user to train certain parts of the body, such as muscles, groups of muscles, joints, groups of joints or combinations thereof.
  • T14. Training machine according to any of the relevant preceding embodiments wherein the training mechanism (200) is integrated into the training machine.
  • T15. Training machine according to any of the relevant preceding embodiments wherein the training mechanism (200) is connected to the training machine.
  • T16. Training machine according to any of the relevant preceding embodiments wherein the resistance component (20) is configured to drive the training mechanism (200).
  • T17. Training machine according to any of the relevant preceding embodiments wherein a further gear component is arranged between the resistance component (20) and the training mechanism (200).
  • T18. Training mechanism according to embodiment T17, wherein the gear is a transmission component.
  • T19. Training machine according to any of the relevant preceding embodiments further comprising a sensor that is configured to deliver a signal that is configured to be used by the safety component (40) to monitor the training machine on the basis of at least one of velocity, acceleration and/or jerk.
  • T20. Training machine according to any of the relevant preceding embodiments wherein the sensor is configured to sense the movement of the resistance component (20).
  • T21. Training machine according to any of the relevant preceding embodiments wherein the sensor is configured to sense the movement of the resistance component (20) over time.
  • T22. Training machine according to any of the relevant preceding embodiments wherein the sensor is a rotational sensor that is sensing the rotation of the resistance component (20).
  • T23. Training machine according to any of the relevant preceding embodiments wherein the sensor is a rotational sensor that is sensing the rotation of a motor of the resistance component (20).
  • T24. Training machine according to any of the relevant preceding embodiments wherein the sensor is configured to sense the movement of a training mechanism (200).
  • T25. Training machine according to any of the relevant preceding embodiments wherein the sensor is configured to sense the movement of a training mechanism (200) over time.
  • T26. Training machine according to any of the relevant preceding embodiments wherein the safety component (40) is configured to analyze one or more actual value(s) of velocity, acceleration and/or jerk vis-à-vis one or more respective target value(s).
  • T27. Training machine according to any of the relevant preceding embodiments wherein the safety component (40) is configured to analyze one or more actual value(s) of velocity, acceleration and/or jerk vis-à-vis one or more respective target value(s) over time.
  • T28. Training machine according to any of the relevant preceding embodiments wherein the actual value(s) are delivered by a sensor.
  • T29. Training machine according to any of the relevant preceding embodiments wherein the actual value(s) are based on a speed signal.
  • T30. Training machine according to any of the relevant preceding embodiments wherein the actual value(s) are based on a speed signal provided by a sensor.
  • T31. Training machine according to any of the relevant preceding embodiments wherein the actual value(s) are based on a speed signal provided by a sensor that is sensing rotational speed of the resistance component (20).
  • T32. Training machine according to any of the relevant preceding embodiments wherein the target value(s) are trained by a machine learning algorithm on the basis of training data.
  • T33. Training machine according to any of the relevant preceding embodiments wherein the safety component (40) is configured to analyze one or more actual value(s) of velocity, acceleration and/or jerk vis-à-vis one or more respective target value(s) over time.
  • T34. Training machine according to any of the relevant preceding embodiments wherein the target values are provided by a range of respective values of velocity, acceleration and/or jerk over time.
  • T35. Training machine according to any of the relevant preceding embodiments wherein the safety component (40) is configured to adjust the resistance component (20) at least in part and/or complete.
  • T36. Training machine according to any of the relevant preceding embodiments wherein the safety component (40) is configured to deactivate the resistance component (20) at least in part and/or complete upon in case the actual value(s) is/are out of the target value(s) or a range of (a) target value(s).
  • T37. Training machine according to any of the relevant preceding embodiments wherein the safety component (40) is configured to deactivate the resistance component (20) by a gradual reduction of the resistance to a minimum or zero.
  • T38. Training machine according to any of the relevant preceding embodiments wherein the safety component (40) is configured to deactivate the resistance component (20) by a gradual reduction of the resistance to a minimum or zero depending on the position of the machine and/or an activator configured to be activated by a user.
  • T39. Training machine according to any of the relevant preceding embodiments wherein the actual value(s) at and/or around the change of concentric and eccentric movement are analyzed.
  • T40. Training machine according to any of the relevant preceding embodiments wherein the actual value(s) at and/or around the change of concentric and eccentric movement are analyzed and a deactivation of the resistance component (20) at least in part and/or complete is triggered when the actual value(s) of the velocity is/are lower in absolute terms than the range of target value(s) of the velocity.
  • T41. Training machine according to any of the relevant preceding embodiments wherein the safety component (40) is configured to force the resistance component (20) into one of an idle status, a powerless status, an end position or a blocked position.
  • T42. Training machine according to any of the relevant preceding embodiments wherein the safety component (40) is configured to indicate a signal.
  • T43. Training machine according to any of the relevant preceding embodiments wherein the signal is at least one of sounded, visual or vibrational.
  • T44. Training machine according to any of the relevant preceding embodiments wherein the safety component (40) is integrated with the control (30).
  • T45. Training machine according to any of the relevant preceding embodiments wherein the training machine is one or a combination of:
    • i. a leg training machine;
    • ii. an arm training machine;
    • iii. a strength training machine;
    • iv. a rehabilitation machine;
    • v. a torso rotation machine;
    • vi. a torso training machine; and
    • vii. a gluteus training machine.


Below, system embodiments will be discussed. These embodiments are abbreviated by the letter “S” followed by a number. Whenever reference is herein made to “system embodiments”, these embodiments are meant.

  • S1. System for training comprising any training machine according to any of the relevant preceding embodiments further comprising a data storage for storing values of users during training.
  • S2. System according to the preceding system embodiment wherein the data storage is arranged remotely.
  • S3. System according to any of the relevant preceding system embodiments wherein the data storage is arranged on a server in the cloud.
  • S4. System according to any of the relevant preceding system embodiments further comprising a machine learning component for optimizing the range of values on the basis of the user values.
  • S5. System according to any of the relevant preceding system embodiments comprising a machine learning component for optimizing the range of values on the basis of user values and labels assigned to stored data through a manual or semi-automated process.


Below, method embodiments will be discussed. These embodiments are abbreviated by the letter “M” followed by a number. Whenever reference is herein made to “method embodiments”, these embodiments are meant.

  • M1. Method of controlling a training machine comprising the steps of:
    • a. providing a resistance during training by a resistance component;
    • b. controlling a resistance component (20) during training by a control component; and
    • c. monitoring the training machine on the basis of at least one of, velocity and/or any derivative thereof by a safety component.
  • M2. Method according to the preceding method embodiment with the further steps of deriving the velocity value to result in an acceleration and/or a jerk.
  • M3. Method according to any of the preceding method embodiments with the further step of providing a motor control that receives measured velocity data and controls the resistance component, the resistance component being a motor.
  • M4. Method according to any of the preceding method embodiments with the further step of providing the safety component with a differentiator that determines at least a first derivative of the velocity and/or a second derivative of the velocity during training and communicating with the motor control of the resistance module.
  • M5. Method according to any of the preceding method embodiments with the further step of providing the safety component with a measuring component for measuring velocity and/or acceleration and/or jerk on basis of distance.
  • M6. Method according to method embodiment M1 with the further step of measuring and/or monitoring rotational velocity.
  • M7. Method according to any of the preceding method embodiments with the further step of providing a training mechanism, wherein the training mechanism enables a user to train certain parts of the body, such as muscles, groups of muscles, joints, groups of joints or combinations thereof.
  • M8. Method according to any of the preceding method embodiments with the further step of the resistance component driving a training mechanism.
  • M9. Method according to any of the preceding method embodiments with the further step of a sensor monitoring at least one value being at least one of a velocity or an acceleration of the training mechanism and delivering the monitored at least one value to the safety component.
  • M10. Method according to any of the preceding method embodiments with the further step of a sensor monitoring at least one value being at least one of a velocity or an acceleration of the resistance component and delivering the monitored at least one value to the safety component.
  • M11. Method according to any of the preceding method embodiments with the further step of the safety component analyzing one or more actual value(s) of velocity, acceleration and/or jerk vis-à-vis one or more respective target value(s) and vis-à-vis one or more respective target value(s).
  • M12. Method according to any of the preceding method embodiments, wherein the resistance component is a motor that is controlled by a motor control and the velocity is received from the motor control.
  • M13. Method according to any of the preceding method embodiments, wherein the resistance component is a motor that is controlled by a motor control and the velocity is received from control model of the motor control.
  • M14. Method according to any of the preceding method embodiments with the further step of providing the target values and the measured values are trained into a machine learning algorithm.
  • M15. Method according to the preceding method embodiment, wherein the machine learning algorithm provides a control signal to the safety component.
  • M16. Method according to any of the relevant preceding method embodiments wherein the safety component (40) is configured to determine velocity, acceleration and/or jerk during training and to communicate with the control (30).
  • M17. Method according to any of the relevant preceding method embodiments wherein the safety component (40) is configured to determine velocity, acceleration and/or jerk.
  • M18. Method according to any of the relevant preceding method embodiments wherein the safety component (40) comprises a measurement component that is configured to measure velocity, acceleration and/or jerk.
  • M19. Method according to any of the relevant preceding method embodiments wherein the safety component (40) comprises a measurement component that is configured to measure and/or to monitor rotational velocity.
  • M20. Method according to any of the relevant preceding method embodiments wherein the safety component (40) comprises a derivation component for deriving velocity, acceleration and/or jerk on the basis of distance.
  • M21. Method according to any of the relevant preceding method embodiments further comprising a training mechanism.
  • M22. Method according to any of the relevant preceding method embodiments wherein the training mechanism is configured to enable a user to train certain parts of the body, such as muscles, groups of muscles, joints, groups of joints or combinations thereof.
  • M23. Method according to any of the relevant preceding method embodiments wherein the training mechanism is integrated into the method.
  • M24. Method according to any of the relevant preceding method embodiments wherein the training mechanism is connected to the method.
  • M25. Method according to any of the relevant preceding method embodiments wherein the resistance component (20) is configured to drive the training mechanism.
  • M26. Method according to any of the relevant preceding method embodiments wherein a further gear component is arranged between the resistance component (20) and the training mechanism.
  • M27. Method according to any of the relevant preceding method embodiments further comprising a sensor that is configured to deliver a signal that is configured to be used by the safety component (40) to monitor the method on the basis of at least one of velocity, acceleration and/or jerk.
  • M28. Method according to any of the relevant preceding method embodiments wherein the sensor is configured to sense the movement of the resistance component (20).
  • M29. Method according to any of the relevant preceding method embodiments wherein the sensor is configured to sense the movement of the resistance component (20) over time.
  • M30. Method according to any of the relevant preceding method embodiments wherein the sensor is a rotational sensor that is sensing the rotation of the resistance component (20).
  • M31. Method according to any of the relevant preceding method embodiments wherein the sensor is a rotational sensor that is sensing the rotation of a motor of the resistance component (20).
  • M32. Method according to any of the relevant preceding embodiments wherein the sensor is configured to sense the movement of a training mechanism (200).
  • M33. Method according to any of the relevant preceding method embodiments wherein the sensor is configured to sense the movement of a training mechanism (200) over time.
  • M34. Method according to any of the relevant preceding method embodiments wherein the safety component (40) is configured to analyze one or more actual value(s) of velocity, acceleration and/or jerk vis-à-vis one or more respective target value(s).
  • M35. Method according to any of the relevant preceding method embodiments wherein the safety component (40) is configured to analyze one or more actual value(s) of velocity, acceleration and/or jerk vis-à-vis one or more respective target value(s) over time.
  • M36. Method according to any of the relevant preceding method embodiments wherein the actual value(s) are delivered by a sensor.
  • M37. Method according to any of the relevant preceding method embodiments wherein the actual value(s) are based on a speed signal.
  • M38. Method according to any of the relevant preceding method embodiments wherein the actual value(s) are based on a speed signal provided by a sensor.
  • M39. Method according to any of the relevant preceding method embodiments wherein the actual value(s) are based on a speed signal provided by a sensor that is sensing rotational speed of the resistance component (20).
  • M40. Method according to any of the relevant preceding method embodiments wherein the target value(s) are trained by a machine learning algorithm on the basis of training data.
  • M41. Method according to any of the relevant preceding method embodiments wherein the safety component (40) is configured to analyze one or more actual value(s) of velocity, acceleration and/or jerk vis-à-vis one or more respective target value(s) over time.
  • M42. Method according to any of the relevant preceding method embodiments wherein the target values are provided by a range of respective values of velocity, acceleration and/or jerk over time.
  • M43. Method according to any of the relevant preceding method embodiments wherein the safety component (40) is configured to deactivate the resistance component (20) at least in part and/or complete.
  • M44. Method according to any of the relevant preceding method embodiments wherein the safety component (40) is configured to deactivate the resistance component (20) at least in part and/or complete upon in case the actual value(s) is/are out of the target value(s).
  • M45. Method according to any of the relevant preceding method embodiments wherein the safety component (40) is configured to deactivate the resistance component (20) by a gradual reduction of the resistance to a minimum or zero.
  • M46. Method according to any of the relevant preceding method embodiments wherein the safety component (40) is configured to deactivate the resistance component (20) by a gradual reduction of the resistance to a minimum or zero depending on the position of the machine.
  • M47. Method according to any of the relevant preceding method embodiments wherein the actual value(s) at and/or around the change of concentric and eccentric movement are analyzed.
  • M48. Method according to any of the relevant preceding method embodiments wherein the actual value(s) at and/or around the change of concentric and eccentric movement are analyzed and a deactivation of the resistance component (20) is triggered when the actual value(s) of the velocity is/are lower in absolute terms than the range of target value(s) of the velocity.
  • M49. Method according to any of the relevant preceding method embodiments wherein the safety component (40) is configured to force the resistance component (20 into one of an idle status, a powerless status, an end position or a blocked position.
  • M50. Method according to any of the relevant preceding method embodiments wherein the safety component (40) is configured to indicate a signal.
  • M51. Method according to any of the relevant preceding method embodiments wherein the signal is at least one of sounded, visual or vibrational.


Below, program embodiments will be discussed. These embodiments are abbreviated by the letter “P” followed by a number. Whenever reference is herein made to “use embodiments”, these embodiments are meant.

  • P1. A computer program product comprising instructions, which, when the program is executed on a data processing system causes the method steps according to any method embodiment.


Below, use embodiments will be discussed. These embodiments are abbreviated by the letter “U” followed by a number. Whenever reference is herein made to “use embodiments”, these embodiments are meant.

  • U1. Use of a machine according to any of the relevant preceding system embodiments for monitoring training.
  • U2. Use of a system according to any of the relevant preceding system embodiments for optimizing training on training machines.
  • U3. Use of a method according to any of the preceding method embodiments for monitoring training.


Below, a computer related product embodiment is discussed. This embodiment is abbreviated by the letter “C” followed by a number. Whenever reference is herein made to “computer related product embodiment”, this embodiment is meant.

  • C1. A computer related product with a program that is configured for carrying out the method according to any of the preceding method embodiments.


DESCRIPTION OF THE FIGURES

It is noted that not all the drawings carry all the reference signs. Instead, in some of the drawings, some of the reference signs have been omitted for sake of brevity and simplicity of illustration. Embodiments of the present invention will now be described with reference to the accompanying drawings.



FIG. 1 provides a schematic of a computing device 100. The computing device 100 may comprise a computing unit 35, a first data storage unit 30A, a second data storage unit 30B and a third data storage unit 30C.


The computing device 100 can be a single computing device or an assembly of computing devices. The computing device 100 can be locally arranged or remotely, such as a cloud solution.


On the different data storage units 30 the different data can be stored, such as the genetic data on the first data storage 30A, the time stamped data and/or event code data and/or phenotypic data on the second data storage 30B and privacy sensitive data, such as the connection of the before-mentioned data to an individual, on the thirds data storage 30C.


Additional data storage can be also provided and/or the ones mentioned before can be combined at least in part. Another data storage (not shown) can comprise data specifying the composition or pharmaceutically active composition and/or medication data, such as pharmaceutical activities, side effects, interactions between the different components etc. This data can also be provided on one or more of the before-mentioned data storages.


The computing unit 35 can access the first data storage unit 30A, the second data storage unit 30B and the third data storage unit 30C through the internal communication channel 160, which can comprise a bus connection 160.


The computing unit 30 may be single processor or a plurality of processors, and may be, but not limited to, a CPU (central processing unit), GPU (graphical processing unit), DSP (digital signal processor), APU (accelerator processing unit), ASIC (application-specific integrated circuit), ASIP (application-specific instruction-set processor) or FPGA (field programmable gate array). The first data storage unit 30A may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM).


The second data storage unit 30B may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM). The third data storage unit 30C may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM).


It should be understood that generally, the first data storage unit 30A (also referred to as encryption key storage unit 30A), the second data storage unit 30B (also referred to as data share storage unit 30B), and the third data storage unit 30C (also referred to as decryption key storage unit 30C) can also be part of the same memory. That is, only one general data storage unit 30 per device may be provided, which may be configured to store the respective encryption key (such that the section of the data storage unit 30 storing the encryption key may be the encryption key storage unit 30A), the respective data element share (such that the section of the data storage unit 30 storing the data element share may be the data share storage unit 30B), and the respective decryption key (such that the section of the data storage unit 30 storing the decryption key may be the decryption key storage unit 30A).


In some embodiments, the third data storage unit 30C can be a secure memory device 30C, such as, a self-encrypted memory, hardware-based full disk encryption memory and the like which can automatically encrypt all of the stored data. The data can be decrypted from the memory component only upon successful authentication of the party requiring to access the third data storage unit 30C, wherein the party can be a user, computing device, processing unit and the like. In some embodiments, the third data storage unit 30C can only be connected to the computing unit 35 and the computing unit 35 can be configured to never output the data received from the third data storage unit 30C. This can ensure a secure storing and handling of the encryption key (i.e. private key) stored in the third data storage unit 30C.


In some embodiments, the second data storage unit 30B may not be provided but instead the computing device 100 can be configured to receive a corresponding encrypted share from the database 60. In some embodiments, the computing device 100 may comprise the second data storage unit 30B and can be configured to receive a corresponding encrypted share from the database 60.


The computing device 100 may comprise a further memory component 140 which may be singular or plural, and may be, but not limited to, a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM). The memory component 140 may also be connected with the other components of the computing device 100 (such as the computing component 35) through the internal communication channel 160.


Further the computing device 100 may comprise an external communication component 130. The external communication component 130 can be configured to facilitate sending and/or receiving data to/from an external device (e.g. backup device 10, recovery device 20, database 60). The external communication component 130 may comprise an antenna (e.g. WIFI antenna, NFC antenna, 2G/3G/4G/5G antenna and the like), USB port/plug, LAN port/plug, contact pads offering electrical connectivity and the like. The external communication component 130 can send and/or receive data based on a communication protocol which can comprise instructions for sending and/or receiving data. Said instructions can be stored in the memory component 140 and can be executed by the computing unit 35 and/or external communication component 130. The external communication component 130 can be connected to the internal communication component 160. Thus, data received by the external communication component 130 can be provided to the memory component 140, computing unit 35, first data storage unit 30A and/or second data storage unit 30B and/or third data storage unit 30C. Similarly, data stored on the memory component 140, first data storage unit 30A and/or second data storage unit 30B and/or third data storage unit 30C and/or data generated by the commuting unit 35 can be provided to the external communication component 130 for being transmitted to an external device.


In addition, the computing device 100 may comprise an input user interface 110 which can allow the user of the computing device 100 to provide at least one input (e.g. instruction) to the computing device 100. For example, the input user interface 110 may comprise a button, keyboard, trackpad, mouse, touchscreen, joystick and the like.


Additionally, still, the computing device 100 may comprise an output user interface 120 which can allow the computing device 100 to provide indications to the user. For example, the output user interface 110 may be a LED, a display, a speaker and the like.


The output and the input user interface 100 may also be connected through the internal communication component 160 with the internal component of the device 100.


The processor may be singular or plural, and may be, but not limited to, a CPU, GPU, DSP, APU, or FPGA. The memory may be singular or plural, and may be, but not limited to, being volatile or non-volatile, such an SDRAM, DRAM, SRAM, Flash Memory, MRAM, F-RAM, or P-RAM.


The data processing device can comprise means of data processing, such as, processor units, hardware accelerators and/or microcontrollers. The data processing device 20 can comprise memory components, such as, main memory (e.g. RAM), cache memory (e.g. SRAM) and/or secondary memory (e.g. HDD, SDD). The data processing device can comprise busses configured to facilitate data exchange between components of the data processing device, such as, the communication between the memory components and the processing components. The data processing device can comprise network interface cards that can be configured to connect the data processing device to a network, such as, to the Internet. The data processing device can comprise user interfaces, such as:

    • output user interface, such as:
      • screens or monitors configured to display visual data (e.g. displaying graphical user interfaces of the questionnaire to the user, training guidance),
      • signal light (e.g. a single light, a combination or a sequence of signal lights, flashlight)
      • speakers configured to communicate audio data (e.g. playing audio data to the user),
      • sound giving devices (e.g. a horn, a bell)
      • tactile signal generators (e.g. buzzer, sounder, supplier of electric tension
      • dangling;
    • input user interface, such as:
      • camera configured to capture visual data (e.g. capturing images and/or videos of the user),
      • microphone configured to capture audio data (e.g. recording audio from the user, detecting ultrasonic signals),
      • myoelectric sensors,
      • capacitive sensors,
      • pressure and/or vibration sensors,
      • keyboard configured to allow the insertion of text and/or other keyboard commands (e.g. allowing the user to enter text data and/or other keyboard commands by having the user type on the keyboard) and/or trackpad, mouse, touchscreen, joystick—configured to facilitate the navigation through different graphical user interfaces of the questionnaire.


The data processing device can be a processing unit configured to carry out instructions of a program. The data processing device can be a system-on-chip comprising processing units, memory components and busses. The data processing device can be a personal computer, a laptop, a pocket computer, a smartphone, a tablet computer. The data processing device can be a server, either local and/or remote. The data processing device can be a processing unit or a system-on-chip that can be interfaced with a personal computer, a laptop, a pocket computer, a smartphone, a tablet computer and/or user interface (such as the upper-mentioned user interfaces).



FIG. 2 depicts a resistance component 20 that is represented as a motor 20 that can reversely be used as a generator 20. The resistance component 20 can either propel a mechanical device, retard a mechanical device or dependent from a control 30 propel or retard a resistant component 20.


The control unit 30 may comprise a power supply and a control that is configured to control the resistance component 20. The control 30 may further comprise a computer 100 from FIG. 1. The control 30 may comprise one or a plurality of input user interfaces and besides controlling the resistance component 20 also control output user interface(s).


The resistance component 20 may be mechanically, hydraulically and/or electrically connected to one or a plurality of lever(s) 200, to a rope arrangement, a pulley arrangement that is in contact with an exercising person. Furthermore, the resistance component 20 may be configured to linear and/or rotational recurring movement.


A frame 220 can comprise all or a number of components needed for carrying out training exercises.



FIG. 3 shows an arrangement where risk points are defined at or around a turn point x1, x2. At this location, a precaution is observed to reduce the counter force of a resistance component 20. The precaution is defined as a limitation of the counterforce that is provided by the resistance component. At return points x1, x2, where a joint 210 can get locked, serious injuries may occur if a training machine keeps on applying a force; this may happen in certain training methods. Such or similar problems can also occur at different points. In isokinetic training machines by definition the machine is designed to maintain a constant velocity of motion. However, at turn points, the isokinetic machine would smoothly reduce velocity, come to the extremum and then smoothly accelerate to the intended speed. This speed is by definition the same as in the prior phase. However, in practice, the concentric and the eccentric phase may comprise different speeds.


At point x0, if the trainee is about to stretch his legs, no special precaution is observed. While moving the legs from x′ to x, the turn point x2 may be assumed to be a safe point where the machine stops and returns in the direction of x to x. The machine will then, keeping the velocity of the mechanic, return with a velocity to move towards the trainee. The machine will then rapidly invert the direction to move towards the trainee with a certain target velocity If the trainee has properly adjusted the machine prior to the first exercise, the knees will not be extended entirely. The proper adjustment is represented by point x2. However, if the adjustment of the machine has not been properly accomplished, like represented with a point at x1, the knees may come into a straight or hyperextended position which may lock them position. If the machine then maintains its velocity of (back-)motion, the trainee may be seriously injured.


Other than a joint lock situation, an overstretching of tendons, ligaments and/or muscles may be prevented by safety means. However, people are different in their physiognomy and also have fundamentally various fitness status. Further, a human problem, self-estimation may be far away from reality. A slim person may adjust the limitations of a training apparatus beyond the muscular conditions of the trainee. In such a case, the machine has too high values preset to avoid injuries.


In a further case, a trainee may be training while still recovering from a bone fracture or rupture of a sinew or a muscle. In this case, even more precautions must be observed by the machine to be careful at specific locations or rather in specific training phases.


Also, a medical- or non-medical practitioner may want to restrict a maximum force to his/her patient. This restriction may not be dependent from fixed locations (like a turning point or, more general, a risk point).


The training settings (risk points, and max. strength) may be incorrect for various reasons: Accidental switching of identification-tokens, an unexperienced trainer who does not follow safety instructions, an attacker who modifies settings which are stored in an IT system, a user who was not in the gym for a very long time and whose fitness level has in the meantime decreased.


Therefore, the predefinition of certain locations where the training apparatus must restrict its counter force, cannot be assumed to be safe for the trainee in all cases.



FIG. 4a shows conceptual value measurements of a single isokinetic training repetition which is performed correctly, which means there are no obvious irregularities. The line “Position” in the first row represents the lever motion over the whole range of motion (ROM). The movement is performed from a start point at position 0 and a turn point at position 1. The movement is performed in two sequential phases, a concentric phase between time points 0.05 and 0.6 and an eccentric phase between time points 0.6 and 0.95. It should be noted that the shown repetition is performed in a cadence where the concentric movement phase is slower (approximately 0.6 time units) than the eccentric movement phase (approximately 0.4 time units).


The line “Velocity” in the second row represents the actual velocity which is the first derivative of the position. The actual velocity changes smoothly, identical to an ideal target velocity. It is shown that there is a larger target velocity for the eccentric movement phase because the backwards movement towards the user overcomes the same range of motion in a shorter time.


The line “Force” in the third row represents a possible resistance or torque development over the time of the repetition. The force change is smooth and does not exceed the user's maximum force (or strength) which is represented by the y axis value of 1.



FIG. 4b shows three conceptual value measurements for a single isokinetic training repetition in the case of a hypothetical injury, caused by a joint lock situation, caused by a training which is performed incorrectly. The scenario assumes an identical repetition cadence as in FIG. 4a. The line “Position” in the first row represents the actual lever movement where the trainee moves in the concentric movement phase from start point at time 0.05 to the turn point at time 0.45. The eccentric movement phase from turn point at time 0.45 back to the start point at time 1.5 follows an irregular timing, ending approximately 0.5 time units later than prescribed by the specified repetition cadence. This can be explained by the occurrence of a joint lock around the turn point at time 0.45, which causes the leg to give way for a short distance (due to a hyperextension allowed by flexible tendons and ligaments) and then blocks the lever between time points 0.8 and 1.2, until the resistance by the leg is removed which causes the lever to move back to the start point between time points 1.2 and 1.5.


The “Actual Velocity” in the second row represents the velocity which corresponds to the “Position” line. The “Target Velocity” in the second row represents a velocity that may be determined by the system after the concentric movement phase is completed, if there was no irregularity. It is shown that there is a discrepancy between the actual velocity and the target velocity which gradually increases between time points 0.4 and 0.8. This is an irregularity which a safety system may interpret to introduce a safe mitigation (see FIG. 4c) which is not done in this case.


The “Force” line in the third row represents the actual resistance or torque development during the training. It is shown that the user's maximum force of 1 is exceeded between time points 0.8 and 1.4. This is a particularly dangerous situation in the case of knee lock because the leg may not be contracted, but an extreme hyperextension may be the result.



FIG. 4c shows a possible development of the repetition shown in FIG. 4b, assuming that there is a velocity-based safety component. It is shown that the mitigated values represented by the thick lines “Safety Position”, “Safety Velocity” and “Safety Force” deviate from the case of the joint lock represented by the thin dashed lines “Position”, “Velocity” and “Force”.


At time point 0.5, when a discrepancy of the actual velocity from the target velocity is detected, the safety force is gradually limited towards safe force values. Safe force values may be either values which are absolute with respect to the user's maximum force, or proportional with respect to the deviation of target and actual velocity.



FIG. 5 depicts the well-known systematic derivation of position via velocity, acceleration and jerk.



FIG. 6 depicts critical situations around a velocity turn point. Velocity is the first derivation of the positions-function. Where a turn phase, a turn point or similar is referenced, the zero transition of the velocity curve is addressed.


The turn phase (active during a predetermined, fixed time period, t0-t4) can be divided into logical phases:

    • Braking phase or concentric movement phase (t0-t2)
    • Acceleration phase or eccentric movement phase (t2-t4)
    • Tricky phase (t1-t3): The time period in which the model predicts the turn point, and when it is predicted that the lever velocity drops zero or even alternates.


Determination of time points:

    • t0: Instant when the lever passes the predefined turn start point (position based), or a time point relative to the detection of a non-negative velocity (velocity based)
    • t1, t2, t3, t4: Predetermined time offsets from t0, taking the assumption that the lever moves at the target velocity. If the actual lever velocity differs from the concentric phase, these estimations may be inaccurate.


In addition to logical turn phases defined above, the movement phase is defined into different logical movement zones based on the current turn phase and current movement speed:

    • Regular zones “R”:
      • Between the time points t0 and t1, a regular concentric movement at the target velocity is performed. The user is free to drop the speed below target velocity. No safety mitigation is necessary, unless the velocity drops below zero.
      • Between the time points t3 and t4, a regular eccentric movement is performed at an actual velocity which is greater than the “Slow Velocity Threshold”.
    • Eccentric danger zone D1: Active between the time points t3 and t4, when the actual velocity is absolutely greater than “Zero Velocity Threshold” and absolutely less than the “Slow Velocity Threshold”. In this zone, a proportional force limitation or deactivation must be triggered.
    • Reverse-Eccentric danger zone D2: Active between the time points t3 and t4, when the actual velocity is absolutely less than “Zero Velocity Threshold”. This means the user reversed the lever movement direction before reaching the end position. In this zone, it is critical that a force limitation or deactivation is triggered.
    • Tricky zone: Active between the time points t1 and t3. This is the turn point phase, when a joint lock is likely to occur, and when it is most critical to detect and mitigate possible irregularities to avoid safety risks.
      • Tricky zones T1, T2: Active between the time points t1 and t2, when the actual velocity drops absolutely below “Slow Velocity Threshold”. This drop can indicate an early reversal of the movement direction, but the drop can also be easily confused with a correct regular turn phase movement. In general, T1 and T2 can be handled analogously to T4 and T5.
      • Early reversal tricky zone T3: Active between the time points t1 and t2, when the actual velocity drops absolutely below “Zero Velocity Threshold”. This drop indicates an early reversal of the movement direction, and it should be assumed that the eccentric movement phase is entered. Thus, handling should proceed according to T4, T5, or T6.
      • Tricky zones T4, T5: Active between the time points t2 and t3, when the actual velocity drops absolutely below “Slow Velocity Threshold”. This drop can be easily confused with a correct regular turn phase movement and special rules need to be applied. It can be critical to choose t3 small enough, meaning that the lever must not travel further than safely absorbed by leg's tendons and ligaments elasticity, to avoid a possible injury even a misdetection might happen. Further, it is critical to limit the upper limit of the force to a value that does not exceed safe limits (determined relative by the concentric movement velocity, user's maximum strength, and/or absolute training machine parameters)
      • Reverse-Tricky zones T6: Active between the time points t2 and t3, when the actual velocity becomes positive, greater than “Zero Velocity Threshold”. This means the user reversed the lever movement direction. In this zone it is critical that a force limitation or deactivation is triggered.

Claims
  • 1. A training machine comprising: a resistance component configured to provide at least a resistance during training;a control configured to control the resistance component during training;a safety component configured to monitor the training machine on the basis of velocity and/or any derivative thereof.
  • 2. The training machine according to claim 1 wherein the safety component comprises a measurement component that is configured to measure velocity along a path, acceleration along a path and/or jerk along a path.
  • 3. The training machine according to claim 1 further comprising a sensor that is configured to deliver a signal that is configured to be used by the safety component to monitor the training machine on the basis of at least one of velocity, acceleration and/or jerk.
  • 4. The training machine according to claim 1 wherein the actual value(s) are based on a speed signal.
  • 5. The training machine according to claim 1 wherein the safety component is configured to adjust the resistance component at least in part and/or complete.
  • 6. The training machine according to claim 1 wherein the safety component is configured to deactivate the resistance component at least in part and/or complete upon in case the actual value(s) is/are out of the target value(s).
  • 7. The training machine according to claim 1 wherein the safety component is configured to deactivate the resistance component by a gradual reduction of the resistance to a minimum or zero.
  • 8. The training machine according to claim 1 wherein the safety component is configured to deactivate the resistance component by a gradual reduction of the resistance to a minimum or zero depending on the position of the machine.
  • 9. The training machine according to claim 1 wherein the actual value(s) at and/or around the change of concentric and eccentric movement are analyzed.
  • 10. The training machine according to claim 1 wherein the actual value(s) at and/or around the change of concentric and eccentric movement are analyzed and a deactivation of the resistance component at least in part and/or complete is triggered when the actual value(s) of the velocity is/are lower in absolute terms than the range of target value(s) of the velocity.
  • 11. The training machine according to claim 1 wherein the safety component is integrated with the control.
  • 12. A method of controlling a training machine comprising the steps of: providing a resistance during training by a resistance component;controlling a resistance component during training by a control component; andmonitoring the training machine on the basis of at least one of, velocity and/or any derivative thereof by a safety component.
  • 13. The method according to claim 12 with the further step of providing the safety component with a differentiator that determines at least a first derivative of the velocity and/or a second derivative of the velocity during training and communicating with the motor control of the resistance module.
  • 14. The method according to claim 12 wherein the safety component is configured to deactivate the resistance component by a gradual reduction of the resistance to a minimum or zero depending on the position of the machine.
  • 15. The method according to claim 12 wherein the actual value(s) at and/or around the change of concentric and eccentric movement are analyzed.
  • 16. The method according to claim 12 wherein the actual value(s) at and/or around the change of concentric and eccentric movement are analyzed and a deactivation of the resistance component is triggered when the actual value(s) of the velocity is/are lower in absolute terms than the range of target value(s) of the velocity.
  • 17. The method according to claim 12 with the further step of providing target values and the measured values are trained into a machine learning algorithm and the machine learning algorithm provides a control signal to the safety component.
  • 18. The method according to claim 17 wherein the target value(s) are trained by a machine learning algorithm on the basis of training data.
  • 19. The method according to claim 17 wherein the actual value(s) at and/or around the change of concentric and eccentric movement are analyzed and a deactivation of the resistance component is triggered when the actual value(s) of the velocity is/are lower in absolute terms than the range of target value(s) of the velocity.
  • 20. A computer program comprising instructions, which, when the program is executed on a data processing system causes the method steps of providing a resistance during training by a resistance component;controlling a resistance component during training by a control component;monitoring the training machine on the basis of at least one of, velocity and/or any derivative thereof by a safety component;providing target values and the measured values are trained into a machine learning algorithm and the machine learning algorithm provides a control signal to the safety component wherein the target value(s) are trained by a machine learning algorithm on the basis of training data; and/orwherein the actual value(s) at and/or around the change of concentric and eccentric movement are analyzed and a deactivation of the resistance component is triggered when the actual value(s) of the velocity is/are lower in absolute terms than the range of target value(s) of the velocity.
Priority Claims (1)
Number Date Country Kind
20170468.1 Apr 2020 EP regional