Patent Application
20190344121
This description generally relates to sensor-equipped athletic garments, and specifically to determining the type of exercise training adaptation using physiological data from the sensors.
Sensors record a variety of information about the human body. For example, electrocardiograph (ECG) electrodes can measure electrical signals from the skin of a person that are used to determine the person's heart rate. In addition, electromyography (EMG) electrodes can measure electrical activity generated by a person's muscles. EMG provides data associated with neuromuscular function of a person and ECG provides data associated with cardiovascular function. EMG signals associated with strength training exercises may differ from EMG signals associated with endurance training exercises. Thus, it is challenging for a system to generate useful metrics of athletic performance without distinguishing between different types of training. Since athletes playing different sports may have personalized training programs to improve performance based on the specific demands of the sport, it is desirable for systems to tailor metrics by taking into account the relevant types of training.
The goal of training and strength and conditioning in competitive athletics is to prepare an athlete for the demands of their sport and to improve performance. The trainable characteristics of an athlete (supported by American College of Sports Medicine) include, for example, power, strength, hypertrophy, anaerobic endurance, aerobic endurance and speed. Different sports require a different makeup of these trainable characteristics. For example, to improve performance in a 100 meter (100 m) sprint, athletes may focus their training on increasing power, strength and speed. However, to improve performance in the 10 kilometer (10 k) distance, athletes may focus their training on improving anaerobic and aerobic endurance. Using a combination of EMG and ECG sensors, and optionally motion data (e.g., accelerometer data) as an input, a method is described to classify adaptation types and/or determine the proportion of training adaptation types based on the type of neuromuscular and cardiovascular function.
The type of training (e.g., intensity, duration, frequency, and rest) determines the type of stress placed on the neuromuscular and cardiovascular systems. Over time as the body is repeatedly stressed with a specific type of training, the body will adapt to improve performance for that type of training. For example, in the case of the 100 m sprint athlete, repeated training at high intensity over short duration will adapt the athlete to increase power and strength. And in the case of the 10 k distance, training for longer durations at lower intensity will adapt the athlete to increase anaerobic and aerobic endurance.
Tailoring training based on the specific requirements for an individual athlete and their sport has proved to be difficult in conventional systems, largely due to the inability to understand what type of stresses are on each portion of the body during training. EMG sensors provide a measure of neuromuscular function and ECG sensors provide a measure of cardiovascular function. Using a combination of EMG sensors and ECG sensors the embodiments disclosed herein can categorize each athlete's training into the proportion of each adaptation type (e.g., power, strength, hypertrophy, endurance and speed) within a repetition, set, or session of exercise.
By leveraging a classification of the adaptation types, training can be further tailored for the athlete to match training to the specific types of stress required of the athlete for their sport. By classifying adaptation types metrics and relevant data may be provided to the athlete or coach. For example, the metrics can provide the amount of stress for different muscle groups for each adaptation type. The distribution of muscular stress for power and strength adaptation segments may be more important to the 100 m sprint athlete. Also, a model trained to predict injury risk for an athlete can leverage the training adaptation data to improve the predictability of the model. For example, features associated with power and strength adaptation types may be more predictive of injury risk for a 100 m sprint athlete, or certain features may have greater weight in the model for the power and strength adaptations. As another example, for a 10 k endurance athlete, specific features within the anaerobic endurance adaptation may have greater weight to improve injury predictability.
An exercise feedback system generates biofeedback based on physiological adaptations. The exercise feedback system processes physiological data from sensor-equipped garments worn by athletes while performing exercises. The physiological data may include EMG signals indicative of muscle activation levels. Additionally, the physiological data may include ECG signals indicative of heart rate. The exercise feedback system can use motion data from the sensors to identify segments of the physiological data representing periods of time during which the athletes were actively performing the exercises. In addition, the exercise feedback system can use bioimpedance data to determine noise levels or contact quality of the EMG and ECG sensors to the athlete's body. Bioimpedance data for athletic garments is further described in U.S. patent application Ser. No. 15/257,739.
The exercise feedback system may use a trained model to determine classifications of segments of the physiological data. Classifications may represent a type of physiological adaptation, for example, power, strength, hypertrophy, endurance, or speed. As used herein, physiological adaptations may include any changes to the athlete's physiology as result of exercise, e.g., growth in muscles or increase in cardiorespiratory capacity. In relation to training the model, embodiments of the system and method can create a training set upon applying a set of operations (e.g., signal processing operations, frequency domain operations, time domain operations, etc.) to a reference set of physiological data generated from one or more reference users and associated with the exercise(s) being analyzed. The model can be trained, using the training set, to determine, for each of a set of types of physiological adaptations (e.g., power, strength, hypertrophy, endurance, or speed), a probability that a given subset of physiological data is associated with one or more of the set of types of physiological adaptations.
Athletes can focus on training for one or more physiological adaptations, which may be based on a specific sport or training goal of an athlete. For example, long distance runners focus on endurance training, while Olympic lifters focus on power and strength training. The exercise feedback system can generate biofeedback including metrics determined using the classifications. For example, the metrics indicate training load aggregated over multiple muscles or workouts, or the biofeedback may notify athletes regarding a risk of injury.
The figures depict embodiments of the present invention for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the invention described herein.
The client device 110 is a computing device capable of receiving user input as well as transmitting and/or receiving or collecting data via the network 140. A client device 110 is a device having computer functionality, such as a smartphone, personal digital assistant (PDA), a mobile telephone, tablet, laptop computer, desktop computer, a wearable computer (such as a smart watch, wrist band, arm band, chest band, or the like), or another suitable device. In one embodiment, a client device 110 executes an application allowing a user of the client device to interact with the exercise feedback system 100. For example, a client device 110 executes a browser application to enable interaction between the client device 110 and the exercise feedback system 100 via the network 140. In another embodiment, a client device 110 interacts with the exercise feedback system 100 through an application programming interface (API) running on a native operating system of the client device, such as IOS® or ANDROID™.
An athlete 120 wears the athletic garment 130 (further described below with reference to
In various embodiments, the exercise feedback system 100 generates biofeedback based on physiological adaptations, which may provide context to support personalizing training for athletes to reduce injury risk or focus on particular areas of performance. For instance, the exercise feedback system 100 may evaluate injury risk by monitoring stresses experienced by particular muscles over a period of time. For example, physiological adaptations can be used to weight the stresses of particular muscle groups in an injury risk model based on the type of adaptation. One set of features (e.g., muscle stress, activation, sequence, balance, etc.) may show higher correlation to injury risk for an athlete with a higher proportion of power and strength adaptation training. A different set of features may show higher correlation to injury risk for an athlete with a higher proportion of endurance adaptation training. Additionally, using physiological adaptations, the exercise feedback system 100 may determine biofeedback used to improve training for a specific type of sport or goal of an athlete. As used herein, types of physiological adaptations may include, e.g., power, strength, hypertrophy, speed, and endurance. In other embodiments, a different number or other types of physiological adaptations may be used. An athlete training for long distance running (e.g., a marathon) may want to focus on training for endurance, while a different athlete training for weight-lifting may want to focus on training for strength or hypertrophy.
In the embodiment shown in
It should be noted that while the athletic garment 200 shown in
The exercise program engine 300 manages exercise programs for athletes of the exercise feedback system 100. An exercise program may include one or more workouts or exercise sets (also referred to as a “set”), where a set includes a number of repetitions of an exercise to be performed in sequence. In some embodiments, an athlete registers on the exercise feedback system 100 by completing an onboarding process during which the exercise program engine 300 receives user information or physiological data associated with the athlete. The exercise program engine 300 may store the received user information or physiological data in the athlete data store 350. User information includes, for example, demographic data (e.g., age, gender, ethnicity, etc.), geographic data, exercise related data (e.g., sports played, a specific position for sport, or sports team information), or physical attributes (e.g., height or weight). Physiological data may include data generated by the sensors of an athletic garment, e.g., for onboarding or calibration.
The data processing engine 305 processes physiological data generated by sensors of an athletic garment (e.g., athletic garment 130 or 200 shown in
The data processing engine 305 may use EMG signals of physiological data to determine metrics including, for example, muscle activation data, training load, or muscle stress. The EMG signals may vary based on parameters associated with the athlete wearing the athletic garment, e.g., differences in the physiology between multiple types of muscles influences the physiological data representing contraction of the muscles. For instance, the glutes and quads differ in the number of muscle fibers, fiber size, fiber type distribution (e.g., slow twitch or fast twitch), and thickness of adipose tissue between the muscle tissue and skin surface. The amplitude of EMG signals (e.g., muscle stress) may be proportional to the muscle fiber size and/or inversely proportional to the amount of fat between the muscle and the portion of skin.
The data processing engine 305 may determine a quality of physical contact between a sensor of an athletic garment and an athlete's skin based on bioimpedance data, which is generated by another sensor or the same sensor. Thicker hair on the athlete's skin or skin dryness may result in poor contact quality, and thus reduce the amplitude of (or otherwise modify) EMG signals generated by a sensor, or introduce additional noise in the EMG signals. In some embodiments, the amplitude of physiological data generated by a sensor and the signal to noise ratio is inversely proportional to the bioimpedance between the sensor and the skin of the athlete. Responsive to determining that the quality of physical contact during a period of time is less than a threshold quality (e.g., indicative of poor contact quality), the data processing engine 305 may exclude or modify the physiological data received during the period of time from generation of metrics or biofeedback.
In some embodiments, the data processing engine 305 uses at least one of physiological data and motion data received from motion sensors of an athletic garment to determine whether an athlete wearing the athletic garment is actively training or performing a particular type of exercise. In addition to resting between sets, the athlete may also intermittently rest between repetitions or otherwise temporarily interrupt or pause an exercise, e.g., to adjust the athletic garment. By classifying active segments (e.g., periods of time) and inactive segments in physiological data, the data processing engine 305 may account for discrepancies in the data, for example, filtering out noise from inactive periods during which the athlete is not performing an exercise. In some embodiments, the data processing engine 305 disregards physiological data, for determining metrics of a specific physiological adaptation or muscle, generated during a given exercise responsive to determining that the given exercise is not directed to training that specific physiological adaptation or muscle. For instance, physiological data of the glutes and quads generated while the athlete is performing certain upper body exercises may not be normalized or accumulated for determining metrics of the lower body muscles.
The adaptation model 310 classifies physiological data based on types of physiological adaptation. In particular, the adaptation model 310 may determine at least one classification of physiological adaptation for one or more segments of physiological data. The segments may be contiguous portions or subsets of the physiological data representing EMG signals generated by the sensors during different periods of time, which is further described with reference to
In some embodiments, the adaptation model 310 determines adaptation parameters of an input physiological data and uses the adaptation parameters to evaluate the physiological data. The adaptation parameters may describe features such as frequency or amplitude of the physiological data, for example, one or more of EMG signals, ECG signals, and motion data (e.g., acceleration or gyroscope data). The adaptation model 310 may use the adaptation parameters to determine a probability that a given segment (e.g., subset of the physiological data) is associated with a specific one (or more) of the types of physiological adaptation. The adaptation model 310 stores adaptation parameters in the parameter store 340 and may also retrieve previously determined adaptation parameters from the parameter store 340, e.g., to use as feedback.
In some embodiments, the adaptation model 310 aggregates segments according to their respective type of physiological adaptation for determining metrics for athletes. Thus, the exercise feedback system 100 may accumulate training loads (EMG activation over time) for the aggregated segments to determine aggregate training loads for specific muscles and specific physiological adaptations. Responsive to determining that a ratio of aggregate training load of one muscle group over another is greater than a threshold for a certain period of time, and for a specific adaptation type, the exercise feedback system 100 may determine that the athlete has a greater risk of injury.
In some embodiments, the machine learning engine 320 uses one or more machine learning techniques to train an adaptation model 310. The machine learning engine 320 may use reference physiological data as training data for training the adaptation model 310. For instance, the reference physiological data is generated by sensors (e.g., of an athletic garment) on a population of users who performed exercises, and processed with one or more operations (e.g., signal processing operations, windowing, filtering, operations in the frequency domain, operations in the time domain, etc.) to create a training dataset. The training data may be labeled, for example, associating a certain training data set with one or more specific types of physiological adaptation (e.g., power, strength, hypertrophy, endurance, or speed), types of exercise, etc. The training data may also include reference motion data, e.g., a motion profile indicating proper (or improper) form of a certain exercise. During training, the adaptation model 310 may determine features of the training data such as reference parameters, which may be stored in the parameter store 340. A reference parameter may describe one or more attributes of sensor data, e.g., an expected peak amplitude or frequency of a data signal, a particular pattern in the data signal, or certain thresholds of amplitude or width of peaks in the data signal. Example reference parameters are described below with reference to
The biofeedback engine 330 generates or updates user interfaces to present biofeedback via client devices 110 to athletes, coaches, or other persons. Biofeedback may indicate a metric of athletic performance such as a percentage value, a Boolean value (e.g., satisfactory or unsatisfactory), or any suitable type of value. Metrics may be aggregated for multiple muscles, over multiple workouts, based on adaptation type or in any other suitable manner. In some embodiments, the biofeedback engine 330 generates biofeedback based on context, for example, a specific type of physiological adaptation, exercise, muscle or muscle group, repetition, set, or load (e.g., based on a product of a number of repetitions and corresponding weight lifted by an athlete).
In some embodiments, the biofeedback engine 330 generates a graphical depiction of muscles of an athlete for presenting biofeedback. In particular, the biceps and quads (among other types of muscles) may be overlaid on the arm and leg portions, respectively, of a human body graphic (e.g., resembling a silhouette, avatar, or the like) of the athlete. The biofeedback engine 330 may present metrics of muscles by dynamically updating a color or size of the graphic depiction of the corresponding muscle. For instance, as the muscle stress measurement of a given muscle increases, a graphic of the given muscle becomes a brighter color or increases in size to illustrate that the given muscle is being contracted for an exercise. Thus, the athlete can view a real-time progression of one or more muscles that increase or decrease in activation levels, stress, or training load throughout stages of an exercise.
In some embodiments, the biofeedback engine 330 communicates a risk of injury, e.g., via a push notification presented by a client device 110. The biofeedback engine 330 may notify an athlete of the risk of injury after an exercise or while an athlete is exercising, so that the athlete can adjust exercise training to avoid the injury or reduce the risk. The biofeedback engine 330 may provide context with a notification of injury risk, for example, indicating a type of risk (e.g., over stressing a specific muscle or joint), severity of the risk, or remedial action such as performing stretches, icing or heating a muscle, resting, or switching to a different type of physiological adaptation for training.
In some example use cases, an athlete using the exercise feedback system 100 is part of an athletic team including multiple athletes, coaches, or other personnel. The biofeedback engine 330 may generate biofeedback including a comparison between the athlete and another athlete of the same team. The biofeedback may present a comparison of athletes' performance categorized based on physiological adaptation. Furthermore, the biofeedback engine 330 may provide biofeedback for presentation to a coach of the team. The biofeedback engine 330 may send biofeedback based on aggregate metrics of the team to a client device 110 of the coach, or may flag individual team members based on different metrics and may identify an athlete to be at risk of injury, beneficially allowing the coach to intervene before injury occurs.
In some embodiments, some or all of the functionality of the exercise feedback system 100 may be performed by or implemented within a client device 110 or a processing unit 290 (of the athletic garment 200 shown in
In some embodiments, the adaptation model 310 classifies segments as one or more physiological adaptations by identifying one or more features from input data. The features, or reference parameters learned from training, may include a certain pattern (e.g., repeating for at least a particular duration of time or number of instances), or attributes detected in motion data or EMG data. In the example shown in
The adaptation model 310 may determine that the EMG data during the time period of the first segment 410 represents a periodic signal having a given period and amplitude (e.g., peak amplitude or averaged over the duration of the segment). Further, the adaptation model 310 may compare the given period or amplitude to a reference period or amplitude, respectively, of a reference data generated by sensors monitoring a user performing exercise for one or more specific types of physiological adaptation. Responsive to determining that the given period is within a threshold error from the reference period, and/or determining that the given amplitude is within a threshold error from the reference amplitude, the adaptation model 310 may determine that the first segment 410 likely includes EMG data representing training for the one or more specific types of physiological adaptation.
Following in the above example, the reference data may be associated with an exercise having a speed or endurance type of physiological adaptation. For instance, the exercise is a treadmill run and the reference data includes EMG signal of lower body muscles of an athlete running on a treadmill. Accordingly, the adaptation model 310 determines that the first segment 410 should be classified with the speed or endurance type of physiological adaptation.
In some embodiments, the adaptation model 310 determines predictions of physiological adaptation by determining a rate of change of physiological data. Typically, power, strength, or hypertrophy types of exercises involve short bursts of muscle exertion, e.g., to lift a certain amount of weight. Thus, EMG signals generated by sensors tracking athletes performing these types of exercises may exhibit greater rates of change relative to EMG signals for speed or endurance types of exercises. In the example shown in
In some embodiments, the adaptation model 310 classifies a segment as a power type of physiological adaptation responsive to one or more of (i) determining that the EMG signal is greater than 100% of a reference EMG parameter (e.g., reference parameter set by the calibration process, where the reference parameter may indicate an expected peak value), (ii) determining that peaks in the EMG signal have less than a threshold width, (iii) determining that the rate of change of the EMG signal during peaks is greater than a threshold rate, (iv) determining that an accumulation of EMG signal over the duration of the peak is greater than a threshold value, (v) determining that a corresponding motion sensor data signal indicates at least a threshold acceleration (e.g., occurring simultaneously during at least a portion of the peaks in EMG signal), and (vi) determining that a heart rate signal has at least a threshold derivative and/or peak changes. The exercise feedback system 100 may determine (or update) the reference EMG parameter during calibration, user profile information, and/or reference data.
In some embodiments, the adaptation model 310 classifies a segment as a strength type of physiological adaptation responsive to one or more of (i) determining that the EMG signal is greater than a first threshold and less than the reference parameter (e.g., approximately 75% of the reference parameter), (ii) determining that peaks in the EMG signal have greater than a threshold width, (iii) determining that the rate of change of the EMG signal during peaks is less than a threshold rate, (iv) determining that the active duration or number of detected reps is less than a threshold duration or number, (v) determining that a corresponding motion sensor data signal indicates less than a threshold acceleration (e.g., occurring simultaneously during at least a portion of the peaks in EMG signal), and (vi) determining that a heart rate signal has at least a threshold derivative and/or peak changes in heart rate.
In some embodiments, the adaptation model 310 classifies a segment as a hypertrophy type of physiological adaptation responsive to one or more of (i) determining that the EMG signal is greater than a second threshold lower than the first threshold with respect to the strength type of physiological adaptation (e.g., approximately 65% of the reference parameter) and (ii) determining that the active duration or number of detected reps is greater than a threshold duration or number.
In some embodiments, the adaptation model 310 classifies a segment as an endurance type of physiological adaptation responsive to one or more of (i) determining that the EMG signal is less than the second threshold, (ii) determining that the EMG signal peak levels are consistent and repeating (e.g., responsive to determining that the Wiener entropy of a spectrum of the physiological data is less than a threshold value), and (iii) determining that variation in heart rate is less than a threshold rate or indicates slow gradual changes above a threshold value (e.g., above 40% of athlete's max heart rate). EMG signals having peaks less than a third threshold (e.g., approximately 35% of the expected peak value) may be indicative of aerobic endurance training, while EMG signals having peaks between the second and third threshold (e.g., inclusive) may be indicative of anaerobic endurance training.
In some embodiments, the adaptation model 310 classifies a segment as a speed type of physiological adaptation responsive to one or more of (i) determining that the EMG signal peaks repeatedly at least at a threshold frequency (e.g., determining that the Wiener entropy of the spectrum is less than a threshold value) and (ii) determining that motion data indicates repeated patterns in acceleration data at consistent frequency and/or having less than a threshold duration in time.
The adaptation model 310 uses the inputs to classify the active segments and usable data as being associated with one or more physiological adaptations. In some embodiments, the adaptation model 310 outputs any number of classified power segments, strength segments, hypertrophy segments, speed segments, and endurance segments. The biofeedback engine 330 may receive the output from the adaptation model 310 and generate biofeedback based on the classified segments. Additionally, the biofeedback engine 330 may generate biofeedback further using any number of the inputs to the adaptation model 310. For example, the biofeedback determines a measure of stress for each muscle group for the session by aggregating the stress corresponding to the segments of each adaptation type. In another embodiment, the biofeedback includes metrics for segments of a certain classification accompanied with context such as the exercise form of the athlete (e.g., based on EMG and motion data) or the athlete's heart rate while training.
The adaptation model 310 aggregates segments 530 of physiological data according to their respective classifications. For example, the aggregates segments for at least one of power, strength, hypertrophy, speed, and endurance. The aggregated segments are output, for example, to the biofeedback engine 330 for generating biofeedback, which may include metrics of an athlete's aggregate training load or contribution of muscles categorized by type of physiological adaptation. The metrics may allow an athlete to determine whether the athlete is focusing on a desired type of training, to identify muscles that are being overexerted, or to identify muscles that are being under-utilized and need further training.
In the decision tree, the adaptation model 310 calculates 540 P(endurance or speed), which represents the probability that a given segment should be classified as endurance or speed. GivenP(endurance or speed), the adaptation model 310 calculates 542 P(speed|endurance or speed), the probability that the given segment should be classified as speed. Thus, the adaptation model 310 calculates the probability that the given segment should be classified as speed as P(speed)=P(endurance or speed)*P(speed|endurance or speed) The adaptation model 310 calculates the probability that the given segment should be classified as endurance as P(endurance)=P(endurance or speed)*(1−P(speed|endurance or speed)).
On the other branch of the example decision tree, the probability that the given segment should be classified as power, strength, or hypertrophy is P(power, strength, or hypertrophy)=1−P(endurance or speed) Given P(power, strength, or hypertrophy), the adaptation model 310 calculates 544 P(power|power, strength, or hypertrophy), the probability that the given segment should be classified as power. Thus, the probability that the given segment should be classified as strength or hypertrophy P(strength, or hypertrophy)=P(power, strength, or hypertrophy)*(1−P(power|power, strength, or hypertrophy)) Given P(strength or hypertrophy), the adaptation model 310 calculates 546 P(strength|strength or hypertrophy), the probability that the given segment should be classified as strength. Thus, the probability that the given segment should be classified as hypertrophy is P(hypertrophy)=P(strength or hypertrophy)*(1-P(strength|strength or hypertrophy)
The adaptation model 310 may store the parameters for each segment or epoch in a table, array, or other suitable data structure in the parameter store 340.
In one embodiment, the exercise feedback system 100 receives 710 physiological data from a garment (e.g., garment 200 shown in
The model determines 730 using at least the first classification, a second classification from the set of classifications of a second subset of the physiological data. In some embodiments, motion data from the sensors of the garment are also provided as input to the model, e.g., to determine that the first and second subsets of the physiological data correspond to periods of time during which the user actively performed at least a portion of the one or more exercises. In some embodiments, the model determines the classifications responsive to determining that a noise level of the sensors of the garment is less than a threshold noise level. The noise level may be based on bioimpedance, e.g., quality of physical contact between the sensors and skin of the user.
The exercise feedback system 100 transmits 740 biofeedback to a client device 110 for presentation to the user. Additionally or alternatively, biofeedback and/or associated classifications/calculations can be stored and transmitted 740 by the exercise feedback system 100 to a cloud-based computing system, for presentation on another client device (e.g., associated with a coaching entity), as shown in
In one embodiment, the exercise feedback system 100 receives 810 physiological data from a garment worn by a user. The physiological data describes muscle activation of a set of muscles of the user while performing one or more exercises. The garment includes a set of sensors configured to generate the physiological data. The exercise feedback system 100 determines 820 classifications of subsets of the physiological data. The classifications are selected by a model (e.g., adaptation model 310 of
The exercise feedback system 100 aggregates 830 subsets having a same classification to determine 840 biofeedback for each of the set of classifications. The exercise feedback system 100 may repeat steps 830-840 for any number of classifications, e.g., each of which corresponding to a different physiological adaptation. The exercise feedback system 100 transmits 850 the biofeedback for each of the plurality of classifications to a client device for presentation to the user.
As an example of biofeedback, the adaptation model 310 determines a criteria for detecting or evaluating a risk of injury. The risk of injury may be determined based on an identified imbalance of muscle stress or a fatigue level of an athlete, and the biofeedback engine 330 provides context informing the athlete to address the identified imbalance. For instance, the biofeedback recommends increasing training on the left or right side upper arm muscles or reducing the amount of weights overall to recover from a high-risk fatigue level and avoid injury. In some embodiments, the adaptation model 310 calculates a ratio of contribution between two (or more) specific muscles, and determines that there is a risk injury responsive to determining that the ratio is greater than a threshold, which is indicative of a more severe imbalance or discrepancy in exercise form relative to the correct form.
In some variations, the method can further include one or more of: implementing a history of activity sessions of the user to refine characterization of at least one type of the physiological adaptation; implementing a contextual input from the user to refine characterization of at least one type of the physiological adaptation, the contextual input describing difficulty in performing an activity; and customizing a characterization of at least one type of the physiological adaptation to a demographic comprising the user.
The foregoing description of the embodiments of the invention has been presented for the purpose of illustration; it is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Persons skilled in the relevant art can appreciate that many modifications and variations are possible in light of the above disclosure.
Some portions of this description describe the embodiments of the invention in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are commonly used by those skilled in the data processing arts to convey the substance of their work effectively to others skilled in the art. These operations, while described functionally, computationally, or logically, are understood to be implemented by computer programs or equivalent electrical circuits, microcode, or the like. Furthermore, it has also proven convenient at times, to refer to these arrangements of operations as modules, without loss of generality. The described operations and their associated modules may be embodied in software, firmware, hardware, or any combinations thereof.
Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product including a computer-readable non-transitory medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.
Embodiments of the invention may also relate to a product that is produced by a computing process described herein. Such a product may include information resulting from a computing process, where the information is stored on a non-transitory, tangible computer readable storage medium and may include any embodiment of a computer program product or other data combination described herein.
Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based hereon. Accordingly, the disclosure of the embodiments of the invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.
This application claims the benefit of U.S. Provisional Application Ser. No. 62/671,309 filed 14 May 2018, which is incorporated in its entirety herein by this reference.
| Number | Date | Country | |
|---|---|---|---|
| 62671309 | May 2018 | US |
