As fitness instruction and tracking become more accessible, new strategies and techniques for remotely guiding individuals through fitness exercises are required to create the best experience and convey instructional information more effectively.
Embodiments provide improved fitness instruction and performance measurement through determination and use of an instantaneous effort value that is easy to understand and that facilitates optimal exertion during exercise.
In one embodiment, a method of calibrating, for an individual performing a fitness exercise, a level of intensity of the fitness exercise given a target effort value includes configuring a processor to determine an instantaneous effort value corresponding to an instantaneous level of physical and mental exertion for the individual. The determining of an instantaneous effort value includes obtaining measured data from an exertion monitor associated with the individual, the measured data including at least measurements of power. The determining of an instantaneous effort value further includes determining performance metrics from the measured data, the performance metrics including at least a peak power value and an average power value. The performance metrics can also include heart rate, respiratory rate or body positioning data. The determining of an instantaneous effort value further includes obtaining historical performance metrics for the individual, the historical performance metrics including at least historical peak power values and historical average power values. The power values may be reflective of power output by an individual, or power input to an exercise machine, or a combination thereof. The power values may be measured, for example, in Watts or calories per hour, or may be a modality-specific power measurement. The historical performance metrics may be obtained from the memory device, or alternatively raw historical performance data may be obtained from the memory device and historical performance metrics may be calculated therefrom.
The embodiment continues such that the determining of an instantaneous effort value further includes creating a best-fit equation. The equation may be based, at least in part, on historical performance data previously obtained from exertion monitors associated with at least one prior individual, which prior individual may be the same or different from the individual presently performing the fitness exercise. The equation may be based, at least in part, on expertise in a specific fitness exercise modality. For example, constants in the equation may be derived from analysis done on the historical performance data. Such analysis may include a comparison between historical performance data of prior individuals, including historical observed estimated effort values, and corresponding desired effort values. The desired effort values may be defined at various values of performance metrics for a given fitness exercise modality, thereby providing expertise in the modality, which expertise may thus be incorporated into the best-fit equation. In other examples, constants in the equation may be personalized or otherwise modified for the individual, modified for a specific fitness exercise modality or workout program, or modified based on other factors specific to a given scenario. Historical performance data may be represented in tabular format. An example of such historical performance data is a power-based split time measurement in rowing. An instantaneous split time value may be an independent variable used in the best-fit equation, and may be used in combination with other performance metrics values.
In the embodiment, the best-fit equation is modeled on a shape of a graph of the desired effort values defined at various values of performance metrics for the given fitness exercise modality. The equation in the embodiment is created by developing reference data based on the historical performance data, and based on expertise in the given modality, which expertise includes the desired effort values. In embodiments incorporating split times for a rowing modality, the shape of the graph is an S-curve, and a logistic regression is used to derive the equation. The best-fit equation is thus optimized in the embodiment based on distributions of historical values and a modality-specific understanding of individual effort in relation to performance metrics. Other independent variables based on other performance metrics can be added to the equation to increase precision. Similar analysis can be completed on historic data of the other performance metrics to determine constants that can be included in the equation. The embodiment continues such that the determining of an instantaneous effort value further includes calculating the instantaneous effort value for the individual using the best-fit equation.
The embodiment continues such that the processor is further configured to communicate, via a display device, the instantaneous effort value to the individual. The processor is further configured to determine a difference between the instantaneous effort value and the target effort value. The processor is further configured to adjust an aspect of the fitness exercise based on the determined difference.
In some embodiments, the exertion monitor may include sensors placed proximal to the individual or to an exercise machine employed by the individual. In some embodiments, the fitness exercise may be one of running, cycling, and rowing.
In some embodiments, the performance metrics may include measurements of cadence. Measurements of cadence may include, for example, steps per minute, revolutions per minute, or strokes per minute. In some embodiments, the performance metrics may include distance-based measurements. Distance-based measurements may include, for example, speed in miles per hour or kilometers per hour, or pace in minutes per mile, minutes per kilometer, minutes per 500 meters, or minutes per 2000 meters. In some embodiments, the performance metrics may include at least one of heart rate measurements, respiratory rate measurements, and body positioning measurements.
In some embodiments, the display device may be disposed proximal to an exercise machine. In some embodiments, communicating the instantaneous effort value to the individual may include indicating, on the display device, a visual representation of the instantaneous effort value, or a time series thereof, during performance of the fitness exercise by the individual. In some embodiments, adjusting an aspect of the fitness exercise may include automatically modifying a level of resistance on an exercise machine. Alternatively, or in addition, in some embodiments, adjusting an aspect of the fitness exercise may include alerting a trainer to advise the individual to modify his or her level of effort. In some embodiments, an exercise machine may be, for example, a rowing machine, a stationary or spinning bicycle, a conventional bicycle, or a treadmill.
In some embodiments, historical performance metrics, including average power values, may be modified by a decay factor such that more recent historical performance metrics are weighted more strongly and less recent historical performance metrics are weighted less strongly in a calculation of the instantaneous effort value. Such weighting may be achieved by using a weighted moving average calculation with a chosen alpha value and a time (t) value measured in number of days. In some embodiments, the method may further include transmitting an audio and video representation of the individual's performance of the fitness exercise to a device of a remotely located trainer, and further comprising transmitting an audio and video representation of a coaching session of the trainer to the display device to be viewed by the individual.
In another embodiment, a system for calibrating, for an individual performing a fitness exercise, a level of intensity of the fitness exercise given a target effort value, may include a processor and a memory device with instructions loaded thereon, the instructions, when loaded, configuring the processor to determine an instantaneous effort value corresponding to an instantaneous level of physical and mental exertion for the individual. The determining of an instantaneous effort value includes obtaining measured data from an exertion monitor associated with the individual, the measured data including at least measurements of power. The determining of an instantaneous effort value further includes determining performance metrics from the measured data, the performance metrics including at least a peak power value and an average power value. The performance metrics can also include heart rate, respiratory rate or body positioning data. The determining of an instantaneous effort value further includes obtaining historical performance metrics for the individual, the historical performance metrics including at least historical peak power values and historical average power values. The power values may be reflective of power output by an individual, or power input to an exercise machine. The power values, for example, may be measured in Watts or in calories per hour, or may be a modality-specific power measurement. The historical performance metrics may be obtained from the memory device, or alternatively raw historical performance data may be obtained from the memory device and historical performance metrics may be calculated therefrom.
The embodiment continues such that the determining of an instantaneous effort value further includes calculating, using a polynomial equation derived from a best-fit analysis applied to a set of historical observed estimated effort values, the instantaneous effort value for the individual based on the determined and historical performance metrics.
The embodiment continues such that the processor is further configured to communicate, via a display device, the instantaneous effort value to the individual. The processor is further configured to determine a difference between the instantaneous effort value and the target effort value. The processor is further configured to adjust an aspect of the fitness exercise based on the determined difference. This embodiment may further optionally include any features described herein in connection with any of the other embodiments described herein.
The foregoing will be apparent from the following more particular description of example embodiments, as illustrated in the accompanying drawings in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating embodiments.
A description of example embodiments follows.
Continuing with reference to
The embodiment depicted in
In some embodiments, the best-fit equation is modeled on a shape of a graph of the desired effort values defined at various values of performance metrics for the given fitness exercise modality. The equation in the embodiment is created by developing reference data based on the historical performance data, and based on expertise in the given modality, which expertise includes the desired effort values 232. In embodiments incorporating split times for a rowing modality, the shape of the graph is an S-curve, and a logistic regression is used to derive the equation. The best-fit equation is thus optimized in the embodiment based on distributions of historical values and a modality-specific understanding of individual effort in relation to performance metrics. Other independent variables based on other performance metrics can be added to the equation to increase precision. Similar analysis can be completed on historic data of the other performance metrics to determine constants that can be included in the equation.
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The application of a best-fit analysis to a set of historical observed estimated effort values, as described above with reference to
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In
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Client computers/devices 50 may be configured with a computing module (located at one or more of elements 50, 60, and/or 70). In some embodiments, a user may access the computing module executing on the server computers 60 from a user device, such a mobile device, a personal computer, or any computing device known to one skilled in the art without limitation. According to some embodiments, the client devices 50 and server computers 60 may be distributed across a computing module.
Server computers 60 may be configured as the computing modules which communicate with client devices 50 for providing access to (and/or accessing) databases that include fitness data of an individual, such as performance metrics and an instantaneous effort value. The server computers 60 may not be separate server computers but part of cloud network 70. In some embodiments, the server computer (e.g., computing module) may enable calibrating of a level of intensity of a fitness exercise, for an individual performing the fitness exercise, by allowing access to data located on the client 50, server 60, or network 70 (e.g., global computer network). The client (configuration module) 50 may communicate data including fitness data of an individual, such as performance metrics and an instantaneous effort value, back to and/or from the server (computing module) 60. In some embodiments, the client 50 may include client applications or components executing on the client 50 for calibrating of a level of intensity of a fitness exercise, for an individual performing the fitness exercise, and the client 50 may communicate corresponding data to the server (e.g., computing module) 60.
Some embodiments of the system 10 may include a computer system for calibrating of a level of intensity of a fitness exercise, for an individual performing the fitness exercise. The system 10 may include a plurality of processors 84. The system 10 may also include a memory 90. The memory 90 may include: (i) computer code instructions stored thereon; and/or (ii) data including fitness data of an individual, such as performance metrics and an instantaneous effort value. The data may include segments including portions of the performance metrics or instantaneous effort value. The memory 90 may be operatively coupled to the plurality of processors 84 such that, when executed by the plurality of processors 84, the computer code instructions may cause the computer system 10 to implement a computing module (the computing module being located on, in, or implemented by any of elements 50, 60, 70 of
According to some embodiments,
In one embodiment, the processor routines 92 and data 94 are a computer program product (generally referenced 92), including a computer readable medium (e.g., a removable storage medium such as one or more DVD-ROM's, CD-ROM's, diskettes, tapes, etc.) that provides at least a portion of the software instructions for the present disclosure. The computer program product 92 can be installed by any suitable software installation procedure, as is well known in the art. In another embodiment, at least a portion of the software instructions may also be downloaded over a cable, communication and/or wireless connection. Other embodiments may include a computer program propagated signal product 107 (of
In alternate embodiments, the propagated signal is an analog carrier wave or digital signal carried on the propagated medium. For example, the propagated signal may be a digitized signal propagated over a global network (e.g., the Internet), a telecommunications network, or other network. In one embodiment, the propagated signal is a signal that is transmitted over the propagation medium over a period of time, such as the instructions for a software application sent in packets over a network over a period of milliseconds, seconds, minutes, or longer. In another embodiment, the computer readable medium of computer program product 92 is a propagation medium that the computer system 50 may receive and read, such as by receiving the propagation medium and identifying a propagated signal embodied in the propagation medium, as described above for computer program propagated signal product.
Generally speaking, the term “carrier medium” or transient carrier encompasses the foregoing transient signals, propagated signals, propagated medium, storage medium and the like.
Embodiments or aspects thereof may be implemented in the form of hardware (including but not limited to hardware circuitry), firmware, or software. If implemented in software, the software may be stored on any non-transient computer readable medium that is configured to enable a processor to load the software or subsets of instructions thereof. The processor then executes the instructions and is configured to operate or cause an apparatus to operate in a manner as described herein.
Further, hardware, firmware, software, routines, or instructions may be described herein as performing certain actions and/or functions of the data processors. However, it should be appreciated that such descriptions contained herein are merely for convenience and that such actions in fact result from computing devices, processors, controllers, or other devices executing the firmware, software, routines, instructions, etc.
It should be understood that the flow diagrams, block diagrams, and network diagrams may include more or fewer elements, be arranged differently, or be represented differently. But it further should be understood that certain implementations may dictate the block and network diagrams and the number of block and network diagrams illustrating the execution of the embodiments be implemented in a particular way.
Accordingly, further embodiments may also be implemented in a variety of computer architectures, physical, virtual, cloud computers, and/or some combination thereof, and, thus, the data processors described herein are intended for purposes of illustration only and not as a limitation of the embodiments.
The teachings of all patents, published applications and references cited herein are incorporated by reference in their entirety.
While example embodiments have been particularly shown and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the embodiments encompassed by the appended claims.