Musculoskeletal burden

Abstract

To provide a means for evaluating a musculoskeletal burden.SOLUTION: The physiological monitor assesses the degree of muscle strain, musculoskeletal strain and / or biomechanical strain experienced by the user during the strength training activity using, for example, motion patterns detected by the wearable monitor during the strength training activity. Quantifying the resulting burden can be advantageously used to provide coaching recommendations, update daily burden metrics, or take other actions.SELECTED DRAWING: Figure 9

Description

[Technical Field]

[0001] [CROSS-REFERENCE TO RELATED APPLICATIONS] This application claims priority to U.S. Provisional Patent Application No. 63 / 395,244, filed August 4, 2022, the entire contents of which are incorporated herein by reference.

[0002] The present disclosure relates to physiological monitoring systems, and more particularly to techniques for quantitatively tracking musculoskeletal strain. [Background technology]

[0003] [background] Wearable physiological monitors can obtain a wealth of physiological data from the wearer. However, the muscle strain generated during strength training can be difficult to characterize using traditional fitness metrics such as heart rate and heart rate variability. There remains a need for improved methods and systems for monitoring musculoskeletal strain and using quantitatively measured strain to provide coaching recommendations, etc. Summary of the Invention [Means for solving the problem]

[0004] [overview] The physiological monitor may use, for example, motion patterns detected by a wearable monitor during strength training activities to assess the degree of muscular, musculoskeletal, and / or biomechanical strain experienced by a user during strength training. Quantifying the strain experienced can be advantageously used to provide coaching recommendations, update daily strain metrics, or take other measures.

[0005] In one aspect, a computer program product disclosed herein may include computer-executable code embodied in a non-transitory computer-readable medium, which, when executed on one or more computing devices, causes the one or more computing devices to: receive raw motion data from one or more motion sensors of a wearable fitness monitor worn by a user during a strength training activity comprising a set of one or more repetitive movements, the raw motion data comprising angular rotation data from a plurality of gyroscopes and linear acceleration data from a plurality of accelerometers; fuse the raw motion data from the one or more motion sensors to mitigate gravity artifacts, thereby providing motion data comprising three-axis acceleration data; identify a type of the strength training activity; determine an amount of the repetitive movements in the set based on a variability in magnitude of the three-axis acceleration data; and, for each one of the repetitive movements, calculate a magnitude of the three-axis acceleration data. calculating a raw strength score indicative of musculoskeletal movement based on changes in data; determining, for each one of the repetitive movements, a maximum intensity at which the user will perform the strength training activity, the maximum intensity being indicative of the user's ability to perform the strength training activity based on the user's exercise history; estimating a maximum volume of the strength training activity for the user based on the user's historical performance at the strength training activity, the maximum volume being indicative of an upper threshold for the user to perform repetitive movements of the strength training activity without injury; and calculating an effective load for the user during the strength training activity, the effective load being based on one or more load parameters including at least the user's body weight and an add-on weight for the strength training activity.The method may further include the steps of: calculating a payload, the payload indicative of a relative portion of the maximum volume exerted by the user during the strength training activity; calculating, for each of the repetitive movements, a musculoskeletal strain per repetition as a product of a first ratio of the payload to the maximum volume and a second ratio of the raw strength score to the maximum strength; summing the musculoskeletal strains per repetition for all the repetitive movements in the set to provide a musculoskeletal strain score for the strength training activity; and displaying the musculoskeletal strain score for the strength training activity to the user. Other embodiments of this aspect may further or alternatively include a method for performing one or more of the above steps. Other embodiments of this aspect may further or alternatively include a system including a wearable fitness monitor including one or more motion sensors and one or more processors configured to perform one or more of the above steps to calculate a user-specific musculoskeletal strain score for a user of the wearable fitness monitor.

[0006] Embodiments may include one or more of the following features. The computer program product may include code for causing the one or more computing devices to generate a coaching recommendation for the user based on the musculoskeletal strain score. The coaching recommendation may be based, at least in part, on a fitness goal of the user. Calculating the raw strength score for one of the repetitive movements may include calculating an average of multiple instantaneous strength measurements for the one of the repetitive movements. One or more of the multiple instantaneous strength measurements may be calculated based on an average of a ratio of discretely measured changes in current acceleration to the current acceleration. One or more of the multiple instantaneous strength measurements may be calculated based on a ratio of a first average of changes in current acceleration to a second average of the current acceleration. The computer program product may include code for causing the one or more computing devices to create a load-repetitive motion profile for the user based on a history of the strength training activity by the user, the load-repetitive motion profile being indicative of the user's ability to perform repetitive movements under one or more loads during the strength training activity. The computer program product may include code for causing the one or more computing devices to add a row to the load-repetitive motion profile when the user performs the strength training activity under a new load that is not included in the one or more loads in the load-repetitive motion profile. The computer program product may include code for causing the one or more computing devices to update the load-repetitive motion profile when the user exceeds a number of repetitive motions for one of the loads in the load-repetitive motion profile. The computer program product may include code for receiving user input specifying the type of the strength training activity.The computer program product may include code that causes the one or more computing devices to identify the type of the strength training activity based on the raw motion data. Embodiments of the described technology may include hardware, methods or processes, computer software on a computer-accessible medium, and systems.

[0007] In one aspect, a method disclosed herein includes receiving motion data from one or more motion sensors of a wearable monitor worn by a user during a strength training activity including a set of one or more repetitive movements; identifying a type of the strength training activity; determining an amount of the repetitive movements in the set; calculating a raw strength score indicative of musculoskeletal movement for each of the repetitive movements based on characteristics of the motion data; scaling the raw strength score to a maximum intensity at which the user performs the strength training activity to obtain a user strength score per repetitive movement, wherein the maximum intensity is determined based on the user's exercise history.

[0013] The method may further include: calculating, for each of the repetitive movements, a personalized scale based on a ratio of an effective load for the user during the strength training activity to a predetermined load threshold for the user performing the strength training activity; calculating, for each of the repetitive movements, a musculoskeletal strain per repetitive movement as a product of the user strength score per repetitive movement and the personalized scale; calculating a musculoskeletal strain score for the strength training activity by summing the musculoskeletal strains per repetitive movement for all the repetitive movements in the set; and taking action based on the musculoskeletal strain score. Other embodiments of this aspect may further or alternatively include a computer program product comprising computer-executable code embodied in a non-transitory computer-readable medium, the computer program product comprising computer-executable code embodied in a non-transitory computer-readable medium, the computer program product causing one or more computing devices to perform one or more of the above steps when the computer-executable code is executed on the one or more computing devices. Other embodiments of this aspect may additionally or alternatively include a system including a wearable fitness monitor including one or more motion sensors and one or more processors configured to calculate a user-specific musculoskeletal strain score for a user of the wearable fitness monitor by performing one or more of the above steps.

[0008] Embodiments may include one or more of the following features. Determining the amount of the repetitive movements in the set may include determining the amount based on motion data. Determining the amount of the repetitive movements in the set may include determining the amount based on user input. The measures may include refining a daily strain calculation for the user based on the musculoskeletal strain score. The measures may include generating a coaching recommendation for the user. The method may include displaying the coaching recommendation to the user. The coaching recommendation may be a real-time coaching recommendation. The coaching recommendation may be related to a subsequent exercise activity of the user. The method may include automatically identifying a type of the strength training activity based on the motion data. The method may include calculating a plurality of musculoskeletal strain scores for each of a plurality of types of strength training activities in an exercise routine. The method may include calculating the payload based on a user input of a weight of the user. The method may include calculating the payload based on a user input of an add-on weight for the strength training activity. The motion data may include raw motion data from a triaxial gyroscope and a triaxial accelerometer fused to provide triaxial acceleration data of the repetitive motion that reduces the effects of acceleration due to gravity. The wearable monitor may include a wrist-worn photoplethysmography device. Receiving the motion data may include receiving raw motion data from at least one gyroscope and at least one accelerometer of the wearable monitor. Calculating the musculoskeletal strain score may include calculating the musculoskeletal strain score on the user's personal computing device communicatively coupled to the wearable monitor. Calculating the musculoskeletal strain score may include calculating the musculoskeletal strain score on a remote server communicatively coupled to the wearable monitor.The predetermined load threshold may be an estimated maximum volume that indicates an upper threshold for the user to perform repetitive movements of the strength training activity without injury.Embodiments of the described technology may include hardware, methods or processes, computer software on computer-accessible media, and systems.

[0009] In one aspect, a system disclosed herein may include a wearable fitness monitor including one or more motion sensors and one or more processors configured to calculate a user-specific musculoskeletal strain score for a user of the wearable fitness monitor, the one or more processors including the steps of receiving motion data acquired from the one or more motion sensors during a strength training activity, identifying a type of the strength training activity, identifying a set of the strength training activities including one or more repetitive movements, calculating a raw strength score indicative of musculoskeletal movement for each of the repetitive movements based on characteristics of the motion data, scaling the raw strength score to a maximum intensity at which the user performs the strength training activity to obtain a user strength score per repetition, the maximum intensity being indicative of the user's ability to perform the strength training activity based on the user's exercise history, and The user-specific musculoskeletal strain score can be calculated by performing the following steps: for each repetitive movement, calculating a personalized scale based on the ratio of the effective load for the user during the strength training activity to a predetermined load threshold for the user when performing the strength training activity; for each repetitive movement, calculating a musculoskeletal strain per repetitive movement as the product of the user strength score per repetitive movement and the personalized scale; calculating a musculoskeletal strain score for the strength training activity by summing the musculoskeletal strain per repetitive movement for all the repetitive movements in the set; and taking action based on the musculoskeletal strain score. Taking action may include transmitting the user-specific musculoskeletal strain score to a personal computing device associated with the user and displaying it to the user. Taking action may include generating a coaching recommendation for the user. Other embodiments of this aspect may also or instead include methods and / or computer program products.

[0010] In one aspect, a method disclosed herein may include receiving motion data from one or more motion sensors of a wearable fitness monitor worn by a user during a strength training activity; calculating an individualized musculoskeletal strain for the user during the strength training activity by adjusting an intensity associated with the motion data according to the user's exercise history associated with the strength training activity, a payload during the strength training activity, and the user's maximum volume associated with the strength training activity; and taking action based on the individualized musculoskeletal strain. Other embodiments of this aspect may further or alternatively include a computer program product including computer-executable code embodied in a non-transitory computer-readable medium, the computer-executable code, when executed on one or more computing devices, causing the one or more computing devices to perform one or more of the above steps. Other embodiments of this aspect may further or alternatively include a system including a wearable fitness monitor including one or more motion sensors and one or more processors configured to perform one or more of the above steps to calculate a user-specific musculoskeletal strain score for a user of the wearable fitness monitor.

[0011] In one aspect, a method disclosed herein may include receiving motion data from one or more motion sensors of a wearable fitness monitor worn by a user during a strength training activity; calculating a raw strength score for multiple repetitions of the strength training activity based on the motion data; calculating an effective load for the strength training activity based on one or more load parameters including at least a body weight of the user and a load add-on weight for the strength training activity; calculating a personalized musculoskeletal strain score based on a combination of the raw strength score calculated from the motion data and the effective load calculated based on the one or more load parameters; and presenting information to the user based on the personalized musculoskeletal strain score. Other embodiments of this aspect may further or alternatively include a system including a wearable fitness monitor including one or more motion sensors and one or more processors configured to calculate a user-specific musculoskeletal strain score for a user of the wearable fitness monitor by performing one or more of the above steps. [Brief explanation of the drawings]

[0012] The foregoing objects, features, and advantages of the devices, systems, and methods described herein will become apparent from the following description of specific embodiments thereof, as illustrated in the accompanying drawings. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the devices, systems, and methods described herein. Like reference numbers throughout the drawings generally identify corresponding elements.

[0013] [Figure 1] FIG. 1 illustrates a physiological monitoring device.

[0014] [Figure 2] FIG. 1 illustrates a physiological monitoring system.

[0015] [Figure 3]FIG. 1 illustrates a detection system.

[0016] [Figure 4] FIG. 1 illustrates a method for generating and using MSK load data.

[0017] [Figure 5] FIG. 1 illustrates a load-repetitive motion profile for scaling the intensity of repetitive exercise.

[0018] [Figure 6] FIG. 1 shows a graph of velocity over time measured during a bench press exercise.

[0019] [Figure 7] FIG. 1 illustrates triaxial acceleration data for one repetition of a bench press exercise.

[0020] [Figure 8] FIG. 8 shows the integral of the normalized acceleration magnitude of the data of FIG. 7.

[0021] [Figure 9] FIG. 1 illustrates a system for monitoring MSK burden.

[0022] [Figure 10] FIG. 1 illustrates a system for monitoring MSK burden.

[0023] [Figure 11] FIG. 1 illustrates a system for monitoring MSK burden.

[0024] [Figure 12] FIG. 1 shows the user interface of a strength training system with MSK strain scoring functionality.

[0025] [Figure 13]FIG. 1 shows the user interface of a strength training system with MSK strain scoring functionality. DETAILED DESCRIPTION OF THE INVENTION

[0026] [explanation] Various embodiments will now be described in more detail below with reference to the accompanying drawings, in which preferred embodiments are shown. However, the above should not be construed as being limited to the embodiments described herein, as they may be embodied in many different forms. Rather, the illustrated embodiments are provided so that this disclosure will convey its scope to those skilled in the art.

[0027] All documents mentioned herein are incorporated herein by reference in their entirety. Reference to a singular item should be understood to include the plural item, and vice versa, unless expressly stated otherwise or apparent from the context. Grammatical conjunctions are intended to express all disjunctive and conjunctive combinations of joined clauses, sentences, words, etc., unless stated otherwise or apparent from the context. Thus, the word "or" should generally be understood to mean "and / or," etc.

[0028] The recitation of ranges of values ​​herein is not intended to be limiting and, unless otherwise specified, refers individually to every value within that range, and each value within such range is incorporated herein as if it were individually set forth herein. Words such as "about," "approximately," and the like associated with numerical values ​​are to be interpreted as indicating a degree of deviation that one skilled in the art would recognize as sufficient to function for the intended or stated purpose. Similarly, approximation words such as "approximately" or "substantially" used in connection with physical properties should be interpreted as indicating a range of deviation that one skilled in the art would recognize as sufficient to function for the corresponding use, function, purpose, etc. Ranges of values ​​and / or numerical values ​​are provided herein as examples only and do not limit the scope of the described embodiments. When ranges of values ​​are presented, unless expressly stated otherwise, it is intended that such ranges include each value within the range as if it were individually set forth. The use of any examples or exemplary language (such as "for example," "e.g.," etc.) provided herein is intended only to better illustrate the embodiments and does not limit the scope of the disclosed embodiments. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the embodiments.

[0029] In the following description, it will be understood that terms such as "first," "second," "upper," "lower," "above," "below," "upper," "under," etc. are terms of convenience and should not be construed as limiting terms unless specifically stated otherwise.

[0030] As used herein, the term "user" refers to any type of animal, human or non-human, having physiological information that can be monitored using the exemplary wearable physiological monitoring system.

[0031] The term "continuous" as used herein with respect to heart rate data refers to acquiring heart rate data frequently enough to detect individual heartbeats, and also refers to collecting heart rate data over extended periods of time, such as an hour, a day, or longer (including acquisition throughout the day and night). More generally, "continuous" or "continuously," with respect to physiological signals that may be monitored by a wearable device, is understood to mean continuous at a rate and duration appropriate for intended time-based processing, and physically, at a periodic rate sufficient to resolve desired physiological characteristics, such as heart rate, heart rate variability, heart rate peak detection, pulse shape, etc. (e.g., multiple times per heartbeat, breath, etc.). At the same time, continuous monitoring is not intended to exclude interruptions in normal data acquisition, such as sudden movement, changes in external lighting, loss of power, physical manipulation and / or adjustment by the wearer, temporary movement of the monitoring hardware due to external forces, etc. It should also be noted that heart rate data or monitored heart rate in this context may more generally refer to raw sensor data, such as light intensity signals, or to processed data, such as heart rate data, signal peak data, heart rate variability data, or other physiological or digital signals suitable for recovering heart rate information as contemplated herein. Furthermore, such heart rate data is typically obtained over a historical period and may then be correlated with various other data or metrics related to, for example, sleep state, perceived athletic activity, resting heart rate, maximum heart rate, etc.

[0032] As used herein, the term "computer-readable medium" refers to a non-transitory storage medium, such as storage hardware, storage devices, or computer memory, accessible by a controller, microcontroller, microprocessor, computing system, or the like, or other modules or components or modules of a computing system, capable of encoding computer-executable instructions, software programs, and / or other data. A "computer-readable medium" may be accessed by a computing system or a module of a computing system to retrieve and / or execute computer-executable instructions or software programs encoded therein. Non-transitory computer-readable media include, but are not limited to, one or more types of hardware memory, non-transitory tangible media (e.g., one or more magnetic storage disks, one or more optical disks, one or more USB flash drives), virtual or physical computer system memory, physical memory hardware such as random access memory (DRAM, SRAM, EDORAM, etc.), and the like. Although not shown, any device or component described herein may include a computer-readable medium or other memory for storing program instructions, data, and the like.

[0033] FIG. 1 illustrates a physiological monitoring system. The system 100 may include a wearable monitor 104 configured for physiological monitoring. The system 100 may further include a removable and replaceable battery 106 for recharging the wearable monitor 104. The wearable monitor 104 may include a strap 102 or other retention system(s) for securing the wearable monitor 104 to a specific location on the wearer's body to acquire physiological data as described herein. For example, the strap 102 may include a thin elastic band formed of any suitable elastic material, such as rubber, or a woven polymer fiber, such as polyester, polypropylene, nylon, or spandex. The strap 102 may be adjustable to accommodate various wrist sizes and may include a latch, clasp, or the like for securing the wearable monitor 104 in a desired location for monitoring physiological signals. While a wrist-worn device is illustrated, it will be understood that the wearable monitor 104 may be configured for placement at any suitable location on the user's body based on the sensing means and nature of the signals to be acquired. For example, the wearable monitor 104 may be configured for use on the wrist, ankle, biceps, chest, or other suitable location, and the strap 102 may be or include a waistband or other elastic band within a garment or accessory. The wearable monitor 104 may also or alternatively be structurally configured for placement on or within a garment, e.g., permanently or in a removable and replaceable manner. To this end, the wearable monitor 104 may be shaped and sized to be placed within a pocket, slot, and / or other housing coupled to or embedded within the garment. In such a configuration, the garment pocket or other retention means may include a sensing window or the like so that the wearable monitor 104 can operate even when used within the garment.Non-limiting examples of suitable wearable monitor 104 embodiments are described in US Pat. No. 11,185,292, which is incorporated herein by reference in its entirety.

[0034] System 100 may include any hardware components, subsystems, etc. for supporting various functions of wearable monitor 104, such as data collection, processing, display, and communication with external resources. For example, system 100 may include hardware for heart rate monitoring using photoplethysmography (PPG), electrocardiography, or other techniques. System 100 may be configured such that when wearable monitor 104 is worn on the wrist (or elsewhere on the body) during use, system 100 begins acquiring physiological data from the wearer. In some embodiments, pulse and / or rate may be acquired optically using a light source (e.g., a light-emitting diode (LED)) and a photodetector within wearable monitor 104. The LED may be positioned to direct light toward the user's skin, and a photodetector, such as a photodiode, may be used to acquire light intensity measurements indicative of light from the LED reflected and / or transmitted by the wearer's skin.

[0035] System 100 may be configured to record other physiological and / or biomechanical parameters, including, but not limited to, skin temperature (using a thermometer), galvanic skin response (GSR) (using a galvanic skin response sensor), movement (using one or more multi-axis accelerometers and / or gyroscopes), blood pressure, etc., as well as environmental or situational parameters, such as ambient light, ambient temperature, humidity, time of day, etc. For example, wearable monitor 104 may include sensors such as an accelerometer and / or gyroscope for motion detection, sensors for sensing ambient temperature, sensors for measuring electrodermal activity (EDA), sensors for measuring galvanic skin response (GSR) detection, etc. System 100 may additionally or alternatively include other systems or subsystems that support additional functionality of wearable monitor 104. For example, system 100 may include a communication system supporting near field communication (NFC), proximity sensing, Bluetooth® communication, Wi-Fi communication, cellular communication, satellite communication, etc. The wearable monitor 104 may additionally or alternatively include components such as a global positioning system (GPS), a display and / or user interface, a clock and / or timer, etc.

[0036] The wearable monitor 104 may include one or more battery power sources, such as a first battery within the wearable monitor 104 and a second battery 106 that is removable and replaceable from the wearable monitor 104 to recharge the battery within the wearable monitor 104. The system 100 may additionally or alternatively include multiple wearable monitors 104 (and / or other physiological monitors) that can share battery power or power each other. The system 100 may perform numerous functions related to continuous monitoring, such as automatically detecting whether the user is asleep, awake, exercising, etc. These detections may be performed locally on the wearable monitor 104 or by a remote service communicatively coupled to and receiving data from the wearable monitor 104. In general, the system 100 may support continuous and independent monitoring of physiological signals, such as heart rate, and the underlying acquired data may be stored on the wearable monitor 104 for an extended period of time before being uploaded to a remote processing resource for more complex computational analysis.

[0037] In one embodiment, the wearable monitor may be a wrist-worn photoplethysmography device.

[0038] 2 illustrates a physiological monitoring system. More specifically, FIG. 2 illustrates a physiological monitoring system 200 that may be used in any of the methods or apparatus described herein. Generally, the system 200 may include a physiological monitor 206, a user device 220, a remote server 230 with remote data processing resources (such as any of the processors or processing resources described herein), and one or more other resources 250, all of which may be interconnected via a data network 202.

[0039] Data network 202 may be any of the various data networks described herein. For example, data network 202 may be any network or internetwork suitable for communicating data or information between participants in system 200. This may include public networks such as the Internet, private networks, telecommunications networks such as the Public Switched Telephone Network (PSTN), or cellular networks using third-generation (e.g., 3G or IMT-200), fourth-generation (e.g., LTE (E-UTRA) or WiMAX-Advanced (IEEE 802.16m)), fifth-generation (e.g., 5G), and / or other technologies, as well as various enterprise or local area networks and other switches, routers, hubs, gateways, etc., that may be used to transmit data between participants in system 200. This may further include local or short-range communications infrastructure suitable, for example, for coupling physiological monitor 206 to user device 220 or for supporting communication with local resources. By way of non-limiting example, short-range communication may include Wi-Fi communication, Bluetooth communication, infrared communication, NFC (near field communication), communication with an RFID tag or reader, and the like.

[0040] The physiological monitor 206 may generally be any physiological monitoring device or system, such as any of the wearable monitors or other monitoring devices or systems described herein. In one embodiment, the physiological monitor 206 may be a wearable physiological monitor shaped and sized to be worn on the wrist or other body part. The physiological monitor 206 may include a wearable housing 211, a network interface 212, one or more sensors 214, one or more light sources 215, a processor 216, a haptic device 217 or other user input / output hardware, memory 218, and a strap 210 for holding the physiological monitor 206 in a desired position on the user. In one embodiment, the physiological monitor 206 may be configured to acquire heart rate data and / or other physiological data from the wearer intermittently or substantially continuously. In another embodiment, the physiological monitor 206 may be configured to support long-term, continuous acquisition of physiological data, for example, over a period of several days, a week, or more.

[0041] The network interface 212 of the physiologic monitor 206 may be configured to communicatively couple the physiologic monitor 206 with one or more other components of the system 200. This communication may be direct, such as via a cellular data connection, or indirectly via a short-range wireless communication channel. The short-range wireless communication channel locally couples the physiologic monitor 206 to a wireless access point, router, computer, laptop, tablet, mobile phone, or other device. These devices may process data locally and / or relay data from the physiologic monitor 206 to a remote server 230 or other resource(s) 250 depending on the need or usefulness for obtaining and processing data from the physiologic monitor 206.

[0042] The one or more sensors 214 may include any of the sensors described herein or other sensors or subsystems suitable for physiological monitoring or support functions. By way of example and not limitation, the one or more sensors 214 may include one or more of a light source, a light sensor, an accelerometer, a gyroscope, a temperature sensor, a galvanic skin response sensor, a capacitance sensor, a resistive sensor, an environmental sensor (e.g., for measuring ambient temperature, humidity, lighting, etc.), a geolocation sensor, a global positioning system (GPS), a proximity sensor, an RFID tag reader, an RFID tag, a time sensor, an electrodermal activity sensor, etc. The one or more sensors 214 may be located within the wearable housing 211 or may be located elsewhere and configured to provide physiological monitoring or other functions described herein. In one embodiment, the one or more sensors 214 may include a photodetector configured to provide light intensity data to the processor 216 (or to the remote server 230) for calculating heart rate and heart rate variability. The one or more sensors 214 may additionally or alternatively include an accelerometer, a gyroscope, etc. configured to provide motion data to the processor 216, for example, to detect activity such as sleep state, rest state, wake-up event, movement, and / or other user activity. In one embodiment, the one or more sensors 214 may include a sensor for measuring the user's electrodermal response. The one or more sensors 214 may additionally or alternatively include electrodes, etc. for capturing electrical signals, for example, to obtain an electrocardiogram and / or other electrically derived physiological measurements.

[0043] The processor 216 and memory 218 may be any of the processors and memories described herein. In one embodiment, the memory 218 may store physiological data obtained by monitoring the user using one or more sensors 214, and / or other sensor data, program data, or other data useful in the operation of the physiological monitor 206 or other components of the system 200. While only memory 218 is shown in the physiological monitor 206, it will be understood that any other device or component of the system 200 may additionally or alternatively include memory for storing program instructions, raw data, processed data, user input, etc. In one embodiment, the processor 216 of the physiological monitor 206 may be configured to obtain heart rate data from the user, for example, including raw data from the sensors 214 or based on such raw data. The processor 216 may also or alternatively be configured to determine or assist in determining a state of the user, for example, related to health, fitness, strain, restorative sleep, or any of the other conditions described herein.

[0044] One or more light sources 215 may be coupled to the wearable housing 211 and controlled by the processor 216. At least one of the light sources 215 is directed toward the user's skin adjacent the wearable housing 211. Light from the light sources 215, or more generally, light of one or more wavelengths of the light sources 215, may be detected by one or more of the sensors 214 and may be processed by the processor 216 as described herein.

[0045] System 200 may further include a remote data processing resource executing on remote server 230. The remote data processing resource may include any of the processors and associated hardware described herein and may be configured to receive data transmitted from memory 218 of physiological monitor 206 and process the data to detect or infer physiological signals of interest, such as heart rate, heart rate variability, respiratory rate, blood oxygen saturation, blood pressure, etc. Remote server 230 may additionally or alternatively assess user status, such as recovery status, sleep status, exercise activity, exercise type, sleep quality, daily activity strain, or any other health or fitness status that may be detected based on such data.

[0046] The system 200 may include one or more user devices 220. The user device 220 may cooperate with the physiological monitor 206 to, for example, provide a display for user data and analysis, or more generally provide user input / output, and / or provide a communications bridge from the network interface 212 of the physiological monitor 206 to the data network 202 and the remote server 230. For example, the physiological monitor 206 may locally communicate with the user device 220, such as a user's smartphone, via short-range communications, such as Bluetooth, to exchange data between the physiological monitor 206 and the user device 220. The user device 220 may then communicate with the remote server 230 via the data network 202 to transfer data from the physiological monitor 206 and receive analysis results from the remote server 230 for presentation to the user. In one embodiment, the user device(s) 220 may support physiological monitoring by processing or pre-processing data from the physiological monitor 206 to extract heart rate data and heart rate variability data from the raw data acquired by the physiological monitor 206. In another embodiment, the remote server 230 may have greater memory capacity and processing power than the physiological monitor 206 and / or the user device 220, and computationally intensive processing may be advantageously performed on the remote server 230.

[0047] The user device 220 may include any suitable computing device, including, but not limited to, a smartphone, a desktop computer, a laptop computer, a network computer, a tablet, a mobile device, a personal digital assistant (PDA), a mobile phone, a portable media or entertainment device, or any other computing device described herein. The user device 220 may include a user interface 222 to enable user access and analysis of data and / or to support user control of the operation of the physiological monitor 206. The user interface 222 may be managed by one or more applications executing locally on the user device 220. Alternatively, the user interface 222 may be provided and displayed remotely on the user device 220, for example, from a remote server 230 or one or more other resources 250.

[0048] Generally, the remote server 230 may include data storage, a network interface, and / or other processing circuitry. The remote server 230 may process data from the physiological monitor 206, perform physiological and / or health monitoring / analysis or other analyses described herein (e.g., sleep analysis, strain determination, recovery assessment, etc.), and may include a user interface for remotely accessing this data (e.g., from the user device 220). The remote server 230 may include a web server or other programmatic front end to facilitate web-based access by the user device 220 or physiological monitor 206 to the functions of the remote server 230 and other components of the system 200.

[0049] System 200 may include other resources 250, such as any resources that can be usefully used in the devices, systems, and methods described herein. For example, these other resources 250 may include other data networks, databases, processing resources, cloud data storage, data mining tools, computational tools, data monitoring tools, algorithms, etc. In another embodiment, other resources 250 may include one or more administrative or programmatic interfaces through which a human actor, such as a programmer, researcher, annotator, editor, analyst, or coach, can interact with any of the above. Other resources 250 may also or instead include any other software or hardware resources that can be usefully used in the network applications contemplated herein. For example, other resources 250 may include a payment processing server or platform used to authorize payments for access, content, or option / feature purchases. In another embodiment, other resources 250 may include a certificate server or other security resource for a third party to verify identity or encrypt or decrypt data. In another embodiment, other resources 250 may include user device 220, wearable strap 210, or a desktop computer co-located with (e.g., on the same local area network or directly connected thereto via a serial or USB cable) remote server 230. In this case, other resources 250 may provide ancillary functions to various components of system 200, such as firmware upgrades, a user interface, and storage and / or pre-processing of data from physiologic monitor 206 before transmission to remote server 230.

[0050] Other resources 250 may additionally or alternatively include one or more web servers that enable web-based access to and from other participants in system 200. While other resources 250 (e.g., web servers) are depicted as separate network entities, it will be readily understood that other resources 250 (e.g., web servers) may additionally or alternatively be logically and / or physically associated with one of the other devices described herein. Other resources 250 (e.g., web servers) may also include or provide a user interface 222 for web access to remote server 230 or database or other resource(s) to facilitate user interaction with information (e.g., from physiological monitor 206 or user device 220) over data network 202.

[0051] In another embodiment, other resources 250 may include fitness equipment or other fitness infrastructure. For example, a strength training machine may automatically record the number of repetitions and / or the weight added during repetitions. These may be wirelessly accessible by physiological monitors 206 or other user devices 220. More generally, a gym may be configured to track a user's movement between machines and receive activity reports from each machine to track various strength training activities during a workout. Other resources 250 may also or instead include other monitoring equipment or infrastructure. For example, system 200 may include one or more cameras for tracking the movement of free weights and / or the user's body position during repetitive movements, such as strength training activities. Similarly, a user may wear or have embedded in their clothing tracking fiducial markers, such as visually identifiable objects for image-based tracking or wireless beacons for other tracking. In another embodiment, the weights themselves may be equipped with devices, such as sensors for recording and communicating detected movements and / or beacons for self-identification of type, weight, etc., to facilitate automatic detection and tracking of athletic activity by other connected devices.

[0052] 3 illustrates a sensing system. Generally, the system 300 may include a physiological monitor 302 with a processor 304, a light source 306, a first photodetector 308, a second photodetector 310, one or more accelerometers 312, one or more gyroscopes 318, and any other hardware or other components and systems suitable for physiological monitoring as described herein. In use, the physiological monitor 302 is placed against a surface 313 of a user's skin 314, where the light source 306 and sensors 308, 310 can be brought into contact with the skin 314 to acquire physiological data. While not shown, it will be understood that the position of the physiological monitor 302 may generally be maintained using any of the straps, clothing, etc. described herein.

[0053] The processor 304 may be any microprocessor, microcontroller, application specific integrated circuit (ASIC), or other processing circuit suitable for controlling the operation of the physiological monitor and acquiring physiological data, or may be a combination of the foregoing.

[0054] The light source 306 may include one or more light-emitting diodes or other light sources and may be disposed within the physiological monitor 302. The light source 306 may be positioned such that when the physiological monitor 302 is placed on the skin 314 during use, the light source 306 directs light toward the skin 314, which is reflected, as indicated by arrow 316, toward the sensors 308, 310, where the intensity can be measured. In one embodiment, the light source 306 may include a light-emitting diode that emits light in the infrared or near-infrared wavelength range. This provides good light transmission through human skin and facilitates low-power transmission of measurable light to the sensors 308, 310. However, other light sources and wavelengths may also or alternatively be used.

[0055] The sensors 308, 310 may be oriented to contact the skin 314 when the physiological monitor 302 is placed on the skin 314 during use and may be positioned such that the sensors 308, 310 capture light from the light source 306 reflected and / or transmitted by the skin. Generally, the sensors 308, 310 may include a photodiode, a photodetector, or any other sensor(s) responsive to light from the light source 306. This may include broadband optical sensors, narrowband optical sensors, filtered sensors, etc. Generally, the first sensor 308 may be positioned closer to the light source 306 than the second sensor 310 to facilitate detection of intensity differences at the measurement wavelength(s). For example, the first sensor 308 may be positioned 1-4 millimeters from the light source 306, and the second sensor 310 may be positioned 2-8 millimeters from the light source 306, i.e., approximately twice as far from the light source 306 as the first sensor 310.

[0056] Other spacings may additionally or alternatively be used depending, for example, on the intensity of the light source 306, the sensitivity of the sensors 308, 310, the force of contact of the physiological monitor 302 with the skin 314, the penetration of ambient light, the physiological measurement / characteristic of interest, etc. In one embodiment, the sensors 308, 310 may be positioned in a straight line away from the light source 306. This ensures consistency in comparative measurements, but is not strictly required. The sensor 308 may be offset in any direction from the light source 306, as long as both sensors are in contact with the skin 314 in a manner that allows light from the light source 306 to be captured through the skin 314. In another embodiment, the physiological monitor 302 may include one or more other light sources and / or light sensors. These may be positioned to improve the accuracy of contact detection and / or to provide redundancy or to support other measurements such as oxygenation or skin thickness. This may include light sources / sensors using different wavelength ranges, different illumination patterns, etc. In another embodiment, the two sensors 308, 310 may be positioned at different distances from the periphery of the physiological monitor 302, thereby allowing the sensors 308, 310 to obtain a value for the difference in the intensity of ambient light incident on the skin and transmitted through the skin to the sensors 308, 310.

[0057] During operation, the processor 304 obtains raw intensity data from the sensors 308, 310 and may perform local calculations such as pre-processing the raw data for heart rate measurement and assessing whether the physiological monitor 302 is properly positioned on the skin 314 when in use.

[0058] The accelerometer 312 may include, for example, one or more single-axis or multi-axis accelerometers. These accelerometers can effectively measure the movement of the physiological monitor 302 and support calculations such as automatic activity detection, device on / off assessment, and degree of musculoskeletal activation, as described herein. Other hardware that senses movement and orientation (e.g., one or more gyroscopes 318, inertial motion sensors, and / or other microelectromechanical systems (MEMS) sensors) may also or instead be used for these purposes. More generally, the physiological monitor 302 may include any additional components, subsystems, etc. suitable to support various forms of physiological monitoring and contextual data acquisition as described herein.

[0059] A method for calculating strain scores based on heart rate is described, by way of non-limiting example, in U.S. Patent No. 11,185,292, which is incorporated herein by reference in its entirety. In one aspect, additional methods and systems are disclosed herein for estimating musculoskeletal load based on motion patterns in a monitoring device and using this load to provide improved strain analysis, coaching recommendations, and the like. Musculoskeletal (MSK) strain captures a type of load that may be missed or underestimated when estimating strain based solely on heart rate. Motion data, such as accelerometer and gyroscope data from wearable devices, can be advantageously used to fill this gap and estimate muscle strain based on various motion parameters during strength training activities. While users can record weight, number of repetitive movements, and subjective strain, directly and objectively measuring MSK strain offers advantages such as reducing manual user data entry, reducing underreporting of high-strain events, and eliminating subjective variability from strain calculations. In one embodiment, the disclosed technology includes objectively quantifying the effort of strength training exercises (and other activities) and reporting corresponding strain metrics that can be used to understand the physiological impact of strength training activities. In another embodiment, the disclosed technology may be used to create coaching metrics, refine cardiac-based strain estimates, etc. In another embodiment, the disclosed technology may be used to monitor whether exercise technique or metrics (e.g., weights currently being used in strength training exercises) are appropriate for the intended training stimulus and / or user goals (e.g., toning, improving, weight loss, etc.).

[0060] FIG. 4 illustrates a method for calculating a musculoskeletal (MSK) strain score. Method 400 may be implemented, for example, in any of the devices and systems described herein, and may be implemented using computer code to perform some or all of the following steps: MSK strain can be assessed as two components: volume and intensity. As described herein, an objective measure of MSK strain can be obtained by creating metrics to measure each of these components during a strength training activity and combining them to calculate a single MSK strain score. The MSK strain score may be used for user reporting and / or coaching recommendations.

[0061] In the context of strength training, "volume" generally refers to the total amount of work performed during a particular workout or period. While there are various methods for calculating volume, one common method is to multiply the number of sets for a particular exercise by the number of repetitions in each set, and then multiply that by the weight lifted for each repetition. For example, if you perform three sets of 10 repetitions with 100 pounds, your volume is 3,000 pounds (3 sets x 10 repetitions x 100 pounds). Another way to consider volume is to simply count the total number of sets or repetitions performed for a particular muscle group or exercise during a workout. For example, if you perform five sets of five repetitions on the bench press, your volume is 25 repetitions (5 sets x 5 repetitions). Managing volume is important in strength training because it has a significant impact on recovery and progress. Too much volume can lead to overtraining and increased risk of injury. On the other hand, too little volume can result in insufficient stimulation for growth and improvement.

[0062] As described below, volume (i.e., the total amount of work performed in a strength training activity) can be objectively quantified for a particular user. This may be done, for example, by determining the effective load of the exercise for the user and comparing it to that user's maximum volume. The effective load may be based on other objective parameters, such as the physical weight lifted by the user (e.g., the weight of a barbell, the weight of dumbbells, the weight of a strength training machine, etc.), the user's body weight (the user's body weight may affect exercises such as push-ups, squats, and pull-ups, where some or all of the load is provided by the user's body weight), or a combination of these (e.g., a user performing pull-ups with an additional 10 pounds (approximately 4.5 kilograms) of weight). The user's maximum volume may be estimated, for example, based on a statistical inference that performing repetitive movements with a certain weight / volume increases the likelihood of injury.

[0063] The concept of intensity presents various computational challenges. In strength training, "intensity" generally refers to the amount of exercise effort or load relative to maximal capacity, or how hard a particular load is for a particular individual. It is often defined as a percentage of one-repetition maximum (1RM), which is the maximum weight that can be lifted during one repetition of a particular exercise. For example, if a user with a 1RM of 200 pounds on the bench press lifts 150 pounds, they are training at an intensity that is 75% of their 1RM (150 divided by 200). Another way to measure intensity, especially in methods such as high-intensity interval training (HIIT), is by measuring perceived exertion—how hard the exercise feels. While this can be somewhat subjective, tools like the Borg Rating of Perceived Exertion (RPE) scale can help quantify intensity.

[0064] To capture intensity (e.g., measured muscle capacity relative to maximum), body movement during exercise may be tracked with a wearable monitor, which may then be used to objectively calculate a movement-based intensity for a particular user and type of exercise. Because different users have different abilities, this movement-based metric may be scaled according to the user's exercise history to determine how much effort the user exerted relative to maximum during exercise. In some cases, it may be useful to synthesize intensity based on other data. For example, some exercises do not involve movement detectable by a wearable monitor, such as training performed on a leg curl or leg extension machine while using a wrist-worn monitor. In such cases, the user may report the weight and number of repetitions, and intensity may be estimated based on the user's history. Alternatively, some exercises do not involve any movement at all. For example, isometric exercises such as planks and wall sits require the user to remain stationary. For these exercises, the "number of repetitions" may be derived based on the amount of time the exercise is performed. For example, a plank may be expressed as one repetition every six seconds. In some cases, both of these approaches may be used. For example, a user may be performing an isometric exercise that places strain on muscle groups whose movement (e.g., strain-induced tremors) cannot be detected from the position of the wearable monitor. In some cases, muscle tremors due to isometric strain may be detectable by the wearable monitor, and muscle tremors may be used to assess strain even in the absence of any intentional movement by the user.

[0065] An exemplary method for calculating musculoskeletal strain based on objective measures of strength and volume is described below.

[0066] As shown in step 404, the method may include receiving user data. In one embodiment, this may include receiving input from a user, such as weight, added weight, and other parameters for evaluating a strength training activity. This may include manually entered information, such as weight, number of repetitions, and type, for one or more strength training activities. For manual data entry, this may be entered by the user into a user device before, during, and / or after a workout, or into a wearable monitor with an appropriate user interface for corresponding data entry. For some activities, it may be difficult, impossible, or inconvenient for the wearable monitor to track the exercise. For example, if a user is performing isometric exercise that does not place strain on muscles near the wearable monitor, it may be difficult or impossible for the wearable monitor to automatically measure the exercise. In such cases, the user may manually enter some or all of the relevant data to support MSK strain analysis during the workout.

[0067] In another embodiment, some or all of the user data describing a particular workout or strength training activity may be derived automatically from motion data acquired, for example, by a wearable monitor or smart gym equipment. For example, the wearable monitor may detect the type of activity based on characteristics of the motion data acquired by the wearable monitor during exercise. The wearable monitor may also or instead detect individual repetitions within a set. In another embodiment, the user may provide input for each repetition or set to clearly delineate between activities. Using this, a system such as that described herein can more easily identify sets and repetitions within sets.

[0068] In other embodiments, data may be obtained from other equipment. For example, a strength training machine may count repetitions and wirelessly or otherwise report these repetitions to a wearable monitor or other user device. The strength training machine may also or instead report the amount of weight for a particular set of repetitions. This amount of weight may be obtained by the wearable monitor or other device and used to support MSK strain calculations as described herein. In other embodiments, a camera may be used to track the user's movements and / or equipment, and strength training data may be derived from the camera images. For example, image processing may be applied to identify activity, count repetitions, evaluate form, or identify added weight. In other embodiments, scales or tags may be attached to the weights to enable acquisition of motion data, weight data, etc. directly from the weights. Various combinations may be used. For example, a camera may be used to count repetitions, while simultaneously tagging the weights to enable automatic identification or determination of load.

[0069] In one embodiment, the user data includes the user's historical exercise data, which may be obtained, for example, from a remote server or other resource and may be used to support MSK score calculation. For example, this may include obtaining a user's load-repetitive motion profile, such as the load-repetitive motion profile shown in FIG. 5, based on the user's history of strength training activities. The load-repetitive motion profile generally indicates a user's ability to perform repetitive motions under one or more loads during strength training activities and may be used to scale the user's raw strength based on motion data from the wearable device. While using the velocity of repetitive motion to detect approach to maximum strength is known in the art, the load-repetitive motion profile contemplated herein advantageously uses acceleration data to facilitate intensity estimation using data from sensors in the wearable device. If the user's load-repetitive motion profile is unavailable, method 400 may include creating a load-repetitive motion profile. Method 400 may further include updating the profile as needed. For example, the method may include adding a row to the load-repetitive motion profile when the user performs a strength training activity under a new load not included in one or more loads in the load-repetitive motion profile. Method 400 may also or alternatively include adding a column when the user performs a new number of repetitive movements or updating the load-repetitive movement profile when the user exceeds the number of repetitive movements or load in the load-repetitive movement profile.

[0070] In another embodiment, the user's maximum volume may be obtained, or the user's maximum volume may be estimated based on other obtained user data. Calculating the maximum volume of exercise can be somewhat complex and individualized, as it depends on a variety of factors, including the user's current fitness level, the specific exercise, training goals, and how the user responds to various training volumes. As used herein, maximum volume is intended to indicate the upper threshold at which a user can perform repetitive movements of a strength training activity without injury. Various techniques may be used to estimate this threshold. For example, in the absence of specific user data, linear regression can be used on a population of users to derive a formula relating maximum volume to body weight (maximum volume = a * body weight + b), and this maximum value can be used as an estimate until user data is available. With a substantial user-specific sample, it is convenient to calculate the maximum safe load volume as a baseline or average volume per workout plus two times the standard deviation of the workout volume. In another embodiment, a range of techniques may be used based on the number of user-specific volume measurements available. More generally, any useful technique for estimating a non-injury volume threshold or limit (e.g., a threshold below which effort indicates an acceptable risk of injury) can be used to calculate a maximum volume for scaling volume per workout as described herein.

[0071] As shown in step 406, the method may include receiving motion data. This may include, for example, receiving raw motion data from one or more motion sensors of a wearable monitor, such as a wearable fitness monitor, worn by a user during a strength training activity involving one or more sets of repetitive movements. The raw motion data may include raw motion data from at least one gyroscope and at least one accelerometer of the wearable monitor. More generally, the raw motion data may include any motion data from the motion sensors, including angular rotation data from multiple gyroscopes and linear acceleration data from multiple accelerometers. In another embodiment, receiving motion data may include receiving motion data from multiple body parts. For example, when a user wears dual wristbands and / or ankle bands and / or when the user wears smart clothing with appropriate motion sensors on various body parts. Motion data may also or instead be received from other sources, such as other external motion sensors, a smartwatch or other wearable computing device, an external camera for measuring movement, etc.

[0072] As indicated at step 408, the method 400 may include processing the motion data.

[0073] In one aspect, this may involve fusing raw motion data from one or more motion sensors to mitigate gravity artifacts, thereby providing motion data including three-axis acceleration data. Data fusion, particularly data fusion using sensor fusion techniques, may help mitigate the effects of gravity on accelerometer measurements. A three-axis accelerometer measures both dynamic acceleration (due to movement) and static acceleration (due to the constant force of gravity pulling the device downward). Other sensors, such as gyroscopes and magnetometers, may be used in conjunction with the accelerometer to separate artifacts caused by gravity from motion-related data. One common technique for this is the use of Kalman filters or extended Kalman filters. These are recursive algorithms that use a series of measurements (in this case, readings from the accelerometer and gyroscope / magnetometer) observed over time to generate more accurate estimates of unknown variables than those based on a single measurement alone. Another common technique is the use of complementary filters, such as Mahoney filters or Madgwick filters. These algorithms combine accelerometer and gyroscope data to provide more stable, accurate, and error-free orientation measurements, even in the presence of constant gravity. More generally, by taking readings from multiple sensors and combining them, the measured acceleration can be separated into acceleration due to motion and acceleration due to gravity, thereby reducing the effects of gravity artifacts on accelerometer measurements. Any such technique can be used to process motion data and obtain three-axis acceleration data as described herein.

[0074] In general, "motion data," as used herein, may refer to raw motion data from a sensor, fused motion data as described above, filtered motion data, or other raw or processed data from a wearable monitor that represents movements by a wearer. Thus, in one embodiment, the motion data may include raw motion data from a three-axis gyroscope and a three-axis accelerometer. In another embodiment, the motion data may include any such raw data that has been fused to reduce the effects of acceleration due to gravity and provide three-axis acceleration data for repetitive movements.

[0075] As shown in step 410, method 400 may include identifying an activity. More specifically, this may include identifying the type of strength training activity the user is performing. In one embodiment, this may include receiving user input, for example, at a user interface on a user device, specifying the type of strength training activity. In another embodiment, this may include automatically identifying the type of strength training activity based on motion data, such as raw motion data or triaxial acceleration data, or other motion data derived from or based on the raw motion data. The motion data may also or alternatively be obtained from other sources, such as a camera capturing images of the image or weights or weight training equipment in an integrated motion sensor. Identifying the activity may include applying any suitable activity recognition algorithm, such as a machine learning algorithm, a statistical classification scheme, or the like, to motion data obtained from a wearable monitor or other source. In another embodiment, method 400 may include attempting automatic type detection and prompting for user input if the automatic detection cannot reliably (e.g., with sufficient statistical confidence) identify the type. In one embodiment, method 400 may include continuously tracking movement and attempting to identify known patterns of strength training activity, while in another embodiment, identification may be attempted only during known workout periods or upon explicit user request.

[0076] If activities are automatically identified, additional processing may be advantageously applied. For example, activities may be evaluated to determine whether each repetitive movement was properly completed using the full intended range of motion. Also, certain characteristics of the repetitive movement may additionally or alternatively be used to measure effort at the repetitive movement level. For example, repetitive movements that vary in speed, or that include trembling movements, or that are stopped and then aborted may indicate a greater musculoskeletal load than would be expected from a smooth repetitive movement performed at the same pace as previous repetitive movements. Such variability is often observed in motion data. Various statistical measures, such as mean signal amplitude, standard deviation, and rate of change, can be used to quantify these variabilities. Indices of intensity may then be created based on various statistical quantifications of the motion signal, making it possible to distinguish between different levels of musculoskeletal load.

[0077] As shown in step 412, method 400 may include identifying the amount of repetitive motion within a set of strength training activities. In one embodiment, this may include receiving user input specifying the number of repetitive motions within the set, or determining the amount of such repetitive motion based on the user input. In another embodiment, this may include determining the amount of repetitive motion within the set based on the variability in the magnitude of the three-axis acceleration data, or determining the amount of repetitive motion within the set based on the user input. For example, as shown in FIG. 6 , changes in velocity over time can be derived from the three-axis acceleration data and may exhibit certain periodic characteristics indicative of repetition of a repetitive motion. Thus, velocity data can be used to support automatic detection of repetitive motion for certain types of exercise. Cameras or other tracking devices / systems may also or instead be used to identify activities and / or the number of repetitive motions within an activity. In another embodiment, this may include using other data sources or techniques to detect the amount of repetitive motion within the set. This may include, for example, receiving repetitive motion counts from exercise equipment, extracting repetitive motion count information from video images of the activity (e.g., captured by a smartphone, camera, or other image source), or receiving motion data from any of the other sources described herein.

[0078] As shown in step 414, the method may include calculating intensities for the set.

[0079] In one embodiment, this may include calculating a raw strength score indicative of musculoskeletal movement based on changes in triaxial acceleration data for each of the repetitive movements. As previously mentioned, strength measures the ratio of strain to a user's capacity. This raw strength measure may be assessed based on the motion data. For example, for a series of acceleration measurements taken during a repetitive movement, strength can be assessed by calculating the difference between one instantaneous acceleration measurement and the next instantaneous acceleration measurement (also known as "jerk" or the change in acceleration between the two measurements) and dividing this amount by the magnitude of acceleration. The strength of the repetitive movement can then be calculated by averaging the instantaneous strength for all samples during the contraction phase of the repetitive movement, as follows:

number

number

[0080] As will be appreciated, other indices for measuring strength may be derived based on the movement. For example, in one embodiment, the strength of a single repetitive movement may be calculated as follows:

number

[0081] Depending on the type of exercise, this latter approach may be less sensitive to small changes in acceleration, i.e., small jerks, or changes in movement occurring at contraction phase boundaries. Therefore, in one embodiment, calculating a raw strength score for one of the repetitions in a set includes calculating an average of multiple instantaneous strength measurements for one of the repetitions. In one embodiment, one or more of the multiple instantaneous strength measurements are calculated based on an average of the ratio of discretely measured changes in current acceleration to the current acceleration. In another embodiment, one or more of the multiple instantaneous strength measurements are calculated based on the ratio of a first average of current acceleration changes to a second average of current acceleration. More generally, any metric that objectively characterizes strength based on changes in movement during a single repetition of a strength training activity and / or across a set of multiple repetitions may additionally or alternatively be used to measure strength and calculate musculoskeletal strain as contemplated herein. A significant advantage of acceleration-based strength measurements is their ability to capture the spatial extent of movement, which directly correlates to the workout performed, and their ability to capture variability in movement, which may indicate a high level of strain in a person. These relatively fast, sporadic movements outside the general path of movement will yield a quantitatively high intensity score due to the accumulated acceleration changes caused by tremors. For activities where movement cannot be measured directly, a proxy for intensity may be used, such as the duration of static isometric movements. Even in the absence of such significant muscle movement, muscle tremors may manifest in a way that can be detected, measured, and used to quantitatively assess intensity.

[0082] As described above, calculating the intensity may further include adjusting the raw intensity score based on the user's activity history. To personalize the intensity in this manner, method 400 may include determining the maximum intensity at which the user performs the strength training activity for each repetition in the set (or for the entire set). The maximum intensity may indicate the user's ability to perform the strength training activity based on the user's exercise history. Various techniques can be used to assess or estimate this maximum capacity and adjust the raw intensity score accordingly. For example, adjusting the raw intensity score may include deriving a scaling factor from a load-repetitive motion profile (e.g., the load-repetitive motion profile shown in FIG. 5). The load-repetitive motion profile characterizes a set of repetitive motions relative to the user's maximum capacity over a range of loads and repetitive motion counts. The intensity score for the set of repetitive motions may be expressed as the product of the (motion-based) raw intensity score and a scaling factor indicating the user's maximum ability to perform repetitive motions of an exercise under a specific load. In another aspect, the scaling factor may be estimated or interpolated based on the maximum load observed or user-reported for a particular exercise, or the maximum number of repetitive movements observed or reported for multiple different loads. More generally, any technique suitable for quantitatively determining a user's maximum capacity for a type of strength training activity and / or scaling the observed activities relative to maximum capacity can be used to scale the raw strength scores for a set of repetitive movements to provide an strength score for the user. All of these techniques are intended to be within the scope of the present disclosure, so long as they support a useful calculation of a musculoskeletal strain score, as further described below.

[0083] It will also be appreciated that other techniques for calculating intensity are known in the art and can be adapted for use in a wearable fitness monitor such as described herein. For example, intensity can be assessed based on the speed, linearity, and / or continuity of the movement associated with the exercise, all of which can be usefully detected with a motion sensor as described herein. For example, the magnitude of load can be identified based on how clean the movement trajectory of the exercise is, in other words, the amount of noise (jitter) in the movement compared to an expected trajectory and / or the user's past trajectory. Various measures of linearity, continuity, and / or variability are known in mathematics and can be usefully used as estimates of movement-based intensity. In one embodiment, the amount of variation from an overall expected value and / or the amount of movement outside of an expected range of variation may be used for an individual. In another embodiment, local measures of variation in direction (e.g., amount and magnitude of change in direction) or speed (e.g., amount and magnitude of change in speed) may be used to identify when increasing load causes the movement to become less smooth or continuous. In one embodiment, load may be measured across the major muscle groups targeted by the exercise. For example, if a user is performing bicep curls, the load on the biceps can be estimated based on any of the above-mentioned movement components measured by a wrist-worn monitor. As another example, if a user is performing squats, a monitor worn on the thigh or calf can be used to estimate the load on various leg muscles. As yet another example, if a user is performing bodyweight exercises such as push-ups or dips, a monitor worn on the user's torso can be used to estimate strength based on the range of motion of the torso segments. Strength scores may also be generated contextually, as described herein, using additional data, such as a particular user's maximum or typical weight and other related training of the corresponding muscle groups.

[0084] As shown in step 416, method 400 may include updating the user profile. This may generally involve updating the load-repetitive motion profile or any other user profile as new data becomes available. In one aspect, this may involve adding columns or rows to the profile. In another aspect, this may involve updating other entries in the profile, such as when the current set of repetitive motions exceeds an expected maximum.

[0085] As shown in step 418, method 400 may include calculating a volume of the strength training activity. As described herein, volume generally refers to the total amount of work performed during a workout (or other period). Volume may be individualized for each user based on two factors: maximum volume and payload. Thus, method 400 may include, for example, estimating a maximum volume or other predetermined threshold for a user performing the strength training activity based on the user's historical performance in the strength training activity. The maximum volume (or estimated maximum volume) may indicate, for example, an upper threshold for a user to perform repetitive movements of the strength training activity without injury.

[0086] The method 400 may further include calculating a payload for the user during the strength training activity. The payload may indicate, for example, a relative portion of the maximum volume the user exerts during the strength training activity based on one or more load parameters. The one or more load parameters may include at least the user's body weight and an additional weight for the strength training activity. For example, calculating the payload may include calculating the payload based on a user input of the user's body weight, such as when the strength training activity is an activity such as pull-ups, push-ups, or squats that is based in whole or in part on the user's body weight. Calculating the payload may also be based on a user input of an additional weight for the strength training activity, such as when the user is using hand-held weights, barbell weights, or weights on a strength training machine, or when the user is adding weight to another activity (such as pull-ups, push-ups, or squats) to increase volume. In some cases, for example, when using a strength training machine, body weight may be ignored, and / or other load parameters may be included, such as arm length or leg length.

[0087] As shown in step 420, method 400 may include calculating a musculoskeletal strain score. For example, this may include calculating, for each repetition, the musculoskeletal strain per repetition as the product of a first ratio of the effective load to the maximum volume and a second ratio of the raw strength score to the maximum strength, and then summing the musculoskeletal strains per repetition for all repetitions in the set to provide a musculoskeletal strain score for the strength training activity. Generally, this may include calculating the musculoskeletal strain score on a user's personal computing device coupled in communication with the wearable monitor, or on a remote server coupled in communication with the wearable monitor, or a combination thereof.

[0088] Then, for each set of exercise, an MSK strain score can be calculated using the following formula:

number

[0089] where M is the number of repetitions in the set,

number

number

number

number

number

[0090] If you accumulate MSK for multiple sets of exercise, it will look like this:

number

[0091] where N is the number of exercise sets.

[0092] Finally, for sessions consisting of multiple exercises:

number

[0093] where L is the number of exercises in a session.

[0094] The maximum amounts and intensities may be tailored for each individual and may be changed as an individual's intensity changes over time.

[0095] These can be used to calculate MSK strain for each repetitive movement of an exercise. Individual MSK strain scores can be aggregated to generate a workout-level score. At this level, MSK strain may be aggregated at the muscle group level and / or the whole body level. If sensor data is available, intensity may be determined based on the acceleration or speed of the movement. If sensor data is not available, effort can still be determined based on, for example, previous workouts, demographic criteria, and / or self-reported load levels. Thus, the systems and methods described herein can perform MSK scoring using motion data, without motion data, or a combination thereof.

[0096] The raw MSK strains described herein are linearly accumulative scores with no limit. That is, the more effort expended in an activity or the more repetitive movements performed, the higher the score. To provide a bounded range for a user, these raw scores may be scaled to adjust their value based on the user's personal performance profile. In some embodiments, this may be achieved through a two-step process including (1) exercise-specific normalization and (2) performance normalization. An individual's daily MSK strain may be converted to a score, for example, on a scale of 0 to 21, or any other suitable range.

[0097] As shown in step 422, method 400 may include taking action based on the musculoskeletal (MSK) strain score. The MSK strain score provides a highly actionable indicator of strength training activity that takes into account context, such as the particular type of exercise, demonstrated performance history, and the user's estimated injury limitations. In one embodiment, taking action may include displaying (e.g., on a user device) the musculoskeletal strain score or other calculated indicator or analysis of the strength training activity for user viewing.

[0098] In another embodiment, taking action may include refining the user's daily strain calculation (such as a cardiac strain calculation based on heart rate and / or heart rate variability) based on the musculoskeletal strain score. For example, a strain calculation based on cardiovascular activity (e.g., as described in U.S. Pat. No. 11,185,292, incorporated herein by reference) may underestimate the actual strain during strength training activities. By determining the MSK strain during strength training or other exercise, the user's total daily strain can be updated to more accurately reflect strain due to cardiovascular strain as well as muscular strain.

[0099] In another embodiment, taking action may additionally or alternatively include calculating multiple musculoskeletal strain scores for each of multiple types of strength training activities in the exercise routine, which may be aggregated into a single MSK strain score for the entire workout routine comprised of multiple individual strength training activities and / or reported to the user as a single score or multiple scores.

[0100] Taking action may additionally or alternatively include generating coaching recommendations for the user based on the musculoskeletal strain score and / or displaying coaching recommendations to the user. This may include generating recommendations based on the user's stated objectives or fitness goals (e.g., recommending increases when appropriate, or recommending decreases when there are indications that the user is approaching their maximum volume or exceeding a recommended training limit). In one embodiment, the coaching recommendations may be real-time coaching recommendations presented to the user during a strength training activity. This may include, for example, recommendations to increase volume (e.g., adding more repetitions or weight) or a warning about approaching maximum volume. In another embodiment, the coaching recommendations may relate to a subsequent exercise activity by the user, such as a next activity in a current workout or the same activity (or a different activity) in a future workout. The coaching recommendations may additionally or alternatively include recommendations regarding the timing of the next strength training activity.

[0101] As mentioned above, a system for calculating a musculoskeletal strain score is also described herein. The system may include a wearable fitness monitor and one or more processors. The wearable fitness monitor may be any wearable monitor described herein and may include one or more motion sensors. The one or more processors may include, for example, a processor on the wearable fitness monitor, a processor on a user device, a processor on a remote server, or a combination thereof. The one or more processors are configured by computer-executable code stored on a non-transitory computer-readable medium to perform the following steps: receive motion data acquired from the one or more motion sensors during a strength training activity; identify a type of the strength training activity; identify a set of the strength training activities including one or more repetitive movements; calculate, for each of the repetitive movements, a raw strength score indicative of musculoskeletal movement based on characteristics of the motion data; scale the raw strength score relative to a maximum intensity at which the user performs the strength training activity to obtain a user strength score per repetition; and scale the maximum intensity based on the user's exercise history. the individual strength score is indicative of the user's ability to perform the strength training activity based on the individual strength score, and for each of the repetitive movements, the individual strength score is indicative of the user's ability to perform the strength training activity based on the individual strength score; for each of the repetitive movements, the individual strength score is indicative of the user's ability to perform the strength training activity based on the individual strength score; calculating a musculoskeletal strain per repetitive movement for each of the repetitive movements as the product of the per repetitive user strength score and the individual strength score; calculating a musculoskeletal strain score for the strength training activity by summing the musculoskeletal strain per repetitive movement for all the repetitive movements in the set; and taking action based on the musculoskeletal strain score.

[0102] In one aspect, taking action may include transmitting the user-specific musculoskeletal strain score to a personal computing device associated with the user and displaying it to the user, hi another aspect, taking action may include generating coaching recommendations for the user.

[0103] In general, the methods described herein may include more or fewer steps than those described with reference to Figure 4, or may include variations of each of those steps. For example, in one embodiment, a method disclosed herein includes receiving motion data from one or more motion sensors of a wearable monitor worn by a user during a strength training activity including a set of one or more repetitive movements; identifying a type of the strength training activity; determining an amount of the repetitive movements in the set; calculating a raw strength score indicative of musculoskeletal movement for each of the repetitive movements based on characteristics of the motion data; scaling the raw strength score relative to a maximum intensity at which the user performs the strength training activity to obtain a user strength score per repetitive movement, and determining whether the maximum intensity is a user strength score based on the user's exercise history. the strength training activity score indicates the user's ability to perform the strength training activity; for each of the repetitive movements, calculating a personalized scale based on the ratio of the available load on the user during the strength training activity to a predetermined load threshold for the user performing the strength training activity; for each of the repetitive movements, calculating a musculoskeletal strain per repetition as the product of the user strength score per repetition and the personalized scale; calculating a musculoskeletal strain score for the strength training activity by summing the musculoskeletal strain per repetition for all of the repetitive movements in the set; and taking action based on the musculoskeletal strain score.

[0104] In another aspect, a method described herein includes receiving motion data from one or more motion sensors of a wearable fitness monitor worn by a user during a strength training activity; calculating an individualized musculoskeletal strain of the user during the strength training activity by adjusting an intensity associated with the motion data according to the user's exercise history associated with the strength training activity, an effective load during the strength training activity, and the user's maximum volume associated with the strength training activity; and taking action based on the individualized musculoskeletal strain.

[0105] In another aspect, a method described herein includes receiving motion data from one or more motion sensors of a wearable fitness monitor worn by a user during a strength training activity; calculating a raw strength score for multiple repetitions of the strength training activity based on the motion data; calculating an effective load for the strength training activity based on one or more load parameters including at least a body weight of the user and an added weight for the strength training activity; calculating an individualized musculoskeletal strain score based on a combination of the raw strength score calculated from the motion data and the effective load calculated based on the one or more load parameters; and presenting information to the user based on the individualized musculoskeletal strain score.

[0106] FIG. 5 illustrates a load-repetition profile for scaling the intensity of exercise repetitions. Generally, this profile 500 can be used to scale a user's raw intensity score based on their workout history. Typically, a maximum intensity value may be set to the user's maximum single repetition value (load 10, repetition 1 in FIG. 5). A scale can be created for all intensity values ​​for a particular person and exercise based on the maximum intensity for a single repetition. This may be done, for example, by interpolating an effort rate to scale the calculated intensity based on the maximum possible value for the exercise. The profile 500 may be adjusted based on newly observed maxed-out repetitions, and new rows may be added as the user adds new repetitions or new loads. More generally, the number of repetitions may be adjusted, recalculated, or re-interpolated as additional user data becomes available. In one embodiment, if a user exceeds the predicted maximum (e.g., by performing one or more repetitions under a load that exceeds the current maximum), the profile 500 may be adjusted before calculating the intensity, and the user's intensity for those repetitions may be calculated using the adjusted profile 500. This has the advantage that it avoids calculating intensity values ​​that exceed the user's theoretical maximum.

[0107] In another embodiment, an estimate of the intensity scale may be created first (e.g., based on weight or other factors) to support calculation of the user's intensity before the profile 500 is fully populated.

[0108] FIG. 6 is a graph showing velocity measured over time during a bench press exercise. In general, velocity may be derived from triaxial acceleration data or other motion data obtained from a wearable monitor or other motion data source. While a series of bench press exercises is shown, it will be understood that any other exercise involving measurable repetitive motion can be similarly detected. As shown, five large peaks 602 in the velocity data indicate five repetitive motions of the exercise. Vertical lines 604 have been added to indicate measurable landmarks indicating the end of each repetitive motion. Various signal processing techniques, such as frequency domain techniques and time domain peak detection, can be used to identify such repetitive motion. Any such technique suitable for identifying periodic cycles in velocity measurements indicative of repetitive motion of the exercise can be used to automatically detect repetitive motion as described herein.

[0109] Figure 7 shows triaxial acceleration data for one repetition of a bench press exercise. The data in Figure 7 is fused data that has been processed to reduce gravity-based acceleration artifacts. This data may be used, for example, to calculate a strength score and / or derive velocity data (e.g., velocity data as shown in Figure 6) that can be used to identify individual repetitions in a series of strength training activities.

[0110] Figure 8 shows the integral of the normalized acceleration magnitude of the data from Figure 7. This may be used, for example, to quantitatively assess exercise intensity, which may then be scaled according to maximum intensity, maximum volume, and effective load as described herein to obtain an MSK score for repetitive movements of a strength training activity.

[0111] FIG. 9 illustrates a system for monitoring musculoskeletal (MSK) strain. The system 900 may include a user 901 wearing a physiological monitor 910, a user device 920 with a display 922 suitable for providing information to the user 901, a data network 902 interconnecting one or more participants in the system 900, a server 930, and a database 940. Generally, FIG. 9 illustrates the user 901 performing an exercise (e.g., a weight training exercise such as a bicep curl using a barbell-shaped weight 902 with weighted plates). As described herein, the movement and / or physiological data detected by the physiological monitor 910 may be used to calculate an MSK strain score for the user 901. The MSK strain score may be displayed to the user (e.g., during and / or after the exercise) via the user device 920 along with other relevant information.

[0112] The physiological monitor 910 may include a wrist-worn photoplethysmography device. The physiological monitor 910 may additionally or alternatively include monitors placed on other parts of the user's 901 body, such as the biceps, thighs, or calves. In one embodiment, the physiological monitor 910 may include at least one accelerometer, gyroscope, or the like, for detecting movement of the user 901 and providing motion data. In other embodiments, the accelerometer data or other motion sensor data is obtained from a source external to the physiological monitor 910.

[0113] The user 901 may be performing an exercise, such as a strength training activity, in which case motion data (and / or physiological data) is provided to the user device 920 and / or server 930 for analysis. Generally, motion data acquired by motion sensors during exercise may be analyzed to derive an MSK strain score, as described herein. This score may be used, for example, to provide coaching information to the user 901, such as to adjust the exercise and / or provide other training recommendations. By way of example and not limitation, the user 901 is shown performing biceps curls using a barbell as weights 902. In this example, motion data obtained from the detected movements 904 may include three-axis acceleration data representing movement during the repetitive motion of the exercise. This may include motion data acquired by the physiological monitor 910 described above, motion data acquired by motion sensors in the weights 902 (e.g., the barbell and weights added thereto), motion data acquired by a camera in the user device 920, or motion data acquired from other suitable sources. While free weight exercise is depicted, it will be appreciated that the systems and methods described herein may also be used to calculate MSK strain during a variety of other strength training activities, such as weightlifting exercises (e.g., using free weights and / or weight training machines), bodyweight and isometric exercises (e.g., push-ups, sit-ups, squats, burpees, dips, leg raises, etc.), aerobic exercises (e.g., walking, running, cycling, swimming, elliptical exercise, circuit training, skipping rope, sports participation and / or training, dancing, etc.), etc. The motion data may also be used for other coaching recommendations, such as recommendations regarding speed, range of motion, form, etc. of repetitive movements.Thus, in one aspect, described herein are systems and methods for providing coaching recommendations to a user engaged in a strength training activity based on movements detected during the strength training activity, where the recommendations may relate to one or more of speed, range of motion, and morphology of the strength training activity.

[0114] The data network 902, the user devices 920, the servers 930, and the databases 940 may be any of those described herein. In general, the data network 902 may support communication between participants in the system 901. For example, motion data and / or physiological data sensed by the physiological monitors 910 may be provided to the server 930 or the user devices 920 for processing. Such data and / or analysis of that data may be stored in a database 940, as described herein. The database 940 may be a local database or a remote database. The database 940 may also or alternatively store user profiles, load-repetitive motion profiles, etc., as described herein.

[0115] The display 922 of the user device 920 may include a graphical user interface provided on the display 922 and configured to present information to the user 901. The information 924 displayed on the display 922 may include any of the outputs described herein, such as an MSK strain score 924, coaching recommendations, an exercise plan including several strength training activities, and the repetitions completed in a particular set.

[0116] In one use example, the system 900 may receive information related to an exercise being performed by the user 901, such as the type of exercise, a description or indication of sets or repetitions, a training goal, additional weight 902 or resistance being used, or information about the user 901 (e.g., height, weight, gender, etc.). In some embodiments, the information may include motion data from a physiological monitor 910 or other source(s).

[0117] FIG. 10 illustrates a system for monitoring MSK strain. System 1000 may include any of the features described herein, such as those described above with respect to FIG. 9. As shown in FIG. 10, system 1000 may include a weight training machine 1002. A physiological monitor 1010 may be placed on a user's leg to detect leg movement during leg strength training activities. In one embodiment, weight training machine 1002 may be a smart device that communicates data, such as current weight / load, number of repetitions, and range of motion, to physiological monitor 1010 or other system resources, which can be used to calculate an MSK strain score. Weight training machine 1002 may also or alternatively include a camera for capturing images that can be used to derive motion data.

[0118] FIG. 11 illustrates a system for monitoring MSK strain. System 1100 may include any of the features described herein. As shown, user 1101 is performing an isometric exercise, more specifically, a plank. This type of exercise may require certain modifications to strain scoring. For example, with a plank (or certain other isometric exercises), there is no literal repetitive motion. Instead, a measure of repetitions may be derived, for example, based on the time the exercise is maintained. Thus, for example, a plank may be counted as one repetitive motion every five seconds, and performing a plank for one minute may correspond to 12 repetitive motions. Similarly, even in the absence of periodic motions that can serve as the basis for automatic detection of repetitive motions, high loads may result in trembling in the shoulders, arms, or abdomen, which can be detected and used by the wearable physiological monitor 1102 to calculate the intensity of the strength training activity.

[0119] FIG. 12 illustrates a user interface for a strength training system with MSK strain scoring functionality (e.g., using the systems and methods described herein). In one embodiment, user interface 1200 may include several controls for setting up a workout routine, for example, by specifying the type of strength training activity and, if desired, the number of repetitions and weight for each strength training activity. Through this interface, a user can set up a workout routine by adding exercise sets within the routine, deleting exercise sets, and specifying exercise set details. The workout routine may then be saved for future use or used as a guide during a current workout.

[0120] FIG. 13 illustrates a user interface for a strength training system with MSK strain scoring functionality (e.g., using the systems and methods described herein). In one embodiment, the user interface 1300 may display an MSK strain score 1302 that quantitatively summarizes the user's amount of musculoskeletal strain for the current day or other suitable time period. The user interface 1300 may further display other useful information, such as current or most recent strength training activity, the percentage of cardiovascular strain versus muscular strain for the day, coaching recommendations, etc. In this context, the MSK strain score 1302 can provide the user with concise, quantitative, and objective feedback regarding their recent strength training activity based on information derived from identified activity and physical movement measurements.

[0121] In user interface 1300, the user can track a current workout, modify the current workout (e.g., by changing weights, repetitions, or activity), review past workouts, and even create new workouts. The user can also view relevant information representing time, weight, resistance, cardiovascular strain, etc. In another embodiment, user interface 1200 can provide interactive instructions for performing various types of exercises and can provide motion-based feedback regarding the user's form for a particular exercise.

[0122] The above-described systems, devices, methods, processes, etc. can be implemented in the form of hardware, software, or any combination thereof suitable for the control, data acquisition, and data processing described herein. This includes implementation in the form of one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors, or other programmable devices or processing circuitry, as well as internal and / or external memory. This may also or alternatively include one or more application-specific integrated circuits, programmable gate arrays, programmable array logic components, or other devices that can be configured to process electronic signals. Furthermore, it will be understood that implementations of the above-described processes or devices may include computer-executable code (which may be stored, compiled, or interpreted for execution on one of the above-described devices) created using a structured programming language such as C, an object-oriented programming language such as C++, or any other high- or low-level programming language (including assembly language, hardware description language, database programming language, and technology), as well as heterogeneous combinations of processors and processor architectures, or combinations of different hardware and software.

[0123] Thus, in one aspect, each of the above methods and combinations thereof may be embodied in the form of computer-executable code that performs its various steps when executed on one or more computing devices. In another aspect, these methods may be embodied in various systems that perform its steps, distributed among devices in various ways, or all functionality may be incorporated into a dedicated, stand-alone device or other hardware. The code may be stored in a non-transitory manner in computer memory. The computer memory may be memory from which a program is read and executed (e.g., random access memory associated with a processor), or may be a storage device such as a disk drive, flash memory, or any other optical, electromagnetic, magnetic, infrared, or other device, or combination of devices. In another aspect, any of the above systems and methods may be embodied in any suitable transmission or propagation medium that carries the computer-executable code and / or any input or output from the computer-executable code. In another aspect, the means for performing the various steps associated with the above processes may include any of the hardware and / or software described above. All such permutations and combinations are intended to be within the scope of this disclosure.

[0124] The various method steps of the embodiments described herein, unless expressly given a different meaning or otherwise clear from the context, are intended to include any suitable manner of causing such method steps to be performed, in a manner consistent with the patentability of the invention as set forth in the following claims. Thus, for example, performing step X may include any suitable manner of causing another person, such as a remote user, a remote processing resource (e.g., a server or cloud computer), or a machine, to perform step X. Similarly, performing steps X, Y, and Z may include any manner of directing or controlling any combination of such other people or other resources to perform steps X, Y, and Z, thereby obtaining the benefit of those steps. Thus, unless expressly given a different meaning or otherwise clear from the context, the various method steps of the embodiments described herein are intended to include any suitable manner of causing one or more other people or one or more other organizations to perform those steps, in a manner consistent with the patentability of the invention as set forth in the following claims. Such people or organizations need not be under the direction or control of any other people or organizations, nor need they be located in any particular jurisdiction.

[0125] It will be understood that the methods and systems described above are provided by way of example and not by way of limitation. Numerous variations, additions, omissions, and other modifications will be apparent to those skilled in the art. Furthermore, the order of presentation of method steps in the above description and drawings is not intended to require that the described steps be performed in that order unless a particular order is expressly required or apparent from the context. Thus, while specific embodiments have been shown and described, it will be apparent to those skilled in the art that various changes and modifications in form and detail can be made without departing from the spirit and scope of the present disclosure, and are also intended to form a part of the invention as defined in the following claims.

[0126] Exemplary embodiments of the present invention are listed below. 1. A computer program product comprising computer-executable code embodied in a non-transitory computer-readable medium, said computer-executable code, when executed on one or more computing devices, causing said one or more computing devices to: receiving raw motion data from one or more motion sensors of a wearable fitness monitor worn by a user during a strength training activity comprising a set of one or more repetitive movements, the raw motion data including angular rotation data from a plurality of gyroscopes and linear acceleration data from a plurality of accelerometers; fusing the raw motion data from the one or more motion sensors to reduce gravity artifacts, thereby providing motion data comprising three-axis acceleration data; identifying a type of said strength training activity; determining the amount of the repetitive motion in the set based on the variability in magnitude of the three-axis acceleration data; calculating a raw intensity score indicative of musculoskeletal movement based on changes in the three-axis acceleration data for each of the repetitive movements; determining, for each one of the repetitive movements, a maximum intensity at which the user performs the strength training activity, the maximum intensity being indicative of the user's ability to perform the strength training activity based on the user's exercise history; estimating a maximum volume of the strength training activity for the user based on a history of the user's performance at the strength training activity, the maximum volume indicating an upper threshold for the user to perform repetitive movements of the strength training activity without injury; calculating an effective load for the user during the strength training activity, the effective load indicating a relative portion of the maximum volume exerted by the user during the strength training activity based on one or more load parameters including at least a body weight of the user and an add-on weight for the strength training activity; For each one of the repetitive movements, calculating a musculoskeletal load per repetitive movement as the product of a first ratio of the payload to the maximum volume and a second ratio of the raw strength score to the maximum strength; summing the musculoskeletal strain per repetition for all the repetitive movements in the set to provide a musculoskeletal strain score for the strength training activity; displaying the musculoskeletal strain score of the strength training activity to the user; A computer program product that causes the 2. Code for causing the one or more computing devices to generate coaching recommendations for the user based on the musculoskeletal strain score. 10. The computer program product of claim 1, further comprising: 3. The computer program product of 2, wherein the coaching recommendations are based at least in part on fitness goals of the user. 4. A computer program product described in any one of 1 to 3, wherein calculating the raw intensity score for one of the repetitive movements includes calculating an average of multiple instantaneous intensity measurements for the one of the repetitive movements. 5. The computer program product of 4, wherein one or more of the plurality of instantaneous intensity measurements are calculated based on an average of a ratio of a discretely measured change in current acceleration to the current acceleration. 6. The computer program product of 4, wherein one or more of the plurality of instantaneous intensity measurements are calculated based on a ratio of a first average of a current acceleration change to a second average of a current acceleration. 7. further comprising code for causing the one or more computing devices to perform the step of creating a load-repetitive motion profile for the user based on a history of the strength training activity by the user; 7. The computer program product of any one of claims 1 to 6, wherein the load-repetitive motion profile indicates the user's ability to perform repetitive motions under one or more loads during the strength training activity. 8. Code for causing the one or more computing devices to perform the step of adding a row to the load-repetitive motion profile when the user performs the strength training activity under a new load that is not included in the one or more loads in the load-repetitive motion profile. 8. The computer program product of claim 7, further comprising: 9. Code for causing the one or more computing devices to perform a step of updating the load-repetitive behavior profile when the user exceeds a number of repetitive behaviors for one of the loads in the load-repetitive behavior profile. 8. The computer program product of claim 7, further comprising: 10. Code for receiving user input specifying the type of strength training activity. 10. The computer program product according to any one of claims 1 to 9, further comprising: 11. Code for causing the one or more computing devices to identify the type of strength training activity based on the raw motion data. 11. The computer program product according to any one of claims 1 to 10, further comprising: 12. Receive motion data from one or more motion sensors of a wearable monitor worn by a user during a strength training activity including one or more sets of repetitive movements; Identifying a type of strength training activity; determining the amount of the repetitive motions in the set; For each of the repetitive movements, calculating a raw strength score indicative of musculoskeletal movement based on the motion data characteristics, and scaling the raw strength score to a maximum intensity at which the user performs the strength training activity to obtain a user strength score per repetitive movement, the maximum intensity being indicative of the user's ability to perform the strength training activity based on the user's exercise history; calculating, for each one of the repetitive movements, a personalized scale based on a ratio of an effective load for the user during the strength training activity to a predetermined threshold load for the user performing the strength training activity; calculating a musculoskeletal strain per repetition for each of the repetitive movements as the product of the user strength score per repetition and the individualized scale; calculating a musculoskeletal strain score for the strength training activity by summing the per-repetition musculoskeletal strains for all of the repetitive movements in the set; Taking action based on said musculoskeletal strain score A method comprising: 13. The method of claim 12, wherein determining the amount of the repetitive motion in the set includes determining the amount based on motion data. 14. The method of claim 12 or 13, wherein determining the amount of the repetitive motions in the set includes determining the amount based on user input. 15. A method according to any one of claims 12 to 14, wherein the measure comprises refining the user's daily strain calculation based on the musculoskeletal strain score. 16. A method according to any one of claims 12 to 15, wherein the measure includes generating coaching recommendations for the user. 17. The method of claim 16, further comprising displaying the coaching recommendations to the user. 18. The method of claim 16 or 17, wherein the coaching recommendations are real-time coaching recommendations. 19. The method of claim 16 or 17, wherein the coaching recommendations relate to the user's subsequent athletic activity. 20. The method of any one of claims 12 to 19, further comprising automatically identifying the type of strength training activity based on the motion data. 21. The method of any one of 12 to 20, further comprising calculating a plurality of musculoskeletal strain scores for each of a plurality of types of strength training activities in the exercise routine. 22. The method of any one of claims 12 to 21, further comprising calculating the payload based on user input of the user's weight. 23. The method of any one of claims 12 to 22, further comprising calculating the payload based on user input of an additional weight for the strength training activity. 24. A method according to any one of claims 12 to 23, wherein the motion data comprises raw motion data from a three-axis gyroscope and a three-axis accelerometer fused together to provide three-axis acceleration data of the repetitive motion that reduces the effects of acceleration due to gravity. 25. The method of any one of claims 12 to 24, wherein the wearable monitor includes a wrist-worn photoplethysmography device. 26. A method according to any one of claims 12 to 25, wherein receiving the motion data includes receiving raw motion data from at least one gyroscope and at least one accelerometer of the wearable monitor. 27. A method according to any one of claims 12 to 26, wherein calculating the musculoskeletal strain score includes calculating the musculoskeletal strain score on the user's personal computing device that is communicatively coupled to the wearable monitor. 28. A method according to any one of claims 12 to 27, wherein calculating the musculoskeletal strain score includes calculating the musculoskeletal strain score on a remote server communicatively coupled to the wearable monitor. 29. A method according to any one of claims 12 to 28, wherein the predetermined load threshold is an estimated maximum volume indicating an upper threshold for the user to repeat the strength training activity without injury. 30. A wearable fitness monitor including one or more motion sensors; one or more processors configured to calculate a user-specific musculoskeletal strain score for a user of the wearable fitness monitor; A system comprising: the one or more processors: receiving motion data obtained from the one or more motion sensors during a strength training activity; identifying a type of said strength training activity; identifying the set of strength training activities comprising one or more repetitive movements; For each of the repetitive movements, calculating a raw strength score indicative of musculoskeletal movement based on the motion data characteristics, and scaling the raw strength score to a maximum intensity at which the user performs the strength training activity to obtain a user strength score per repetitive movement, the maximum intensity being indicative of the user's ability to perform the strength training activity based on the user's exercise history; calculating, for each one of the repetitive movements, a personalized scale based on a ratio of an effective load on the user during the strength training activity to a predetermined threshold load on the user when performing the strength training activity; calculating, for each of said repetitive movements, a musculoskeletal strain per repetitive movement as the product of said user strength score per repetitive movement and said individualized scale; calculating a musculoskeletal strain score for the strength training activity by summing the per-repetitive musculoskeletal strains for all the repetitive movements in the set; taking action based on the musculoskeletal strain score; The system calculates the user-specific musculoskeletal strain score by executing: 31. The system of claim 30, wherein taking action includes transmitting the user-specific musculoskeletal strain score to a personal computing device associated with the user and displaying it to the user. 32. The system of 30 or 31, wherein taking action includes generating coaching recommendations for the user. 33. Receive motion data from one or more motion sensors of a wearable fitness monitor worn by a user during a strength training activity; calculating an individualized musculoskeletal strain of the user during the strength training activity by adjusting the intensity associated with the motion data according to the user's exercise history associated with the strength training activity, an effective load during the strength training activity, and the user's maximum volume associated with the strength training activity; Taking measures based on the individual musculoskeletal burden A method comprising: 34. Receive motion data from one or more motion sensors of a wearable fitness monitor worn by a user during a strength training activity; calculating a raw strength score for multiple repetitions of the strength training activity based on the motion data; calculating an effective load for the strength training activity based on one or more load parameters including at least a body weight of the user and a load add-on weight for the strength training activity; calculating an individualized musculoskeletal strain score based on a combination of the raw intensity score calculated from the motion data and the paid load calculated based on the one or more loading parameters; presenting information to the user based on the individualized musculoskeletal strain score. A method comprising:

Claims

1. receiving motion data from one or more motion sensors of a wearable monitor worn by a user during a strength training activity including one or more sets of repetitive movements; Identifying a type of strength training activity; determining the amount of the repetitive motions in the set; For each of the repetitive movements, calculating a raw strength score indicative of musculoskeletal movement based on the motion data characteristics, and scaling the raw strength score to a maximum intensity at which the user performs the strength training activity to obtain a user strength score per repetitive movement, the maximum intensity being indicative of the user's ability to perform the strength training activity based on the user's exercise history; calculating, for each one of the repetitive movements, a personalized scale based on a ratio of an effective load for the user during the strength training activity to a predetermined load threshold for the user performing the strength training activity; calculating a musculoskeletal strain per repetition for each of the repetitive movements as the product of the user strength score per repetition and the individualized scale; calculating a musculoskeletal strain score for the strength training activity by summing the per-repetition musculoskeletal strains for all of the repetitive movements in the set; Taking action based on said musculoskeletal strain score A method comprising:

2. The method of claim 1 , wherein determining the amount of the repetitive motion in the set comprises determining the amount based on motion data.

3. The method of claim 1 , wherein determining the amount of the repetitive motions in the set comprises determining the amount based on user input.

4. The method of claim 1 , wherein the action includes refining the user's daily strain calculation based on the musculoskeletal strain score.

5. The method of claim 1 , wherein the action includes generating coaching recommendations for the user.

6. The method of claim 5 , further comprising displaying the coaching recommendations to the user.

7. The method of claim 5 , wherein the coaching recommendations are real-time coaching recommendations.

8. The method of claim 5 , wherein the coaching recommendations relate to the user's subsequent athletic activity.

9. The method of claim 1 , further comprising automatically identifying a type of the strength training activity based on the motion data.

10. The method of claim 1 , further comprising calculating a plurality of musculoskeletal strain scores for each of a plurality of types of strength training activities in the exercise routine.

11. The method of claim 1 , further comprising calculating the payload based on a user input of a weight of the user.

12. The method of claim 1 , further comprising calculating the payload based on a user input of an additional weight for the strength training activity.

13. 10. The method of claim 1, wherein the motion data comprises raw motion data from a three-axis gyroscope and a three-axis accelerometer fused to provide three-axis acceleration data of the repetitive motion that reduces the effects of acceleration due to gravity.

14. The method of claim 1 , wherein the wearable monitor comprises a wrist-worn photoplethysmograph.

15. The method of claim 1 , wherein receiving the motion data comprises receiving raw motion data from at least one gyroscope and at least one accelerometer of the wearable monitor.

16. 10. The method of claim 1, wherein calculating the musculoskeletal strain score comprises calculating the musculoskeletal strain score on a personal computing device of the user that is communicatively coupled to the wearable monitor.

17. The method of claim 1 , wherein calculating the musculoskeletal strain score comprises calculating the musculoskeletal strain score on a remote server coupled in communication with the wearable monitor.

18. The method of claim 1 , wherein the predetermined load threshold is an estimated maximum volume that indicates an upper threshold for the user to repeat the strength training activity without injury.

19. a wearable fitness monitor including one or more motion sensors; one or more processors configured to calculate a user-specific musculoskeletal strain score for a user of the wearable fitness monitor; A system comprising: the one or more processors: receiving motion data obtained from the one or more motion sensors during a strength training activity; identifying a type of said strength training activity; identifying the set of strength training activities comprising one or more repetitive movements; calculating a raw strength score indicative of musculoskeletal movement for each of the repetitive movements based on the motion data characteristics, and scaling the raw strength score to a maximum intensity at which the user performs the strength training activity to obtain a user strength score per repetitive movement, the maximum intensity being indicative of the user's ability to perform the strength training activity based on the user's exercise history; calculating, for each one of the repetitive movements, a personalized scale based on a ratio of an effective load for the user during the strength training activity to a predetermined load threshold for the user when performing the strength training activity; calculating, for each of said repetitive movements, a musculoskeletal strain per repetitive movement as the product of said user strength score per repetitive movement and said individualized scale; calculating a musculoskeletal strain score for the strength training activity by summing the musculoskeletal strain per repetition for all the repetitive movements in the set; taking action based on the musculoskeletal strain score; The system calculates the user-specific musculoskeletal strain score by executing:

20. 20. The system of claim 19, wherein taking action includes transmitting the user-specific musculoskeletal strain score to a personal computing device associated with the user and displaying it to the user.

21. The system of claim 19 , wherein taking action includes generating coaching recommendations for the user.

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