Musculoskeletal burden

Abstract

The physiological monitor uses a motion pattern during a strength training activity, such as a motion pattern detected by a wearable monitor, to assess the degree of muscle, musculoskeletal and / or biomechanical burden experienced by a user while performing strength training. The resulting burden may advantageously be quantified and used to provide coach suggestions, update daily burden metrics, and take other responsive actions.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 395,244, filed on August 4, 2022, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present disclosure relates to physiological monitoring systems and, more particularly, to techniques for quantitatively tracking musculoskeletal burden. Background Art

[0004] Wearable physiological monitors can provide a wealth of physiological data from the wearer. However, the muscle strain produced during strength training can be difficult to characterize using conventional health indicators, such as heart rate or heart rate variability. There remains a need for improved methods and systems to monitor musculoskeletal strain and use the quantified measured strain to provide coaching advice, etc. Summary of the invention

[0005] The physiological monitor uses movement patterns during strength training activities, such as those detected by the wearable monitor, to assess the degree of muscle, musculoskeletal, and / or biomechanical strain experienced by the user while performing strength training. The resulting strain can advantageously be quantified and used to provide coaching advice, update daily strain metrics, and take other responsive actions.

[0006] In one aspect, a computer program product disclosed herein may include computer executable code contained in a non-transitory computer-readable medium, which, when executed on one or more computing devices, causes the one or more computing devices to perform the following steps: receiving raw motion data from one or more motion sensors of a wearable health monitor worn by a user during a strength training activity comprising a set of one or more repetitions, the raw motion data comprising angular rotation data from multiple gyroscopes and linear acceleration data from multiple accelerometers; fusing the raw motion data from the one or more motion sensors to mitigate gravity artifacts, thereby providing motion data comprising three-axis acceleration data; identifying the type of the strength training activity; determining the number of repetitions in the set based on a change in the amplitude of the three-axis acceleration data; for each of the repetitions, calculating a raw intensity score indicating musculoskeletal movement based on a change in the three-axis acceleration data; for each of the repetitions, determining the maximum intensity at which the user performed the strength training activity; degree, the maximum intensity indicating the ability of the user to perform the strength training activity based on the user's exercise history; estimating the maximum amount of the user and the strength training activity based on the user's history of performing the strength training activity, wherein the maximum amount indicates an upper threshold for the user to repeat the strength training activity without injury; calculating the effective load of the user during the strength training activity based on one or more load parameters, the effective load indicating the relative portion of the maximum amount applied by the user during the strength training activity, the one or more load parameters at least including the user's weight and the added weight of the strength training activity; calculating the musculoskeletal burden per repetition for each of the repetitions as the product of a first ratio of the effective load to the maximum amount and a second ratio of the original intensity score to the maximum intensity; summing the musculoskeletal burden per repetition for all repetitions in the set to provide a musculoskeletal burden score for the strength training activity; and displaying the musculoskeletal burden score for the strength training activity to the user. Other embodiments of this aspect may also or alternatively include a method for performing one or more of the above steps. Other embodiments of this aspect may also or alternatively include a system having a wearable health monitor, wherein the wearable health monitor includes one or more motion sensors and one or more processors, wherein the one or more processors are configured to calculate a user-specific musculoskeletal strain score for a user of the wearable health monitor by performing one or more of the above steps.

[0007] Implementations may include one or more of the following features. The computer program product may include code that causes the one or more computing devices to perform the following steps: generating coaching advice to the user based on the musculoskeletal strain score. The coaching advice may be based at least in part on the user's health goals. Calculating the raw intensity score for one of the repetitions may include calculating an average of multiple instantaneous intensity measurements for one of the repetitions. One or more of the multiple instantaneous intensity measurements may be calculated based on an average of a ratio of a discretely measured current change in acceleration to the current acceleration. One or more of the multiple instantaneous intensity measurements may be calculated based on a ratio of a first average of a current change in acceleration to a second average of a current acceleration. The computer program product may include code that causes the one or more computing devices to perform the following steps: creating a load repetition profile for the user based on a history of the user's strength training activity, the load repetition profile indicating the user's repetition capacity under one or more loads during the strength training activity. The computer program product may include code that causes the one or more computing devices to perform the following steps: adding a row to the load repetition 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 repetition profile. The computer program product may include code causing the one or more computing devices to perform the following steps: updating the load repetition profile when the user exceeds the number of repetitions of one of the loads in the load repetition 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 causing the one or more computing devices to perform the following steps: identifying the type of the strength training activity based on the raw motion data. Implementations of the techniques may include hardware, methods or processes, computer software on a computer accessible medium, and systems.

[0008] In one aspect, the method disclosed herein may include: receiving motion data from one or more motion sensors of a wearable monitor worn by a user during a strength training activity comprising a set of one or more repetitions; identifying the type of the strength training activity; determining the number of repetitions in the set; for each of the repetitions, calculating a raw intensity score indicating musculoskeletal movement based on characteristics of the motion data, and scaling the raw intensity score relative to a maximum intensity at which the user performed the strength training activity to obtain a user intensity score for each repetition, the maximum intensity indicating the user's ability to perform the strength training activity based on the user's exercise history; for each of the repetitions, calculating an individualized scale based on a ratio of the user's effective load during the strength training activity to a predetermined load threshold for the user performing the strength training activity; calculating a per-repetition musculoskeletal burden for each of the repetitions as the product of the per-repetition user intensity score and the individualized scale; calculating a musculoskeletal burden score for the strength training activity by summing the per-repetition musculoskeletal burden for all of the repetitions in the set; and taking action based on the musculoskeletal burden score. Other embodiments of this aspect may also or alternatively include a computer program product, the computer program product including a computer executable code contained 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 also or alternatively include a system with a wearable health monitor, the wearable health monitor including one or more motion sensors and one or more processors, the one or more processors being configured to calculate a user-specific musculoskeletal burden score for a user of the wearable health monitor by performing one or more of the above steps.

[0009] Implementations may include one or more of the following features. Determining the number of repetitions in the set may include determining the number based on motion data. Determining the number of repetitions in the set may include determining the number based on user input. The action may include refining a daily burden calculation for the user based on the musculoskeletal burden score. The action may include generating a coaching suggestion for the user. The method may include displaying the coaching suggestion to the user. The coaching suggestion may be a real-time coaching suggestion. The coaching suggestion may be related to a subsequent exercise activity of the user. The method may include automatically identifying the type of strength training activity based on the motion data. The method may include calculating a plurality of musculoskeletal burden scores for each of a plurality of types of strength training activities in an exercise program. The method may include calculating the effective load based on a user input of a weight of the user. The method may include calculating the effective load based on a user input of an added weight of the strength training activity. The motion data may include raw motion data from a triaxial gyroscope and a triaxial accelerometer, the raw motion data being fused to provide triaxial acceleration data for the repetition while mitigating the effects of acceleration due to gravity. The wearable monitor may include a wrist-worn photoplethysmography device. Receiving 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 a personal computing device of the user coupled to the wearable monitor in a communication relationship. Calculating the musculoskeletal strain score may include calculating the musculoskeletal strain score on a remote server coupled to the wearable monitor in a communication relationship. The predetermined load threshold may be an estimated maximum amount, and the estimated maximum amount indicates an upper threshold value for the user to repeat the strength training activity without injury. Implementation of the technology may include hardware, methods or processes, computer software on computer-accessible media, and systems.

[0010] In one aspect, a system disclosed herein may include: a wearable health 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 health monitor by performing the following steps: receiving motion data obtained 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 repetitions, for each of the repetitions, calculating a raw intensity score indicating musculoskeletal movement based on features of the motion data, and scaling the raw intensity score relative to a maximum intensity at which the user performed the strength training activity. A method of performing a strength training activity in a plurality of steps of a plurality of exercises is provided for performing the strength training activity in a plurality of steps of a plurality of exercises. The method comprises: providing a plurality of exercises for performing the strength training activity in a plurality of steps of a plurality of exercises, wherein the plurality of exercises are performed at a plurality of intervals ...

[0011] In one aspect, the method disclosed herein may include: receiving motion data from one or more motion sensors of a wearable health monitor worn by a user during a strength training activity; adjusting the intensity associated with the motion data according to the user's exercise history associated with the strength training activity, the effective load during the strength training activity, and the maximum amount of the user associated with the strength training activity to calculate the individualized musculoskeletal burden of the user during the strength training activity; and taking action based on the individualized musculoskeletal burden. Other embodiments of this aspect may also or alternatively include a computer program product, the computer program product including a computer executable code contained 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 also or alternatively include a system with a wearable health monitor, the wearable health monitor including one or more motion sensors and one or more processors, the one or more processors being configured to calculate a user-specific musculoskeletal burden score for the user of the wearable health monitor by performing one or more of the above steps.

[0012] In one aspect, the method disclosed herein may include: receiving motion data from one or more motion sensors of a wearable health monitor worn by a user during a strength training activity; calculating a raw intensity score for multiple repetitions of the strength training activity based on the motion data; calculating an effective load of the strength training activity based on one or more load parameters, the load parameters including at least the weight of the user and the added weight of the strength training activity; calculating an individualized musculoskeletal burden score based on a combination of the raw intensity 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 burden score. Other embodiments of this aspect may also or alternatively include a system having a wearable health monitor, the wearable health monitor comprising one or more motion sensors and one or more processors, the one or more processors being configured to calculate a user-specific musculoskeletal burden score for a user of the wearable health monitor by performing one or more of the above steps. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The foregoing and other objects, features and advantages of the apparatus, systems and methods described herein will become apparent from the following description of specific embodiments thereof, as illustrated in the accompanying drawings. The accompanying drawings are not necessarily drawn to scale, but emphasis is placed on illustrating the principles of the apparatus, systems and methods described herein. In the accompanying drawings, like reference numerals generally indicate corresponding elements.

[0014] Figure 1 A physiological monitoring device is shown.

[0015] Figure 2 A physiological monitoring system is shown.

[0016] Figure 3 The sensing system is shown.

[0017] Figure 4 Methods for generating and using musculoskeletal (MSK) load data are shown.

[0018] Figure 5 A load repetition profile for scaling exercise repetition intensity is shown.

[0019] Figure 6 A graph of velocity over time measured during a bench press exercise is shown.

[0020] Figure 7 The triaxial acceleration data for one repetition of a bench press exercise is shown.

[0021] Figure 8 Shown from Figure 7 The integral of the normalized acceleration magnitude of the data.

[0022] Fig. 9 A system for monitoring MSK burden is shown.

[0023] Fig.10 A system for monitoring MSK burden is shown.

[0024] Fig.11 A system for monitoring MSK burden is shown.

[0025] Fig.12 A user interface of a strength training system with an MSK burden score is shown.

[0026] Fig.13 A user interface of a strength training system with an MSK burden score is shown. DETAILED DESCRIPTION

[0027] Embodiments will now be described more fully below with reference to the accompanying drawings, in which preferred embodiments are shown. However, the foregoing may be implemented in many different forms and should not be construed as being limited to the illustrated embodiments set forth herein. Instead, these illustrated embodiments are provided to enable the disclosure to convey the scope to those skilled in the art.

[0028] All documents mentioned herein are incorporated herein by reference in their entirety. References to singular items should be understood to include the plural items, and vice versa, unless otherwise expressly stated or clear from the text. Grammatical conjunctions are intended to express any and all transitional and conjunctive combinations of connected clauses, sentences, words, etc., unless otherwise stated or clear from the context. Thus, the term "or" should generally be understood to mean "and / or" and the like.

[0029] Unless otherwise indicated, the enumeration of numerical ranges herein is not intended to be limiting, but refers to any and all values ​​falling within the range individually, and each individual value within such a range is incorporated into the specification as if it were individually enumerated herein. As will be appreciated by those of ordinary skill in the art, when accompanied by numerical values, the words "approximately", "approximately", etc. should be interpreted as indicating deviations to operate satisfactorily for the intended or specified purpose. Similarly, when used with reference to physical properties, approximate words such as "approximately" or "substantially" should be understood as expected deviation ranges, and those of ordinary skill in the art will appreciate that the deviation ranges are to operate satisfactorily for corresponding uses, functions, purposes, etc. The range of values ​​and / or numerical values ​​is provided herein only as an example and does not constitute a limitation on the scope of the described embodiments. In the case of providing a range of values, they are also intended to include each numerical value within the range, as if proposed individually, unless there is a clear statement to the contrary. The use of any and all examples or exemplary languages ​​("for example", "such as", etc.) provided herein is intended only to better describe the embodiments and does not limit the scope of the disclosed embodiments. Any language in the specification should not be interpreted as indicating any unclaimed element as the essence of the practical embodiment.

[0030] In the following description, it should be understood that terms such as "first", "second", "top", "bottom", "up", "down", "above", "below", etc. are words of convenience and should not be construed as limiting terms unless specifically stated to the contrary.

[0031] As used herein, the term "user" refers to any type of animal, human or non-human, whose physiological information may be monitored using an exemplary wearable physiological monitoring system.

[0032] The term "continuously" used herein in conjunction with heart rate data refers to collecting heart rate data at a sufficient frequency to be able to detect individual heartbeats, and also refers to collecting heart rate data over extended periods such as one hour, one day, or longer (including all-day and nighttime collection). More generally, with respect to physiological signals that can be monitored by a wearable device, "continuous" or "continuously" will be understood to mean continuously at a rate and duration suitable for the expected time-based processing, and physically at a cycle-to-cycle rate (e.g., multiple times per heartbeat, breath, etc.) sufficient to resolve desired physiological characteristics such as heart rate, heart rate variability, heart rate peak detection, pulse shape, etc. At the same time, continuous monitoring is not intended to exclude ordinary data collection interruptions, such as temporary displacement of monitoring hardware caused by sudden movement, changes in external lighting, power loss, physical manipulation and / or adjustment by the wearer, physical displacement of monitoring hardware due to external forces, etc. It will also be noted that in this context, heart rate data or monitored heart rate may more generally refer to raw sensor data such as a light intensity signal, or data processed therefrom, such as heart rate data, signal peak data, heart rate variability data, or any other physiological or digital signal suitable for recovering heart rate information as contemplated herein. In addition, such heart rate data may typically be captured over a certain historical period, which may then be correlated with various other data or metrics, such as sleep state, identified exercise activity, resting heart rate, maximum heart rate, etc.

[0033] The term "computer-readable medium" as used herein refers to a non-transitory storage medium, such as storage hardware, a storage device, a computer memory accessible by a controller, a microcontroller, a microprocessor, a computing system, or the like, or any other module or component or module of a computing system to encode computer-executable instructions, software programs, and / or other data thereon. A "computer-readable medium" can 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 on the medium. Non-transitory computer-readable media may 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 (such as DRAM, SRAM, EDORAM), etc. Although not depicted, any device or component described herein may include a computer-readable medium or other memory for storing program instructions, data, etc.

[0034] Figure 1A physiological monitoring system is shown. The system 100 may include a wearable monitor 104 configured for physiological monitoring. The system 100 may also include a removable and replaceable battery 106 for recharging the wearable monitor 104. The wearable monitor 104 may include a strap 102 or (one or more) other retention systems for fixing the wearable monitor 104 in a position on the wearer's body for collecting physiological data as described herein. For example, the strap 102 may include an elongated elastic band formed by any suitable elastic material (e.g., rubber or textile polymer fibers, such as textile polyester, polypropylene, nylon, spandex, etc.). The strap 102 may be adjustable to accommodate different wrist sizes and may include any latch, buckle, etc. to fix the wearable monitor 104 in an expected position for monitoring physiological signals. Although a wrist-worn device is depicted, it will be understood that the wearable monitor 104 may be configured to be positioned at any suitable position on the user's body based on the sensing modality and the nature of the signal to be acquired. For example, the wearable monitor 104 can be configured to be used on a wrist, ankle, bicep, chest, or any other suitable location (one or more), and the band 102 can be or can include a belt or other elastic band in a garment or accessory, etc. The wearable monitor 104 can also or alternatively be structurally configured to be placed on or in a garment, for example, permanently or in a removable and replaceable manner. To this end, the shape and size of the wearable monitor 104 can be configured to be placed in a pocket, a slot, and / or other housing coupled to or embedded in a garment. In this configuration, a pocket or other holding arrangement on the garment can include a sensing window, etc., so that the wearable monitor 104 can be operated when placed in the garment for use. U.S. Patent No. 11,185,292 describes a non-limiting example embodiment of a suitable wearable monitor 104, and its entire contents are incorporated herein by reference.

[0035] The system 100 may include any hardware components, subsystems, etc. to support various functions of the wearable monitor 104, such as data collection, processing, display, and communication with external resources. For example, the system 100 may include hardware for a heart rate monitor that uses, for example, photoplethysmography, electrocardiogram, or any other technology (one or more). The system 100 may be configured so that when the wearable monitor 104 is placed around the wrist (or at some other body position) for use, the system 100 initiates the acquisition of physiological data from the wearer. In some embodiments, the pulse or heart rate may be optically acquired based on a light source (e.g., a light emitting diode (LED)) and an optical detector in the wearable monitor 104. The LED may be positioned to direct illumination to the user's skin, and an optical detector such as a photodiode may be used to capture illumination intensity measurements indicating illumination reflected and / or transmitted from the LED by the wearer's skin.

[0036] The 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 (using a galvanic skin response sensor), motion (using one or more multi-axis accelerometers and / or gyroscopes), blood pressure, etc., as well as environmental or contextual parameters, such as ambient light, ambient temperature, humidity, time of day, etc. For example, the wearable monitor 104 may include sensors such as accelerometers and / or gyroscopes for motion detection, sensors for ambient temperature sensing, sensors for measuring electrodermal activity (EDA), sensors for measuring galvanic skin response (GSR) sensing, etc. The system 100 may also or alternatively include other systems or subsystems that support additional functionality of the wearable monitor 104. For example, the system 100 may include a communication system that supports, for example, near field communication, proximity sensing, Bluetooth communication, Wi-Fi communication, cellular communication, satellite communication, etc. The wearable monitor 104 may also or alternatively include components such as a geographic positioning system (GPS), a display and / or user interface, a clock and / or timer, etc.

[0037] 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 in the wearable monitor 104. In addition or alternatively, the system 100 may include multiple wearable monitors 104 (and / or other physiological monitors) that can share battery power or provide power to each other. The system 100 can perform a variety of functions related to continuous monitoring, such as automatically detecting when a user is asleep, awake, exercising, etc., and such detection can be performed locally at the wearable monitor 104, or at a remote service that is coupled to the wearable monitor 104 in a communication relationship and receives data from it. In general, the system 100 can support continuous, independent monitoring of physiological signals such as heart rate, and the underlying acquired data can be stored on the wearable monitor 104 for an extended period of time until it can be uploaded to a remote processing resource for more complex computational analysis.

[0038] In one aspect, the wearable monitor can be a wrist-worn photoplethysmographic device.

[0039] Figure 2 A physiological monitoring system is shown. More specifically, Figure 2 A physiological monitoring system 200 is shown that can be used with any of the methods or devices described herein. In general, the system 200 can include a physiological monitor 206, a user device 220, a remote server 230 with remote data processing resources (e.g., any processor or processing resource described herein), and one or more other resources 250, all of which can be interconnected via a data network 202.

[0040] The data network 202 may be any data network described herein. For example, the data network 202 may be any network(s) or interconnected network(s) suitable for communicating data and information between participants of the system 200. This may include a public network such as the Internet, a private network, a telecommunication network such as a public switched telephone network, or a cellular network 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 any of a variety of enterprise area or local area networks and other switches, routers, hubs, gateways, etc. that may be used to communicate data between participants in the system 200. This may also include a local or short-range communication infrastructure suitable for, for example, coupling the physiological monitor 206 to the user device 220 or supporting communication with local resources. As non-limiting examples, short-range communications may include Wi-Fi communications, Bluetooth communications, infrared communications, near field communications, communications with RFID tags or readers, and the like.

[0041] Physiological monitor 206 can generally be any physiological monitoring device or system, such as any wearable monitor or other monitoring device or system described herein. In one aspect, physiological monitor 206 can be a wearable physiological monitor, which is shaped and sized to be worn on a wrist or other body position. Physiological monitor 206 can include a wearable housing 211, a network interface 212, one or more sensors 214, one or more light sources 215, a processor 216, a tactile device 217 or other user input / output hardware, a memory 218, and a strip 210 for keeping physiological monitor 206 in a desired position on the user. In one aspect, physiological monitor 206 can be configured to obtain heart rate data and / or other physiological data from the wearer in an intermittent or substantially continuous manner. On the other hand, physiological monitor 206 can be configured to support extended, continuous collection of physiological data, such as several days, a week or longer.

[0042] The network interface 212 of the physiological monitor 206 can be configured to couple the physiological monitor 206 to one or more other components of the system 200 in a communicative relationship, to locally couple the physiological monitor 206 to a wireless access point, router, computer, laptop, tablet, cellular telephone, or other device that can process data locally, directly (e.g., via a cellular data connection, etc.) or indirectly via a short-range wireless communication channel, and / or to relay data from the physiological monitor 206 to a remote server 230 or (one or more) other resources 250 as may be necessary or helpful in acquiring and processing data from the physiological monitor 206.

[0043] One or more sensors 214 may include any sensor described herein, or any other sensor or subsystem suitable for physiological monitoring or supporting functions. As an example and not limitation, one or more sensors 214 may include one or more of the following: light sources, optical sensors, accelerometers, gyroscopes, temperature sensors, skin galvanic response sensors, capacitive sensors, resistive sensors, environmental sensors (e.g., for measuring ambient temperature, humidity, lighting, etc.), geolocation sensors, global positioning systems, proximity sensors, RFID tag readers and RFID tags, time sensors, skin electrical activity sensors, etc. One or more sensors 214 may be set in the wearable housing 211, or otherwise positioned and configured for physiological monitoring or other functions described herein. In one aspect, one or more sensors 214 include a light detector configured to provide light intensity data to the processor 216 (or remote server 230) for calculating heart rate and heart rate variability. One or more sensors 214 may also or alternatively include an accelerometer, gyroscope, etc. configured to provide motion data to the processor 216, for example, for detecting activities such as sleep state, rest state, wake-up event, exercise and / or other user activities. In implementations, one or more sensors 214 may include sensors that measure the user's galvanic skin response. One or more sensors 214 may also or alternatively include electrodes for capturing electronic signals, etc., for example, to obtain an electrocardiogram and / or other electrically derived physiological measurements.

[0044] The processor 216 and memory 218 may be any processor and memory described herein. In one aspect, the memory 218 may store physiological data obtained by monitoring a user with one or more sensors 214, and / or any other sensor data, program data, or other data useful for the operation of the physiological monitor 206 or other components of the system 200. It will be understood that, although only the memory 218 on the physiological monitor is shown, any other device or component (one or more) of the system 200 may also or alternatively include memory for storing program instructions, raw data, processed data, user input, etc. In one aspect, the processor 216 of the physiological monitor 206 may be configured to obtain heart rate data from the user, such as heart rate data including or based on raw data from the sensor 214. The processor 216 may also or alternatively be configured to determine or help determine a user condition related to, for example, health, physical fitness, fatigue, recovery sleep, or any other condition described herein.

[0045] One or more light sources 215 can be coupled to the wearable housing 211 and controlled by the processor 216. At least one light source 215 can be directed toward the user's skin adjacent to the wearable housing 211. Light from the light source 215, or more generally, one or more wavelengths of light from the light source 215, can be detected by the one or more sensors 214 and processed by the processor 216, as described herein.

[0046] The system 200 may also include remote data processing resources executing on a remote server 230. The remote data processing resources may include any of the processors and associated hardware described herein, and may be configured to receive data transmitted from the memory 218 of the physiological monitor 206, and process the data to detect or infer physiological signals of interest, such as heart rate, heart rate variability, respiratory rate, pulse oximetry, blood pressure, etc. The remote server 230 may also or alternatively assess the user's condition, such as recovery status, sleep status, exercise activity, exercise type, sleep quality, daily activity burden, and any other health or fitness condition that may be detected based on such data.

[0047] The system 200 may include one or more user devices 220 that may work with the physiological monitor 206, for example, to provide a display for user data and analysis, or more generally, user input / output, and / or to provide a communication 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 communicate locally with the user device 220 (e.g., a user's smartphone) via short-range communication (e.g., Bluetooth, etc.) for data exchange between the physiological monitor 206 and the user device 220, and the user device 220 may in turn communicate with the remote server 230 via the data network 202 to forward data from the physiological monitor 206 and receive analysis and results from the remote server 230 for presentation to the user. In one aspect, the user device(s) 220 may support physiological monitoring by processing or pre-processing the data from the physiological monitor 206 to support the extraction of heart rate or heart rate variability data from the raw data obtained by the physiological monitor 206. On the other hand, computationally intensive processing may be advantageously performed at a remote server 230 , which may have greater storage and processing capabilities than the physiological monitor 206 and / or the user device 220 .

[0048] The user device 220 may include any suitable computing device(s), including, but not limited to, a smartphone, a desktop computer, a laptop computer, a network computer, a tablet computer, a mobile device, a portable digital assistant, a cellular phone, a portable media or entertainment device, or any other computing device described herein. The user device 220 may provide a user interface 222 for a user to access data and analysis, and / or to enable a user to control the operation of the physiological monitor 206. The user interface 222 may be maintained by one or more applications executing locally on the user device 220, or the user interface 222 may be remotely served and presented on the user device 220, for example, from a remote server 230 or one or more other resources 250.

[0049] Typically, 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 and perform physiological and / or health monitoring / analysis or any other analysis described herein (e.g., analyzing sleep, determining fatigue, assessing recovery, etc.), and may host a user interface for remotely accessing the data, for example, from the user device 220. The remote server 230 may include a web server or other programming front end that facilitates the ability of the user device 220 or the physiological monitor 206 to access the remote server 230 or other components of the system 200 over a network.

[0050] System 200 may include other resources 250, such as any resources that can be effectively 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, computing tools, data monitoring tools, algorithms, etc. In another aspect, other resources 250 may include one or more management or programming interfaces for human participants such as programmers, researchers, annotators, editors, analysts, coaches, etc. to interact with any of the aforementioned things. Other resources 250 may also or alternatively include any other software or hardware resources that can be effectively used in networked applications as contemplated herein. For example, other resources 250 may include a payment processing server or platform for authorizing payment access, content or option / feature purchases. In another aspect, other resources 250 may include a certificate server or other security resources for third-party identity authentication, data encryption or decryption, etc. On the other hand, the other resources 250 may include a desktop computer or the like co-located with the user device 220, the wearable band 210, or the remote server 230 (e.g., on the same local area network as the user device 220, the wearable band 210, or the remote server 230, or directly coupled via a serial or USB cable). In this case, the other resources 250 may provide supplemental functionality to the components of the system 200, such as firmware upgrades, user interfaces, and storage and / or pre-processing of data from the physiological monitor 206 prior to transmission to the remote server 230.

[0051] The other resources 250 may also or alternatively include one or more network servers that provide network-based access to and from any other participants in the system 200. Although depicted as separate network entities, it will be readily appreciated that the other resources 250 (e.g., network servers) may also or alternatively be logically and / or physically associated with one of the other devices described herein, and may, for example, include or provide a user interface 222 for network access to a remote server 230 or database or other resource(s) to facilitate user interaction over the data network 202, for example from a physiological monitor 206 or user device 220.

[0052] On the other hand, other resources 250 may include health equipment or other health infrastructure. For example, a strength training machine may automatically record the weight added during repetition and / or repetition, which may be wirelessly accessed by a physiological monitor 206 or some other user device 220. More generally, a gym may be configured to track user movement from one machine to another, and report activity from each machine, so as to track various strength training activities in a trial. Other resources 250 may also or alternatively include other monitoring devices or infrastructure. For example, the system 200 may include one or more cameras to track the movement and / or body position of a user's free weights during repeated strength training activities, etc. Similarly, a user may wear or embed a tracking reference in clothing, such as a visually distinguishable object for image-based tracking, or a radio beacon for other tracking, etc. On the other hand, the weight itself may be equipped, for example, with a sensor to record and transmit detected motion, and / or a beacon, etc. to self-identify type, weight, etc., so as to automatically detect and track exercise activities with other connected devices.

[0053] Figure 3 A sensing system is shown. In general, the system 300 may include a physiological monitor 302 having 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. The physiological monitor 302 may be positioned for use against a surface 313 of a user's skin 314, wherein the light source 306 and sensors 308, 310 may contact the skin 314 to acquire physiological data. Although not depicted, it will be understood that the physiological monitor 302 may generally be held in place using any of the straps, clothing, etc. described herein.

[0054] Processor 304 may be any microprocessor, microcontroller, application specific integrated circuit, or other processing circuit, or combination thereof, suitable for controlling the operation of the physiological monitor and acquiring physiological data.

[0055] The light source 306 may include one or more light emitting diodes or other illumination sources, and may be located within the physiological monitor 302 such that when the physiological monitor 302 is placed on the skin 314 for use, the light source 306 directs illumination toward the skin 314, and the illumination is reflected back to the sensors 308, 310, as indicated by arrows 316, where the intensity may be measured at the sensors 308, 310. In one aspect, the light source 306 may include a light emitting diode that emits light in the infrared or near infrared wavelength range, which provides good light transmission through human skin, facilitating low power transmission of measurable illumination to the sensors 308, 310, although other illumination sources and wavelengths may also or alternatively be used.

[0056] When the physiological monitor 302 is placed on the skin 314 for use, the sensors 308, 310 can be oriented to contact the skin 314 and positioned so that the sensors 308, 310 can capture the illumination reflected and / or transmitted by the skin from the light source 306. In general, the sensors 308, 310 can include photodiodes, photodetectors, or any other sensor(s) that respond to illumination from the light source 306. This can include broadband optical sensors, narrowband optical sensors, filtered sensors, etc. In general, the first sensor 308 can be positioned closer to the light source 306 than the second sensor 310 to facilitate detection of different intensities in the measured wavelength(s). For example, the first sensor 308 can be located 1-4 mm from the light source 306, and the second sensor 310 can be located 2-8 mm from the light source, or about twice as far as the first sensor 310 from the light source 306.

[0057] Other spacings may also or alternatively be used depending on, for example, the intensity of the light source 306, the sensitivity of the sensors 308, 310, the contact force of the physiological monitor 302 on the skin 314, the degree of intrusion of ambient light, the physiological measurement / characteristic of interest, etc. In one aspect, the sensors 308, 310 may be arranged linearly in a straight line away from the light source 306. While this provides consistency in comparing measurements, this is not strictly required, and the sensors 308 may be displaced in any direction away from the light source 306 as long as they all contact the skin 314 in a manner that allows capture of light from the light source 306 that passes through the skin 314. On the other hand, the physiological monitor 302 may include one or more other light sources and / or light sensors that may be arranged to improve accuracy and / or provide redundancy for contact detection, 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 aspect, the two sensors 308 , 310 may be located at different distances from the perimeter of the physiological monitor 302 , such that the sensors 308 , 310 may acquire different intensity values ​​of ambient light incident on the skin and transmitted to the sensors 308 , 310 through the skin.

[0058] In operation, the processor 304 may acquire raw intensity data from the sensors 308 , 310 and perform local calculations, such as pre-processing the raw data for heart rate measurement, or evaluating whether the physiological monitor 302 is properly positioned for use on the skin 314 .

[0059] The accelerometer 312 may include, for example, one or more single-axis or multi-axis accelerometers that can effectively measure the motion of the physiological monitor 302 to support functions such as automatic activity detection, device on / off assessment, and calculation of musculoskeletal activation levels, for example, as described herein. Other motion and orientation sensing hardware—such as one or more gyroscopes 318, inertial motion sensors, and / or other microelectromechanical systems (MEMS) sensors—may also or alternatively be used for these purposes. More generally, the physiological monitor 302 may include any additional components, subsystems, etc. suitable for supporting the various modes of physiological monitoring and contextual data acquisition as described herein.

[0060] Methods for calculating burden scores based on heart rate are described in U.S. Pat. No. 11,185,292 by way of non-limiting example, the entire contents of which are incorporated herein by reference. In one aspect, additional methods and systems for estimating musculoskeletal load based on motion patterns in a monitoring device and using the load to provide improved burden analysis, coaching advice, etc. are disclosed herein. Musculoskeletal (MSK) burden captures a type of effort that may be missed or underestimated when burden is estimated based only on heart rate. Motion data (e.g., accelerometer data or gyroscope data) from a wearable device can be advantageously used to fill this gap and estimate muscle burden based on various motion parameters during strength training activities. Although users may record weight, repetitions, and subjective burden, direct objective measurement of MSK burden advantageously alleviates manual data entry by users, mitigates underreporting of high burden events, eliminates subjective variability in burden calculations, and the like. In one aspect, the disclosed technology includes objectively quantifying strength training exercise (and other activities) efforts and reporting corresponding burden metrics that can be used to understand the physiological effects of strength training activities. In another aspect, the disclosed technology can be used to create coaching metrics, refinements to cardio-based burden estimates, etc. In another aspect, the disclosed technology can be used to monitor whether exercise techniques or metrics—e.g., weights currently used in strength training workouts—are appropriate given the intended training stimulus and / or user goals (e.g., body shaping, weight gain, weight loss, etc.).

[0061] Figure 4 A method for calculating a musculoskeletal (MSK) burden score is shown. Method 400 can be deployed on any device and system described herein, for example, and can be deployed with computer code for performing some or all of the following steps. MSK burden can be assessed in two parts - amount and intensity. As described herein, an objective measure of MSK burden can be obtained by developing a metric for measuring each of these components during a strength training activity and combining them into a single MSK burden score that can be reported to a user and / or used for coaching advice, etc.

[0062] In the context of strength training, "volume" generally refers to the total amount of work completed in a given workout or specific time period. There are different ways to calculate volume, but one common method is to multiply the number of sets for a particular exercise by the number of repetitions per set, and then by the weight lifted per repetition. For example, for 3 sets of 10 reps of 100 pounds, the volume would be 3,000 pounds (3 sets x 10 reps x 100 pounds). Another way to think about volume is to simply count the number of sets or repetitions for a muscle group or exercise in the workout. For example, for 5 sets of 5 reps on the bench press, the volume would be 25 reps (5 sets x 5 reps). Managing volume is important in strength training because it has a huge impact on recovery and progression. Too much volume can lead to overtraining and increased risk of injury, while too little volume may not provide enough stimulus for growth and improvement.

[0063] As described below, the amount - the total work done in a strength training activity - can be objectively quantified for a particular user by, for example, determining the effective load of the user's exercise and comparing it to the user's maximum amount. The effective load can be based on other objective parameters, such as the physical weight lifted by the user (e.g., pounds on a barbell, pounds of hand weights, pounds on a strength machine, etc.) or the user's body weight (which may affect exercises such as push-ups, squats, pull-ups, etc., where the user's weight provides some or all of the load), or some combination of these (e.g., a user performing pull-ups with a 10-pound weight added). The maximum amount can be estimated for a user based on, for example, a statistical estimate of the number of repetitions at a particular weight / amount that is more likely to cause injury.

[0064] The concept of intensity presents a different calculation challenge. In strength training, "intensity" generally refers to the amount of exercise effort or load relative to maximal ability, or how difficult a particular load is for a particular individual. This is often defined as a percentage of the one-repetition maximum (1RM), which is the maximum weight that can be lifted for a particular exercise in one repetition. For example, if a user has a 1RM for a 200-pound bench press and is lifting 150 pounds, the user is training at an intensity of 75% of their 1RM (150 divided by 200). Another way to measure intensity, especially with methods like high-intensity interval training (HIIT), is through perceived exertion, or how difficult the workout feels. This can be a bit subjective, but tools like the Borg Rating of Perceived Exertion (RPE) scale can help quantify it.

[0065] In order to capture intensity (e.g., muscle capacity measured relative to maximum), physical movement during exercise can be tracked by a wearable monitor and used to objectively calculate the intensity based on motion for a specific user and exercise type. Because different users have different abilities, this motion-based metric can be scaled according to the user's exercise history to determine how much effort the user has put in relative to the maximum during exercise. In some cases, synthesizing intensity based on other data can also be useful. For example, some exercises may not involve motion that can be detected by a wearable monitor, such as training on a leg curler or leg extension machine while using a wrist-worn monitor. In these cases, the user can report weight and repetition, and the intensity can be estimated based on user history. On the other hand, some exercises do not involve motion at all. For example, isometric exercises, such as plank support or wall sits, require staying still. For these exercises, "repetitions" can be derived based on the amount of time the exercise is performed. For example, a plank support can be represented by repeating once every six seconds. In some cases, both techniques can be used, for example, when a user is performing an isometric exercise, the isometric exercise applies a load to a muscle group whose movement (e.g., shaking due to burden) cannot be detected at the location of the wearable monitor. In other cases, muscle twitches caused by isometric loading can be detected by wearable monitors and used to assess strain, even if the user has no intention of exercising.

[0066] An example method for calculating musculoskeletal burden based on objective measures of intensity and volume is described below.

[0067] As shown in step 404, the method may include receiving user data. In one aspect, this may include receiving input from the user, such as body weight, increased weight, and other parameters for evaluating strength training activities. This may also include manually input information, such as weight, repetition, and type of one or more strength training activities. For manual data input, this may be input by the user on a user device before, during, and / or after the trial, or may be input on a wearable monitor with a suitable user interface for corresponding data input. For some activities, it may be difficult, impossible, or inconvenient to track exercise with a wearable monitor. For example, a user performing an isometric exercise that does not load the muscles near the wearable monitor may be difficult or impossible to automatically measure with a wearable monitor. In these cases, the user may manually input some or all of the relevant data to support the MSK burden analysis of the trial.

[0068] In another aspect, some or all of the user data describing a particular exercise or strength training activity can be automatically derived from motion data captured, for example, by a wearable monitor or smart fitness device. For example, a wearable monitor can detect the type of activity based on characteristics of the motion data captured by the wearable monitor during the exercise. The wearable monitor can also or alternatively detect individual repetitions in a set. In another aspect, a user can provide a per-repetition or per-set input to divide the activity, which can be used by any system such as described herein to more easily identify sets and repetitions in sets.

[0069] On the other hand, data can be obtained from other devices. For example, a strength training machine can count repetitions and report these repetitions to a wearable monitor or some other user device wirelessly or otherwise. The strength training machine can also or alternatively report the weight of a specific repetition group, which can be retrieved by a wearable monitor or other device and used to support the MSK burden calculation as described herein. On the other hand, a camera can be used to track user motion and / or equipment, and can be used to derive strength training data from camera images. For example, image processing can be applied to identify activities, calculate repetitions, evaluate forms, identify increased weight, etc. On the other hand, weights can be equipped or marked to support directly obtaining motion data, weight data, etc. from weights. Combinations can also be used. For example, a camera can be used to count repetitions, and weights can be marked to allow automatic identification for determining loads.

[0070] In one aspect, the user data includes historical exercise data for the user. This can be retrieved, for example, from a remote server or other resource, and can be used to support the MSK score calculation. For example, this can include retrieving a load repetition profile for the user based on the user's history of strength training activity, such as Figure 5 The load repetition profile shown. The load repetition profile may generally indicate the user's repetition capacity under one or more loads during a strength training activity, and may be used to scale the user's original intensity based on motion data from a wearable device. Although it is known in the art to use repetition velocity to detect the approach of a maximum value, the load repetition profile contemplated herein may advantageously use acceleration data to facilitate estimating intensity using data from sensors in a wearable device. If the user does not have an available load repetition profile, method 400 may include creating a load repetition profile. Method 400 may also include updating the profile appropriately. For example, the method may include adding a row to the load repetition profile when the user performs a strength training activity under a new load that is not included in one or more loads in the load repetition profile. Method 400 may also or alternatively include adding a column when the user performs a new number of repetitions, or updating the load repetition profile when the user exceeds the number of repetitions or load within the load repetition profile.

[0071] On the other hand, the maximum amount of the user can be retrieved, or the maximum amount of the user can be estimated based on other retrieved user data. Calculating the maximum amount of exercise can be somewhat complex and personalized, because it depends on various factors, such as current health level, specific exercise, training goals and how the user reacts to different training amounts. As used herein, the maximum amount is intended to provide an indication of the upper threshold for the user's injury-free repeated strength training activities. A variety of techniques can be used to estimate this threshold. For example, in the absence of specific user data, linear regression can be used to derive the formula (maximum amount=a*weight+b) related to the maximum amount of weight to the user population, which can be used as an estimate before other user data are available. For a large number of user-specific samples, the maximum amount of safe load can be effectively calculated as the baseline or average amount of each trial plus twice the standard deviation of the trial amount. On the other hand, a series of techniques can be used based on how many user-specific amount measurements are available. More generally, any useful technique for estimating the threshold or limit of the injury-free amount, for example, striving to reach or be lower than the threshold value presenting an acceptable risk of injury, can be used to calculate the maximum amount for scaling each trial amount as described herein.

[0072] As shown in step 406, the method may include receiving motion data. For example, this may include receiving raw motion data from one or more motion sensors of a wearable monitor, such as a wearable health monitor worn by a user during a strength training activity including one or more repetitions. 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 a motion sensor, including angular rotation data from multiple gyroscopes and linear acceleration data from multiple accelerometers. On the other hand, receiving motion data may include receiving motion data from multiple body positions, for example, in the case where the user has a double wristband and / or ankle band, and / or in the case where the user wears smart clothing with suitable motion sensors at various body positions. The motion data may also or alternatively be received from other sources, such as other external motion sensors, smart watches or other wearable computing devices, external cameras for measuring motion, etc.

[0073] As shown in step 408 , method 400 may include processing the motion data.

[0074] In one aspect, this can include 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 using sensor fusion techniques, can help mitigate the effects of gravity on accelerometer measurements. A three-axis accelerometer measures both dynamic acceleration (generated by motion) and static acceleration (the constant gravity that pulls the device down). In order to separate gravity-induced artifacts from motion-related data, other sensors can be used in conjunction with the accelerometer, such as a gyroscope or magnetometer. A popular technique for this is to use a Kalman filter or an extended Kalman filter, which is a recursive algorithm that uses a series of measurements observed over time (in this case, readings from an accelerometer and a gyroscope / magnetometer) and produces estimates of unknown variables that tend to be more accurate than those based on any individual measurement. Another common technique is to use a complementary filter, such as a Mahony or Madgwick filter. These algorithms combine accelerometer and gyroscope data to provide more stable, accurate, and drift-free direction measurements, even when gravity is constant. More generally, by taking readings from multiple sensors and combining them, it is possible to separate the measured acceleration into acceleration due to motion and acceleration due to gravity, thereby mitigating the effects of gravity artifacts on accelerometer measurements. Any such technique can be used to process motion data and obtain the triaxial acceleration data described herein.

[0075] 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 any other raw or processed data representing the wearer's motion from a wearable monitor. Thus, in one aspect, the motion data may include raw motion data from a three-axis gyroscope and a three-axis accelerometer. On the other hand, the motion data may include any raw data that has been fused to provide three-axis acceleration data for repetition, mitigating the effects of acceleration due to gravity.

[0076] As shown in step 410, method 400 may include identifying an activity. More specifically, this may include identifying the type of strength training activity that the user is performing. In one aspect, this may include receiving a user input specifying the type of strength training activity, such as in a user interface of a user device. On the other hand, 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 raw motion data or based on raw motion data. Motion data may also or alternatively be obtained from other sources, such as a camera capturing active images, a weight or weight training device in an integrated motion sensor, etc. Recognizing an activity may, for example, include applying any suitable activity recognition algorithm (such as a machine learning algorithm, a statistical classification scheme, etc.) to motion data obtained from a wearable monitor or other sources. On the other hand, method 400 may include attempting automatic type detection, and requesting user input when automatic detection cannot reliably (e.g., with sufficient statistical confidence) identify the type. In one aspect, method 400 may include continuously tracking motion and attempting to identify known patterns of strength training activities. On the other hand, recognition may be attempted only during a known trial time or in response to a clear user request.

[0077] Where activities are automatically identified, additional processing may be effectively applied. For example, an activity may be evaluated to determine whether each repetition is completed correctly with the full expected range of motion. Certain features of the repetition may also or alternatively be used to measure the level of effort of the repetition. For example, repetitions that vary in speed, or that contain jerky movements, or that are abandoned due to stopping midway, may indicate greater musculoskeletal effort than would be expected from a smooth repetition performed at a similar speed to the previous repetition. Such variations are often observable in motion data. Various statistical measures such as mean signal amplitude, standard deviation, rate of change, etc. may be used to quantify these variations. Intensity metrics may then be compiled based on different statistical quantifications of the motion signal to distinguish between different levels of musculoskeletal effort.

[0078] As shown in step 412, method 400 may include identifying a number of repetitions in a set of strength training activities. In one aspect, this may include receiving user input specifying the number of repetitions in the set, or otherwise determining the number of repetitions based on the user input. In another aspect, this may include determining the number of repetitions in the set based on a change in amplitude of the triaxial acceleration data, or otherwise determining the number based on the user input. For example, Figure 6As shown, the time-varying velocity can be derived from the three-axis acceleration data, any of which can exhibit certain periodic characteristics of repetitions that indicate exercise repetitions. Therefore, the velocity data can be used to support automatic detection of repetitions of certain types of exercises. Cameras or other tracking devices / systems can also or alternatively be used to identify the number of repetitions in an activity and / or an activity. On the other hand, this can include using other data sources or technologies to detect the number, such as by receiving a repetition count from an exercise device, extracting repetition count information from a video image of the activity, such as obtained by a smart phone, camera or other image source, or receiving motion data from any other source described herein.

[0079] As shown in step 414, the method may include calculating the strength of the group.

[0080] In one aspect, this can include, for each repetition, calculating a raw intensity score indicative of musculoskeletal movement based on changes in triaxial acceleration data. As described above, intensity generally measures effort relative to the user's ability. This raw measure of intensity can be evaluated based on the motion data. For example, intensity can be evaluated by calculating the difference from one instantaneous acceleration measurement to the next instantaneous acceleration measurement (also known as "jerk," or the change in acceleration between two measurements) over a series of acceleration measurements taken over the repetition, and then dividing that number by the acceleration magnitude. The intensity of the exercise repetition can then be calculated by averaging this instantaneous intensity for all samples during the concentric phase of the repetition:

[0081]

[0082] Intensity rep =Mean(Intensity sample )

[0083] It will be appreciated that other measures of intensity may be derived based on the motion. For example, in one aspect, the intensity of the repetition may be calculated as follows:

[0084]

[0085] For some types of exercise, the latter method may be less sensitive to small changes in acceleration or jerkiness, or less sensitive to changes in motion occurring at concentric phase boundaries. Therefore, in one aspect, calculating the raw intensity score of a repetition in a set of repetitions includes calculating the average of multiple instantaneous intensity measurements of the repetition. In one aspect, one or more of the multiple instantaneous intensity measurements are calculated based on the average of the ratio of the current acceleration change to the current acceleration of the discrete measurement. On the other hand, one or more of the multiple instantaneous intensity measurements are calculated based on the ratio of the first average value of the current change of acceleration to the second average value of the current acceleration. More generally, any metric that objectively characterizes intensity based on the changes in motion during the repetition of a strength training activity and / or during a set of such repetitions may also or alternatively be used to measure intensity and calculate musculoskeletal burden, as contemplated herein. As a significant advantage, acceleration-based intensity measurement allows the capture of the spatial range of motion directly related to the work done, as well as any jitter in the motion indicating a high individual burden. Due to the accumulation of acceleration changes caused by jitter, this relatively high-speed sporadic motion that deviates from the typical exercise path will appear as a higher intensity score in quantity. For activities where direct measurement of motion is not possible, proxies for intensity can be used, such as the duration of static isometric exercise. Even in these situations where there is no overt muscle movement, muscle twitching may still manifest in a way that can be detected, measured, and used to quantitatively assess intensity.

[0086] As described above, calculating intensity may also include adjusting the raw intensity score based on a history of the user's activity. To personalize intensity in this manner, method 400 may include, for each repetition in a set (or for the entire set), determining the maximum intensity at which the user performed the strength training activity. The maximum intensity may indicate the user's ability to perform the strength training activity based on the user's exercise history. Various techniques may 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 obtaining a value from a load repetition profile (e.g., Figure 5A scaling factor is retrieved from a load repetition profile (as shown) that characterizes a set of repetitions relative to the user's maximum capacity over a range of loads and repetition counts. The intensity score for a set of repetitions can then be expressed as the product of the (motion-based) raw intensity score and the scaling factor that indicates the user's maximum ability to perform repetitive exercises at a particular load. On the other hand, the scaling factor can be estimated or interpolated based on the observed or user-reported maximum load for a particular exercise, or the observed or reported maximum number of repetitions for multiple different loads. More generally, any technique suitable for quantitatively determining a user's maximum ability for a strength training activity and / or scaling observed activities relative to maximum ability can be used to scale the raw intensity score for a set of repetitions and provide an intensity score for the user. All of these techniques are intended to fall within the scope of the present disclosure so long as they support useful calculations of musculoskeletal burden scores as further described below.

[0087] It will also be appreciated that other techniques for calculating intensity are known in the art and can be applied to wearable health monitors as described herein. For example, intensity can be evaluated based on the speed, linearity and / or continuity of the motion associated with the exercise, any of which can be effectively detected with the motion sensor described herein. For example, the amount of effort can be identified based on how clean the path of the exercise motion is, or in other words, the amount of effort can be identified based on the amount of noise relative to the user's expected trajectory and / or historical trajectory in the motion. Various linear, continuous and / or change measurements are mathematically known and can be effectively used as an estimator of the intensity based on the motion. In one aspect, the amount of motion from the variability of the overall expected value and / or the amount of motion that exceeds the expected change can be used for individuals. On the other hand, local measurements of changes in directionality (e.g., the number and amplitude of changes in direction) or speed (e.g., the number and amplitude of changes in speed) can be used to identify when the increased load causes the motion to be less smooth or less continuous. In one aspect, the load can be measured across the major muscle groups targeted by the exercise. For example, when a user performs a bicep curl, the load on the bicep can be estimated based on any of the above-mentioned motion factors, as measured with a wrist-worn monitor. As another example, when a user is doing squats, monitors located on the thigh or calf can be used to estimate the load on various leg muscles. As another example, in the case where the user is doing push-ups, curls, or another bodyweight exercise, a monitor on the user's torso can be used to estimate intensity based on the range of motion of the torso portion. The intensity score can also be further contextualized using additional data as described herein, such as a particular user's maximum or typical weight, other relevant training for the corresponding muscle group, etc.

[0088] As shown in step 416, method 400 may include updating the user profile. This may generally include updating the load repetition profile or any other user profile when new data becomes available. In one aspect, this may include adding columns or rows to the profile. In another aspect, this may include updating other entries in the profile when, for example, the current group repetition exceeds an expected maximum.

[0089] As shown in step 418, method 400 may include calculating the amount of strength training activity. As described herein, amount generally refers to the total amount of work done during a trial (or other time period). The amount for a user can be personalized based on two components: maximum amount and effective load. Therefore, method 400 may include, for example, estimating the maximum amount or other predetermined threshold value of a user when performing a strength training activity based on the user's history of performing strength training activities. The maximum amount (or estimated maximum amount) can, for example, indicate an upper threshold value for a user to repeat a strength training activity without injury.

[0090] Method 400 may also include calculating the effective load of the user during the strength training activity. The effective load may, for example, indicate the relative portion of the maximum amount applied by the user during the strength training activity based on one or more load parameters. One or more load parameters may at least include the user's weight and the increased weight of the strength training activity. For example, calculating the effective load may include calculating the effective load based on the user's weight input, for example, where the strength training activity is an activity based entirely or partially on the user's weight, such as pull-ups, push-ups, or squats. Calculating the effective load may also be based on the user's input of the increased weight of the strength training activity, for example, when the user uses hand-held weights, barbell weights, or weights on a strength training machine, or when the user adds weight to another activity (such as pull-ups, push-ups, or squats) to increase the amount. In some instances, for example, when using a strength training machine, body weight may be ignored, and / or some other load parameters may be included, such as arm length or leg length.

[0091] As shown in step 420, method 400 may include calculating a musculoskeletal strain score. For example, this may include calculating the musculoskeletal strain per repetition for each repetition as the product of a first ratio of the effective load to the maximum amount and a second ratio of the raw intensity score to the maximum intensity, and then summing the musculoskeletal strain per repetition for all repetitions in the group to provide a musculoskeletal strain score for the strength training activity. In general, this may include calculating the musculoskeletal strain score on a personal computing device of a user coupled to the wearable monitor in a communication relationship, or calculating the musculoskeletal strain score on a remote server coupled to the wearable monitor in a communication relationship, or some combination of these.

[0092] The MSK burden score can then be calculated for each exercise set using the formula below:

[0093]

[0094] Where M is the number of repetitions in a set, and are the amount and intensity of repetition i, V max and I max are the maximum possible amount and intensity values, respectively, and rel refers to the relative amount and intensity of the replicates. Has been replaced by V rep , because it is a constant that repeats within a group. On the other hand, this is not I rep In the case of rep It can be calculated for each repetition based on available motion data.

[0095]

[0096] To accumulate MSK over multiple workouts:

[0097]

[0098] Where N is the number of sets of the exercise.

[0099] Finally, for sessions consisting of multiple workouts:

[0100]

[0101]

[0102] Where L is the number of exercises in a session.

[0103] Volume and intensity maximums can be calibrated for an individual and can change as an individual's strength changes over time.

[0104] Using these, the MSK burden for each repetition of exercise can be calculated. Aggregating individual MSK burden scores can produce a trial level score. At this level, MSK burden can be aggregated at the muscle group level and / or at the whole body level. When sensor data is available, intensity can be based on the acceleration or speed of the movement. When sensor data is not available, effort can still be determined based on, for example, previous trials, demographic criteria, and / or self-reported effort levels. Therefore, the systems and methods described herein can perform MSK scores with motion data, without motion data, or some combination of these.

[0105] As described herein, the raw MSK burden can be an infinite linear cumulative score. That is, the more effort one puts into an activity, or the more repetitions one performs, the higher the score. In order to provide a limited range for the user, these raw scores can be scaled to adjust the value based on the user's personal performance profile. In some aspects, this can be achieved through a two-stage process, including (1) exercise-specific normalization, and (2) performance normalization. For example, an individual's daily MSK burden can be converted into a score of 0-21 or any other suitable range.

[0106] As shown in step 422, method 400 may include taking action based on the musculoskeletal (MSK) burden score. The MSK burden score provides a highly actionable metric for strength training activities, placing it in the context of a particular type of exercise, demonstrated history of ability, and the user's estimated injury limit. In one aspect, taking action may include, for example, displaying the musculoskeletal burden score or any other derived metric or analysis of the strength training activity to the user on a user device for viewing.

[0107] In another aspect, taking action can include refining a daily burden calculation for the user based on the musculoskeletal burden score (e.g., a cardiac burden calculation based on heart rate and / or heart rate variability). For example, a burden calculation based on cardiovascular activity (as described, for example, in U.S. Pat. No. 11,185,292, which is incorporated herein by reference) may underestimate the actual effort during strength training activities. By determining the MSK load during strength training or other exercise, the user's total daily burden can be updated to more accurately reflect the burden due to muscular effort and cardiovascular effort.

[0108] In another aspect, taking action may also or alternatively include calculating multiple musculoskeletal strain scores for each of the multiple types of strength training activities in the exercise program. For an entire exercise program consisting of multiple individual strength training activities, these may be aggregated into a single MSK strain score, and / or may be reported to the user as a single score or multiple scores.

[0109] Taking action may also or alternatively include generating coaching advice to the user based on the musculoskeletal load score, and / or displaying coaching advice to the user. This may include generating advice based on stated user goals or health goals, for example, by suggesting increases when appropriate, or suggesting reductions when there are signs of approaching the user's maximum amount or otherwise exceeding the recommended training limit. In one aspect, coaching advice may be real-time coaching advice presented to the user during a strength training activity. For example, this may include suggestions for increasing amount (e.g., by additional repetitions or adding weight), or warnings about approaching maximum amount. On the other hand, coaching advice may relate to subsequent exercise activities of the user, such as subsequent activities in the current trial, or the same activity (or different activities) in future trials. Coaching advice may also or alternatively include suggestions about the time of the next strength training activity.

[0110] In accordance with the foregoing, a system for calculating a musculoskeletal burden score is also described herein. The system may include a wearable health monitor and one or more processors. The wearable health monitor may be any wearable monitor described herein and may include one or more motion sensors. The one or more processors may, for example, include a processor on the wearable health monitor, a processor on a user device, a processor on a remote server, or some combination of these. One or more processors may be configured by computer executable code stored in a non-transitory computer readable medium to perform the following steps: receiving motion data obtained from one or more motion sensors during a strength training activity, identifying a type of strength training activity, identifying a set of strength training activities comprising one or more repetitions, for each repetition, calculating a raw intensity score indicating musculoskeletal movement based on characteristics of the motion data, and scaling the raw intensity score relative to a maximum intensity at which a user performed the strength training activity to obtain a per-repetition user intensity score, the maximum intensity indicating the user's ability to perform the strength training activity based on the user's exercise history, for each repetition, calculating an individualized scale based on a ratio of the user's effective load during the strength training activity to a predetermined load threshold for the user when performing the strength training activity, calculating a per-repetition musculoskeletal burden for each repetition as the product of the per-repetition user intensity score and the individualized scale, calculating a musculoskeletal burden score for the strength training activity by summing the per-repetition musculoskeletal burden for all repetitions in the set, and taking action based on the musculoskeletal burden score.

[0111] In one aspect, taking action may include transmitting the user-specific musculoskeletal strain score to a personal computing device associated with the user for display to the user. In another aspect, taking action may include generating coaching advice for the user.

[0112] In general, the methods described herein may include more or fewer steps, or may refer to Figure 4 Variations of each of the steps described. For example, in one aspect, disclosed herein is a method comprising: receiving motion data from one or more motion sensors of a wearable monitor worn by a user during a strength training activity comprising a set of one or more repetitions; identifying a type of strength training activity; determining the number of repetitions in the set; for each repetition, calculating a raw intensity score indicating musculoskeletal movement based on features of the motion data, and scaling the raw intensity score relative to a maximum intensity at which the user performed the strength training activity to obtain a per-repetition user intensity score, the maximum intensity indicating the user's ability to perform the strength training activity based on the user's exercise history; for each repetition, calculating an individualized scale based on a ratio of the user's effective load during the strength training activity to a predetermined load threshold for the user performing the strength training activity; calculating a per-repetition musculoskeletal burden for each repetition as the product of the per-repetition user intensity score and the individualized scale; calculating a musculoskeletal burden score for the strength training activity by summing the per-repetition musculoskeletal burden for all repetitions in the set; and taking action based on the musculoskeletal burden score.

[0113] On the other hand, the method described herein includes receiving motion data from one or more motion sensors of a wearable health monitor worn by a user during a strength training activity; calculating an individualized musculoskeletal burden on the user during the strength training activity by adjusting the intensity associated with the motion data based on the user's exercise history associated with the strength training activity, the effective load during the strength training activity, and the user's maximum amount associated with the strength training activity; and taking action based on the individualized musculoskeletal burden.

[0114] On the other hand, the method described herein includes receiving motion data from one or more motion sensors of a wearable health monitor worn by a user during a strength training activity; calculating a raw intensity 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, the load parameters including at least the user's weight and an added 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 effective load calculated based on the one or more load parameters; and presenting information to the user based on the individualized musculoskeletal strain score.

[0115] Figure 5 500 is a load repetition profile for scaling exercise repetition intensity. Typically, the profile 500 can be used to scale a user's raw intensity score based on a trial history. Typically, a maximum intensity value can be established at a user's one repetition maximum ( Figure 5Load 10, Rep 1). Based on the maximum intensity of a single repetition, a scale can be developed for all intensity values ​​for a given individual and exercise, for example, by inserting an effort percentage for scaling the calculated intensity based on the maximum possible value for the exercise. Profile 500 can also be adjusted when new failed repetitions are observed, and new rows can be added when the user adds a new number of repetitions or a new load. As additional user data becomes available, numbers can generally be adjusted, recalculated, re-interpolated, etc. In one aspect, if the user exceeds the predicted maximum, for example, by performing one or more repetitions at a load that exceeds the current maximum, profile 500 can be adjusted before calculating the intensity, and the adjusted profile 500 can be used to calculate the user's intensity for those repetitions. This advantageously avoids calculating intensity values ​​that exceed the user's theoretical maximum.

[0116] On the other hand, before the profile 500 is fully populated, the intensity scale may be initially estimated based on, for example, weight or other factors, in order to support the user's intensity calculations.

[0117] Figure 6 It is a graph showing the speed over time measured during bench press exercise. Generally speaking, speed can be derived from triaxial acceleration data, or derived from other motion data obtained from wearable monitors or some other motion data sources. Although a group of bench press is shown, it will be understood that any other exercise with measurable repetition in motion can be detected similarly. As shown, five large peaks 602 in the speed data indicate five repetitions of exercise, and vertical lines 604 are added to indicate the measurable landmarks at the end of each repetition. Various signal processing techniques can be used to identify this repetition, including frequency domain techniques, time domain peak detection, etc. Any such technology that is suitable for identifying the periodic cycle of speed measurement that indicates exercise repetition can be used to automatically detect repetition, as described herein.

[0118] Figure 7 The triaxial acceleration data for one repetition of a bench press exercise is shown. Figure 7 The data in is fused data that has been processed to mitigate gravity-based acceleration artifacts. This data can be used, for example, to calculate intensity scores, and / or derive velocity data (such as Figure 6 ), this velocity data can be used to identify individual repetitions within a set of strength training activities.

[0119] Figure 8 Shown from Figure 7 The integral of the normalized acceleration magnitude of the data. This can be used, for example, to quantitatively assess the intensity of the exercise and then scaled according to the maximum intensity, maximum volume, and effective load described herein to obtain the MSK score for repeated strength training activities.

[0120] Fig. 9A system for monitoring musculoskeletal (MSK) strain is shown. System 900 may include a user 901 wearing a physiological monitor 910, a user device 920 having a display 922 suitable for providing information to user 901, a data network 902 interconnecting one or more participants of system 900, a server 930, and a database 940. In general, Fig. 9 User 901 is shown performing an exercise - e.g., a weight training exercise such as bicep curls - with weights 902 in the form of a barbell with weight plates thereon. As described herein, motion and / or physiological data sensed by physiological monitor 910 may be used to calculate an MSK burden score for user 901, which may be displayed to the user via user device 920 along with other relevant information (e.g., during and / or after the exercise itself).

[0121] Physiological monitor 910 may include a wrist-worn photoplethysmography device. Physiological monitor 910 may also or alternatively include monitors disposed at other locations on the body of user 901, such as biceps, thighs, calves, etc. In one aspect, physiological monitor 910 includes at least one accelerometer, gyroscope, etc., for sensing motion and providing motion data to user 901. In other aspects, accelerometer data or other motion sensor data is obtained from a source external to physiological monitor 910.

[0122] The user 901 may be performing an exercise such as a strength training activity, wherein motion data (and / or physiological data) is provided to the user device 920 and / or server 930 for analysis. In general, the motion data captured by the motion sensor during the exercise can be analyzed to obtain the MSK burden score as described herein. The score can be used, for example, to provide coaching information to the user 901, for example, for adjusting the exercise and / or providing other training suggestions. As an example and not limitation, the user 901 is shown to perform a biceps curl with a barbell as a weight 902, wherein the motion data from the sensed motion 904 in this example may include three-axis acceleration data describing the movement during the repetitive exercise. This may include motion data obtained by the physiological monitor 910 as described above, or motion data obtained by a motion sensor in a weight 902 (including a barbell or a weight added thereto), motion data obtained by a camera in the user device 920, or motion data obtained from any other suitable source. Although free weight exercises are depicted, it will be appreciated that the systems and methods described herein can be used to calculate MSK burden in various other strength training activities, such as weight lifting exercises (e.g., using free weights and / or weight training machines), bodyweight or isometric exercises (e.g., push-ups, sit-ups, squats, burpees, curls, leg raises, etc.), cardiovascular exercises (e.g., walking, running, cycling, swimming, elliptical exercise, circuit training, skipping, sports participation and / or training, dancing, etc.), etc. The motion data can also be used for other coaching suggestions, such as suggestions related to repetition speed, range of motion, form, etc. Therefore, in one aspect, a system and method are described herein for providing coaching suggestions to a user engaging in a strength training activity based on the motion sensed during the strength training activity. The suggestions can relate to one or more of speed, range of motion, and form of strength training activity.

[0123] Data network 902, user device 920, server 930, and database 940 may be any of those described herein. In general, data network 902 may support communications between participants in system 901, such as motion data and / or physiological data sensed by physiological monitor 910 being provided to server 930 or user device 920 for processing. Such data and / or analysis results of the data may be stored in database 940, which may be a local or remote database as described herein. Database 940 may also or alternatively store user profiles, load repetition profiles, etc., as described herein.

[0124] 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 presented on the display 922 may include any output as described herein, including an MSK burden score 924, coaching suggestions, a workout plan including a plurality of consecutive strength training activities, repetitions completed in a particular set, and the like.

[0125] In an example use case, system 900 may receive information related to an exercise being performed by user 901, such as the type of exercise, set or repetition descriptions or metrics, training goals, weights 902 added or loads being used, information related to user 901 (e.g., height, weight, gender, etc.), and so on. In some aspects, this information may include motion data from physiological monitor 910 or other source(s).

[0126] Fig.10 A system for monitoring MSK burden is shown. System 1000 may include any of the features described herein, such as those described above with respect to Fig. 9 Any characteristics discussed. Fig.10 As shown, the system 1000 may include a weight training machine 1002. A physiological monitor 1010 may be disposed on the user's leg to detect movement of the leg during a leg strength training activity. In one aspect, the weight training machine 1002 may be a smart device that is capable of transmitting data such as current weight / load, number of repetitions, range of motion, and other data to the physiological monitor 1010 or other system resources for calculating an MSK burden score. The weight training machine 1002 may also or alternatively include a camera for capturing images that may be used to derive motion data.

[0127] Fig.11 A system for monitoring MSK burden is shown. System 1100 may include any of the features described herein. As shown, user 1101 is performing an isometric exercise, more specifically a plank. In this type of exercise, certain adaptations to the burden score may be used. For example, in a plank (or some other isometric exercise), there are no literal repetitions. Instead, a repetition metric may be derived, for example, based on the amount of time the exercise is maintained. Thus, for example, a plank may be counted as one repetition every five seconds, so that one minute of performing a plank is equivalent to twelve repetitions. Similarly, there will be no periodic motion upon which automatic detection of repetitions is based, but in cases where the effort is high, there may be a tremor in the shoulders, arms, or abdomen that may be detected by a wearable physiological monitor 1102 and used to calculate the intensity of the activity.

[0128] Fig.12A user interface of a strength training system with an MSK burden score is shown, for example, using the systems and methods described herein. In one aspect, user interface 1200 may include multiple controls to configure a trial program, for example, by specifying the type of strength training activity, and the appropriate repetitions and weights of each strength training activity. Through the interface, a user may configure the exercise program by adding exercise groups, deleting exercise groups, or specifying the details of exercise groups within the program. The trial program may then be saved for future use and used as a guide in the current trial.

[0129] Fig.13 A user interface of a strength training system with an MSK burden score is shown, for example, using the methods and systems described herein. In one aspect, the user interface 1300 can display an MSK burden score 1302 that quantitatively summarizes the amount of musculoskeletal burden of the user for the day or any other suitable time period. The user interface 1300 can also display other useful information, such as current or recent strength training activities, the amount of cardiovascular and muscle burden for the day, coaching advice, etc. In this case, the MSK burden score 1302 can advantageously provide the user with concise, quantitative, objective feedback about recent strength training activities, as informed by the identified activities and measurements of physical movement.

[0130] In user interface 1300, the user can also track the current trial, modify the current trial (e.g., by changing the weight, repetitions, or activities), view previous trials, create new trials, etc. The user can also view related information describing time, weight, effort, cardiovascular burden, etc. In another aspect, user interface 1200 can provide interactive instructions for performing different types of exercises and can provide motion-based feedback on the user's form for a particular exercise.

[0131] The above-mentioned systems, devices, methods, processes, etc. can be implemented in hardware, software, or any combination of these suitable for the control, data acquisition, and data processing described herein. This includes implementation in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors, or other programmable devices or processing circuits, and 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 any other one or more devices that can be configured to process electronic signals. It will also be appreciated that the implementation of the above-mentioned processes or devices may include computer executable code created using a structured programming language such as C, an object-oriented programming language such as C++, or any other high-level or low-level programming language (including assembly language, hardware description language, and database programming language and technology), which can be stored, compiled, or interpreted to run on one of the above-mentioned devices, as well as a heterogeneous combination of processors, processor architectures, or a combination of different hardware and software.

[0132] Therefore, in one aspect, each of the above methods and combinations thereof can be embodied in a computer executable code that performs its steps when executed on one or more computing devices. On the other hand, the method can be implemented in a system that performs its steps and can be distributed across devices in a variety of ways, or all functionality can be integrated into a dedicated stand-alone device or other hardware. The code can be stored in a non-temporary manner in a computer memory, which can be a memory from which a program is executed (e.g., a random access memory associated with a processor), or a storage device, such as a disk drive, flash memory, or any other optical, electromagnetic, magnetic, infrared or other device or combination of devices. On the other hand, any of the systems and methods described above can be implemented in any suitable transmission or propagation medium that carries computer executable code and / or any input or output from the computer executable code. On the other hand, the components for performing the steps associated with the above process can include any of the above hardware and / or software. All such permutations and combinations are intended to fall within the scope of the present disclosure.

[0133] The method steps of the implementation described herein are intended to include any suitable method so that the method steps are performed, consistent with the patentability of the attached claims, unless a different meaning is explicitly provided or is otherwise clear from the context. Thus, for example, performing step X includes any suitable method for causing another party 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 method of directing or controlling any combination of such other individuals or resources to perform steps X, Y, and Z to obtain the benefits of such steps. Therefore, the method steps of the implementation described herein are intended to include any suitable method for causing one or more other parties or entities to perform the steps, which is consistent with the patentability of the attached claims, unless a different meaning is explicitly provided or is otherwise clear from the context. Such parties or entities do not need to be directed or controlled by any other party or entity, nor do they need to be located in a particular jurisdiction.

[0134] It will be appreciated that the above methods and systems are set forth by way of example and not limitation. Many variations, additions, omissions, and other modifications will be apparent to those of ordinary skill in the art. In addition, the order or presentation of the method steps in the above description and accompanying drawings is not intended to require such an order in which the steps are performed, unless a particular order is expressly required or is otherwise clear from the context. Therefore, although specific embodiments have been shown and described, it will be apparent to those skilled in the art that various changes and modifications may be made therein in form and detail without departing from the spirit and scope of the present disclosure, and these changes and modifications are intended to form a part of the present invention as defined by the appended claims.

Claims

1. A computer program product comprising 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 the following steps: 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 repetitions, the raw motion data comprising 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 mitigate gravity artifacts to provide motion data including three-axis acceleration data; identifying the type of strength training activity; determining the number of the repetitions in the set based on a change in the amplitude of the triaxial acceleration data; for each of said repetitions, calculating a raw intensity score indicative of musculoskeletal movement based on changes in said triaxial acceleration data; for each of the repetitions, determining a maximum intensity at which the user performed the strength training activity, the maximum intensity indicating an ability of the user to perform the strength training activity based on the user's exercise history; estimating a maximum amount of the user and the strength training activity based on a history of the user performing the strength training activity, wherein the maximum amount indicates an upper threshold for the user to repeat the strength training activity without injury; calculating an effective load of the user during the strength training activity based on one or more load parameters, the effective load indicating a relative portion of the maximum amount exerted by the user during the strength training activity, the one or more load parameters including at least the user's body weight and an added weight of the strength training activity; calculating a per repetition musculoskeletal burden for each of said repetitions as a product of a first ratio of said effective load to said maximum amount and a second ratio of said raw intensity score to said maximum intensity; summing the per-repetition musculoskeletal strain for all repetitions in the set to provide a musculoskeletal strain score for the strength training activity; as well as The musculoskeletal strain score for the strength training activity is displayed to the user.

2. The computer program product of claim 1, further comprising code that causes the one or more computing devices to generate coaching recommendations to the user based on the musculoskeletal strain score.

3. The computer program product according to claim 2, wherein: The coaching advice is based at least in part on the user's health goals.

4. A computer program product according to any one of the preceding claims, wherein: Calculating the raw intensity score for one of the repetitions includes calculating an average of a plurality of instantaneous intensity measurements for one of the repetitions.

5. The computer program product according to claim 4, wherein: One or more of the plurality of instantaneous intensity measurements are calculated based on an average of ratios of discretely measured current acceleration changes to the current acceleration.

6. The computer program product according to claim 4, wherein: One or more of the plurality of instantaneous intensity measurements are calculated based on a ratio of a first average value of a current change in acceleration to a second average value of a current acceleration.

7. A computer program product according to any of the preceding claims, further comprising code that causes the one or more computing devices to perform the following steps: creating a load repetition profile for the user based on the history of the strength training activity of the user, the load repetition profile indicating the repetition ability of the user under one or more loads during the strength training activity.

8. The computer program product of claim 7 further comprising code that causes the one or more computing devices to perform the following steps: adding a row to the load repetition profile when the user performs the strength training activity at a new load that is not included in the one or more loads in the load repetition profile.

9. The computer program product of claim 7, further comprising code for causing the one or more computing devices to perform the following steps: updating the load repetition profile when the user exceeds the number of repetitions of one of the loads in the load repetition profile.

10. The computer program product of any of the preceding claims, further comprising code for receiving user input specifying the type of the strength training activity.

11. The computer program product of any of the preceding claims, further comprising code for causing the one or more computing devices to perform the step of identifying the type of the strength training activity based on the raw motion data.

12. A method comprising: receiving motion data from one or more motion sensors of a wearable monitor worn by a user during a strength training activity comprising a set of one or more repetitions; identifying the type of strength training activity; determining the number of said replicates in said set; for each of the repetitions, calculating a raw intensity score indicative of musculoskeletal movement based on features of the motion data, and scaling the raw intensity score relative to a maximum intensity of the user performing the strength training activity to obtain a per-repetition user intensity score, the maximum intensity indicative of the user's ability to perform the strength training activity based on the user's exercise history; for each of the repetitions, calculating an individualized scale based on a ratio of the effective load of the user during the strength training activity to a predetermined load threshold for the user performing the strength training activity; calculating a per-repetition musculoskeletal burden for each of said repetitions as a product of said per-repetition user intensity score and said individualized scale; calculating a musculoskeletal strain score for said strength training activity by summing said per-repetition musculoskeletal strain for all of said repetitions in said set; as well as Action is taken based on the musculoskeletal strain score.

13. The method according to claim 12, wherein: Determining the number of the repetitions in the set includes determining the number based on motion data.

14. The method according to any one of claims 12 to 13, wherein: Determining the number of the repetitions in the set includes determining the number based on user input.

15. The method according to any one of claims 12 to 14, wherein: The actions include refining a daily burden calculation for the user based on the musculoskeletal burden score.

16. The method according to any one of claims 12 to 15, wherein: The actions include generating coaching advice for the user.

17. The method of claim 16, further comprising displaying the coaching advice to the user.

18. The method according to any one of claims 16 to 17, wherein: The coaching advice is real-time coaching advice.

19. The method according to any one of claims 16 to 17, wherein: The coaching advice is associated with a subsequent exercise activity of the user.

20. The method of any one of claims 12 to 19, further comprising automatically identifying the type of the strength training activity based on the motion data.

21. The method of any one of claims 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 program.

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 effective load based on user input of an incremental weight for the strength training activity.

24. The method according to any one of claims 12 to 23, wherein: The motion data includes raw motion data from a three-axis gyroscope and a three-axis accelerometer, and the raw motion data is fused to provide the repeated three-axis acceleration data while mitigating the acceleration effect caused by gravity.

25. The method according to any one of claims 12 to 24, wherein: The wearable monitor includes a wrist-worn photoplethysmographic device.

26. The method according to any one of claims 12 to 25, wherein: Receiving motion data includes receiving raw motion data from at least one gyroscope and at least one accelerometer of the wearable monitor.

27. The method according to any one of claims 12 to 26, wherein: Calculating the musculoskeletal strain score includes calculating the musculoskeletal strain score on a personal computing device of the user coupled in a communicative relationship with the wearable monitor.

28. The 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 coupled in a communicative relationship with the wearable monitor.

29. The method according to any one of claims 12 to 28, wherein: The predetermined load threshold is an estimated maximum amount that indicates an upper threshold for the user to repeat the strength training activity without injury.

30. A system comprising: a wearable health monitor including one or more motion sensors; as well as One or more processors configured to calculate a user-specific musculoskeletal strain score for a user of the wearable health monitor by performing the following steps: receiving motion data obtained from the one or more motion sensors during a strength training activity, identifying the type of strength training activity, identifying a set of said strength training activity comprising one or more repetitions, for each of the repetitions, calculating a raw intensity score indicative of musculoskeletal movement based on features of the motion data, and scaling the raw intensity score relative to a maximum intensity at which the user performed the strength training activity to obtain a per-repetition user intensity score, the maximum intensity indicative of the user's ability to perform the strength training activity based on the user's exercise history, for each of the repetitions, calculating an individualized scale based on a ratio of the user's effective load during the strength training activity to a predetermined load threshold for the user while performing the strength training activity, calculating a per-repetition musculoskeletal burden for each of said repetitions as the product of said per-repetition user intensity score and said individualized scale, calculating a musculoskeletal strain score for said strength training activity by summing said per-repetition musculoskeletal strain for all of said repetitions in said set, and Action is taken based on the musculoskeletal strain score.

31. The system of claim 30, wherein: Taking the action includes transmitting the user-specific musculoskeletal strain score to a personal computing device associated with the user for display to the user.

32. A system according to any one of claims 30 to 31, wherein: Taking the action includes generating coaching advice for the user.

33. A method comprising: 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 burden for the user during the strength training activity by adjusting the intensity associated with the motion data based on the user's exercise history associated with the strength training activity, the effective load during the strength training activity, and the user's maximum volume associated with the strength training activity; as well as Action is taken based on the individualized musculoskeletal burden.

34. A method comprising: 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 intensity score for a plurality of repetitions of the strength training activity based on the motion data; Calculating an effective load of the strength training activity based on one or more load parameters, the load parameters including at least the weight of the user and the added weight of 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 effective load calculated based on the one or more load parameters; as well as Information is presented to the user based on the individualized musculoskeletal strain score.

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