Wearable Musculoskeletal Strain Monitoring From Motion Data
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Solution Overview
Problem
Conventional fitness metrics, such as heart rate or heart rate variability, are inadequate for accurately characterizing musculoskeletal strain during strength training, necessitating improved methods for monitoring and quantifying musculoskeletal strain to provide effective coaching recommendations.
Innovation Solution
A wearable physiological monitor that uses motion data from gyroscopes and accelerometers to quantify musculoskeletal strain by calculating intensity scores, load parameters, and strain metrics, integrating them into a comprehensive strain score for real-time coaching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional fitness metrics such as heart rate or heart rate variability are used to monitor strength training, then the monitoring system is simple and easy to implement, but the accuracy and reliability of musculoskeletal strain characterization is insufficient
Solution Approach 1:
The patent segments the monitoring system into multiple independent sensor components (accelerometers, gyroscopes, and optionally magnetometers) that each measure specific motion parameters. These segmented sensors work together to comprehensively characterize musculoskeletal strain through fusion of their individual measurements, resolving the contradiction by dividing the measurement function across multiple specialized elements rather than relying on a single complex metric.
Solution Approach 2:
The patent employs multi-functional sensor modules that can detect multiple types of motion (linear acceleration, angular velocity, and potentially magnetic field orientation) using integrated sensor arrays. This multi-functionality allows a single wearable device to capture comprehensive biomechanical data for strain characterization without requiring multiple separate devices, thus improving measurement precision while controlling system complexity.
2Measurement precision
If motion sensors with multiple axes (gyroscopes and accelerometers) are used to capture comprehensive motion data, then the measurement precision of musculoskeletal strain improves, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent merges data from multiple sensor types (accelerometers and gyroscopes, and optionally magnetometers) into a unified measurement framework. By combining the linear acceleration data from accelerometers with angular velocity data from gyroscopes, the system creates a comprehensive three-dimensional motion profile that accurately characterizes musculoskeletal strain while managing data integration through coordinated sensor fusion algorithms.
Solution Approach 2:
The patent replaces complex mechanical strain measurement systems with electronic sensor-based detection. Instead of using mechanical strain gauges or force sensors that would require direct contact with muscle tissue, the system uses non-invasive accelerometers and gyroscopes to infer musculoskeletal strain from external motion patterns, significantly reducing device complexity while maintaining or improving measurement precision.
3Reliability
If manual data entry and subjective strain assessment are used, then the system is simple to operate, but the reliability and objectivity of strain measurement decreases
Solution Approach 1:
The patent implements self-service automated data collection where the wearable sensor device independently captures, processes, and transmits motion data without requiring manual intervention. The system automatically characterizes musculoskeletal strain by processing sensor readings through algorithms that compute strain metrics directly from the collected motion data, eliminating the need for users to manually enter data or subjectively assess strain levels, thereby ensuring objective and reliable measurements while maintaining ease of operation.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors motion data and provides real-time or near-real-time strain assessment feedback to the user or trainer. This automated feedback loop replaces subjective strain assessment by objectively measuring and reporting musculoskeletal strain levels based on processed sensor data, improving reliability while requiring minimal user input beyond initial setup.
Data Source
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AI summary
A physiological monitor uses patterns of motion during strength training activity, e.g., as detected by a wearable monitor, to evaluate a degree of muscular, musculoskeletal, and/or biomechanical strain experienced by a user while engaged in strength training. The resulting strain may advantageously be quantified and used to provide coaching recommendations, update daily strain metrics, and take other responsive actions.