Muscle Load Monitoring via Exercise Conversion Framework
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Solution Overview
Problem
Current performance monitoring systems for exercisers primarily rely on heart rate to measure training load, which fails to provide detailed muscle-specific load information, leading to a need for enhanced monitoring solutions that accurately track muscle load during physical activities.
Innovation Solution
A smart coaching service with a conversion framework that transforms types of physical activities into muscle load data by utilizing wearable devices equipped with sensors and a database containing conversion entries correlating exercises with muscle load coefficients, allowing for precise muscle load monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If heart rate measurements are used to monitor training load, then overall training intensity can be tracked, but muscle-specific load information is not provided
Solution Approach 1:
The patent segments the overall training load into muscle-specific components by dividing the monitoring function into exercise identification module, database lookup module, and muscle load calculation module. Each module handles a specific aspect of the conversion process, transforming general heart rate data into detailed muscle-specific load information without requiring a completely new complex system.
Solution Approach 2:
The patent introduces a conversion framework as an intermediary between heart rate measurements and muscle load assessment. This framework includes a database of exercise-muscle relationships that translates general training data into specific muscle load information, acting as a mediator that adds detail without direct physical measurement of each muscle.
2Measurement precision
If muscle-specific monitoring is implemented, then detailed muscle load data is obtained, but measurement complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-establishing a comprehensive database of exercise-muscle relationships and conversion coefficients before actual monitoring. This pre-computed framework allows the system to quickly translate heart rate and exercise type into muscle-specific load data without performing complex real-time calculations, thereby improving measurement precision while keeping detection difficulty low.
Solution Approach 2:
The patent changes the parameter representation from direct muscle measurement to derived muscle load coefficients. By using conversion factors that relate exercise intensity to muscle-specific load based on exercise type and duration, the system achieves precise muscle load assessment through parameter transformation rather than direct measurement.
3Reliability
If conversion framework with database is used, then muscle load calculation becomes accurate, but system requirements increase
Solution Approach 1:
The patent implements a universal conversion framework that can calculate muscle load for multiple different exercise types using a single standardized system. The database contains conversion coefficients for various exercises, allowing the same monitoring device to accurately assess muscle load across different sport disciplines without requiring exercise-specific hardware or multiple separate systems.
Data Source
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AI summary
There is provided a system for monitoring muscle load, the system configured to perform operations comprising: obtaining, from at least one sensor, measurement data on an exerciser; determining, based on the measurement data, a number of repetitions of a macroscopic movement performed during a physical exercise; determining, based on a conversion entry corresponding to a type of the physical exercise, muscle load coefficient of one or more muscles loaded in the physical exercise; utilizing the muscle load coefficient of the one or more muscles and the number of repetitions in determining muscle load data indicating muscle specific muscle load caused by the physical exercise performed by the exerciser; and outputting the muscle load data.