Sensor Fusion for Exercise Posture and Muscle Activation
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Solution Overview
Problem
Current fitness training methods lack effective means to ensure accurate posture, proper muscle group usage, and sequence during exercises, leading to potential injuries and a lack of quantifiable exercise effectiveness, especially when training without a coach.
Innovation Solution
A multiple sensor-fusing based interactive training system incorporating inertia and myoelectric sensors to sense posture and muscle activity data, convert it into relevant coordinates, perform fusion calculations, and provide real-time feedback on exercise accuracy and effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple instructions on fitness equipment are used for training, then ease of operation is improved, but measurement precision of exercise effectiveness deteriorates
Solution Approach 1:
The system implements real-time feedback by continuously monitoring posture data through inertia sensors and myoelectric data through myoelectric sensors, converting these into evaluation data that provides quantitative feedback on exercise effectiveness, posture accuracy, and muscle group activation. This feedback loop allows users to adjust their exercise form based on objective measurements while maintaining ease of operation.
Solution Approach 2:
The patent replaces manual mechanical assessment by coaches with an automated sensor-based evaluation system. Inertia sensors capture motion data, myoelectric sensors capture muscle activation, and the computing module processes these signals to generate quantitative evaluation data, substituting the mechanical inspection process with electronic sensing and computation.
2Measurement precision
If multiple sensors are used to sense posture and muscle activity, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple sensor types (inertia sensors and myoelectric sensors) into a unified evaluation system. The sensing module integrates data from both sensor types, and the computing module performs fusion calculation to combine posture data and muscle activity data into comprehensive evaluation results, reducing the complexity of managing separate assessment systems.
Solution Approach 2:
The evaluation system is designed with multi-functionality, serving multiple purposes: it assesses posture accuracy, measures muscle group activation, evaluates exercise effectiveness, and provides real-time feedback. This universal system replaces multiple specialized assessment tools with a single integrated platform.
3Reliability
If real-time sensor feedback is provided, then reliability of exercise safety is improved, but use of energy increases
Solution Approach 1:
The system maintains continuous monitoring throughout the exercise process, with sensors continuously capturing posture and muscle activity data. The computing module continuously processes this data to provide real-time feedback, ensuring uninterrupted safety monitoring and reliable exercise guidance without requiring periodic interruptions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively judges the accuracy of posture, muscle group usage, and exercise sequence, reducing the risk of injury and providing quantifiable feedback for improvement, enabling users to train safely and effectively without a coach.
Implementation Method 1
The inertia sensor is configured to sense multiple posture data related to a training action of a user
Implementation Method 2
the myoelectric sensor is configured to sense multiple myoelectric data related to the training action of the user
Data Source
AI summary
A multiple sensor-fusing based interactive training system, including a posture sensor, a sensing module, a computing module, and a display module, is provided. The posture sensor is configured to sense posture data and myoelectric data related to a training action. The sensing module is configured to output limb torque data according to the posture data, and output muscle group activation time data according to the myoelectric data. The computing module is configured to respectively convert the limb torque data and the muscle group activation time data into a moment-skeleton coordinate system and a muscle strength eigenvalue-skeleton coordinate system according to a skeleton coordinate system, perform fusion calculation, calculate evaluation data based on a result of the fusion calculation, and judge that the training action corresponds to a known exercise action according to the evaluation data. The display module is configured to display the evaluation data and the known exercise action.