Exercise Segmentation via Sensor Fusion and ML Classification
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Solution Overview
Problem
Individuals without access to personal trainers face challenges in monitoring and tracking their physical activities effectively, as existing solutions do not provide accurate and continuous feedback on exercise performance and progress.
Innovation Solution
A physical activity monitoring device equipped with a sensor array and controller that determines active engagement in physical activities and types of exercises, using machine learning to segment and recognize patterns, thereby providing accurate tracking and feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated exercise monitoring is implemented without personal trainer access, then cost and accessibility are improved, but measurement precision and reliability of exercise tracking deteriorate
Solution Approach 1:
The patent replaces the mechanical system of personal trainer supervision with an automated electronic monitoring system using accelerometers, gyroscopes, and signal processing algorithms to detect and classify exercise movements, making the system both accessible and accurate simultaneously
Solution Approach 2:
The patent introduces an intermediary automated analysis system that processes sensor data through signal filtering, feature extraction, and machine learning classification to bridge the gap between simple sensor measurement and accurate exercise recognition, achieving both accessibility and precision
2Productivity
If continuous monitoring of physical activities is provided, then productivity of exercise tracking is improved, but use of energy and device complexity worsen
Solution Approach 1:
The patent implements periodic action by processing sensor data in fixed-time windows (e.g., 1-second intervals) rather than continuously, performing signal processing and classification only at discrete intervals to reduce computational energy consumption while maintaining effective monitoring
Solution Approach 2:
The patent segments the continuous monitoring task into distinct processing stages: raw signal acquisition, preprocessing and filtering, feature extraction, classification, and result output. This segmentation allows energy-intensive operations to be performed only when necessary, improving productivity while managing energy usage
3Loss of information
If automated exercise recognition is implemented, then loss of information regarding exercise performance is reduced, but device complexity increases
Solution Approach 1:
The patent implements multi-functionality by designing a single integrated controller that performs multiple functions: acquiring sensor data, preprocessing signals, extracting features, classifying exercise types, counting repetitions, and providing feedback. This universal approach reduces the need for separate specialized components, managing device complexity while comprehensively capturing exercise performance information
Data Source
AI summary
A physical activity monitoring device includes a sensor array with one or more sensors configured to measure physical activity attributes of a user. A controller automatically determines time intervals where the user is actively engaged in a physical activity based on the physical activity attributes. The controller also automatically determines a type of physical activity the user in actively engaged in during the determined time intervals based on the physical activity attributes. A reporter outputs information regarding the type of physical activity to the user.