Interactive Training Motion Tracking for Adaptive Rehabilitation
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Solution Overview
Problem
Existing remote rehabilitation programs face challenges due to therapist involvement, monotonous exercises, and difficulties in accommodating heterogeneous impairment conditions and varying recovery paces, leading to limited adoption and patient engagement.
Innovation Solution
A computer-implemented method and device using AI machine learning algorithms for motion detection, enabling precise tracking of user movements, customizable exercises, and automatic progression recommendations, eliminating the need for additional sensors or supervision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If remote rehabilitation programs are implemented, then patient monitoring and therapy delivery are improved, but therapist involvement requirements increase system complexity
Solution Approach 1:
The system enables patients to independently perform rehabilitation exercises using the camera-based motion tracking technology. The AI automatically detects and evaluates patient movements without requiring therapist presence, allowing the system to serve itself by providing autonomous monitoring and feedback capabilities that were previously dependent on continuous professional supervision.
Solution Approach 2:
The patent replaces the mechanical system of direct therapist-patient interaction with an automated vision-based detection system. The camera and AI algorithms substitute for the therapist's physical presence and manual assessment, transforming the rehabilitation delivery mechanism from human-dependent to technology-driven while maintaining therapeutic effectiveness.
2Ease of operation
If rehabilitation exercises are made more engaging, then patient engagement is improved, but exercise customization requirements increase system complexity
Solution Approach 1:
The system dynamically adjusts exercise parameters and provides real-time feedback based on detected patient performance. The AI continuously analyzes motion data and automatically modifies exercise difficulty, repetition counts, and intensity levels, transforming static exercise protocols into adaptive, engaging experiences that respond to patient capabilities without requiring complex manual customization.
Solution Approach 2:
The patent utilizes parameter changes in exercise intensity, duration, and complexity based on AI-analyzed patient performance data. By automatically adjusting these parameters according to detected motion quality and patient progress, the system creates engaging variable exercises that adapt to individual patient needs without requiring complex pre-programming or therapist intervention for each adjustment.
3Measurement precision
If motion detection accuracy is improved, then exercise evaluation precision is improved, but processing requirements and system complexity increase
Solution Approach 1:
The system extracts only the essential motion parameters needed for rehabilitation evaluation from the video feed, focusing on key body landmarks and movement patterns relevant to therapeutic exercises. By selectively extracting only the necessary motion data rather than processing all video information, the system achieves high measurement precision while minimizing computational complexity and processing requirements.
Data Source
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AI summary
The invention relates to a computer-implemented method and device of interactive training. The method comprises the step of: receiving a video signal comprising a series of images displaying a subject; processing the series of images for detecting actions of the subject, comprising the steps of: extracting from a first image, a first information in relation to a body portion of the subject; locating, automatically, a set of first key points based on the extracted first information from the first image; extracting from the first image, a second information in relation to a hand portion and/or a foot portion of the subject; locating, automatically, a set of second key points based on the extracted second information from the first image; deriving an action characteristic of the subject based on the located sets of first key points and second key points; repeating the processing steps to acquire a series of action characteristics of the subject in respect to time; and determining a first level of action of the subject based on the series of action characteristics.