Interactive Training Motion Analysis for Independent Rehabilitation
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Solution Overview
Problem
Existing remote rehabilitation programs face challenges due to the substantial involvement required from therapists, monotonous exercises, and difficulties faced by patients with cognitive or physical impairments, leading to limited adoption and ineffective execution of therapeutic movements.
Innovation Solution
A computer-implemented method and device for interactive training that uses AI and motion detection technology to automatically detect user movements, customize exercises, and provide personalized feedback without the need for additional sensors or supervision, enabling independent rehabilitation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If remote rehabilitation programs are implemented to reduce therapist involvement, then accessibility and scalability improve, but therapy accuracy and patient compliance deteriorate
Solution Approach 1:
The system enables patients to perform rehabilitation exercises independently at home using a camera and processing module that automatically detect and analyze movements. The computer vision system processes video feeds to identify key points on the patient's body, track movements, and provide real-time feedback without requiring therapist presence, thus achieving self-service rehabilitation while maintaining therapy accuracy through automated detection algorithms.
Solution Approach 2:
The patent replaces the mechanical system of direct therapist-patient interaction with an automated computer vision system. The processing module uses image processing algorithms to substitute for the therapist's manual observation and correction, transforming physical therapy monitoring into an optical-digital system that captures video signals, extracts movement data, and provides feedback automatically.
2Reliability
If traditional rehabilitation exercises are used to ensure therapeutic effectiveness, then treatment quality improves, but patient engagement and completion rates deteriorate due to monotony
Solution Approach 1:
The system dynamically adjusts the rehabilitation program based on real-time movement analysis. The processing module continuously monitors patient performance, detects deviations from correct form, and provides adaptive feedback. The system can modify exercise parameters, difficulty levels, and provide varying feedback types to maintain patient engagement while ensuring therapeutic effectiveness through continuous adaptation.
Solution Approach 2:
The patent implements a comprehensive feedback mechanism where the processing module analyzes patient movements and provides immediate feedback through the display device. The system compares detected movements against correct exercise patterns, provides visual and auditory feedback on performance quality, and adjusts the program based on progress, creating an interactive loop that maintains both treatment quality and patient engagement.
3Measurement precision
If complex monitoring systems are deployed to ensure accurate movement detection, then measurement precision improves, but device complexity and cost increase
Solution Approach 1:
The patent introduces a camera as an intermediary device between the patient and the analysis system. Instead of requiring complex sensors attached to the patient's body, the system uses a standard camera to capture video feeds, which are then processed by algorithms that identify key points and track movements. This intermediary approach simplifies the physical setup while maintaining measurement precision through sophisticated image processing.
Solution Approach 2:
The system creates a digital copy of the patient's movements through video capture and key point extraction. Instead of directly measuring physical movements with complex sensors, the patent captures visual information, extracts relevant movement data through image processing, and analyzes this digital representation. This copying approach reduces hardware complexity while preserving measurement accuracy through algorithmic analysis.
Data Source
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.