Rehabilitation Motion Signal Alignment Using Gradient Segmentation
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Solution Overview
Problem
Existing methods for processing rehabilitation exercise signals, such as electromyography (EMG) and motion sensor data, are manual, time-consuming, and prone to inaccuracies due to noise, lacking automated noise removal and efficient comparison techniques.
Innovation Solution
A method and system that automatically segments motion signals based on gradients, uses dynamic time warping (DTW) to align and rectify noise, and extracts corresponding time intervals for accurate muscle recovery assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processing of rehabilitation signals is performed through visual inspection, then flexibility in analysis is maintained, but processing time increases significantly and accuracy decreases due to noise signals
Solution Approach 1:
The patent replaces manual visual inspection (mechanical human operation) with an automated computer-based signal processing system. The system automatically segments EMG and motion signals, aligns them using dynamic time warping, and extracts features without human intervention, thereby eliminating processing time delays while maintaining or improving accuracy through consistent algorithmic application.
Solution Approach 2:
The signal processing system performs self-service by automatically segmenting signals based on gradient analysis, aligning multiple signals through dynamic time warping, and extracting features without requiring manual intervention. The system serves itself by implementing the entire processing pipeline autonomously, from raw signal input to processed output ready for muscle recovery assessment.
2Productivity
If automated signal processing is implemented, then processing speed and consistency are improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex signal processing task into distinct modules: gradient-based signal segmentation, dynamic time warping alignment, and feature extraction. Each module handles a specific aspect of processing, making the overall system more manageable and easier to implement despite the automated nature of the complete pipeline.
3Measurement precision
If gradient-based segmentation is applied to motion signals, then signal alignment accuracy is improved, but computational requirements increase
Solution Approach 1:
The patent applies local quality by focusing computational resources on critical segments of the signals. Gradient-based segmentation identifies and processes only the relevant portions of EMG and motion signals where actual muscle activity occurs, rather than uniformly processing the entire signal duration. This localized approach improves alignment accuracy while reducing overall computational energy requirements.
Data Source
AI summary
The present disclosure relates to method and system for signal processing rehabilitation exercise signals. The method comprises the step of receiving a first and a second motion signals associated with movements of a body part, wherein the motion signals comprise temporal data of the movements. The method further comprises the step of segmenting each of the first and second motion signals into a plurality of segmented signals based on gradients of the motion signals, wherein each segmented signal has consistent gradient. The method further comprises the step of automatically modifying the segmented signals to form multiple combinations of matching signals with similar gradients between the first and second motion signals, such that the first and second motion signals are in one-to-one correspondence. The method further comprises the step of extracting corresponding time intervals of the matching signals in the correspondences.