Smart Wear Motion Control via Real-Time Error Estimation
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Solution Overview
Problem
Conventional motion capturing systems struggle to adapt standard rehabilitation training and posture correction methods to individual users, particularly due to differences in gender, age, and body conditions, leading to inefficiencies in training time and accuracy.
Innovation Solution
A smart wear apparatus and method that utilizes sensors and actuators to capture user motion, generate error information, estimate subsequent motion, and provide real-time feedback for optimized motion control, incorporating user-specific physical and competence information to adjust standard motion information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If standard motion information is used for rehabilitation training, then the training method is simple and easy to implement, but it cannot adapt to individual differences in gender, age, and body conditions
Solution Approach 1:
The system dynamically adjusts standard motion information based on real-time sensor data and user characteristics. The motion information is converted and optimized according to individual user parameters such as gender, age, and body conditions, allowing the system to transition from static standard information to dynamic personalized information while maintaining ease of implementation through automated processing.
Solution Approach 2:
The system changes key parameters of motion information based on user-specific parameters. By inputting user characteristics (gender, age, body conditions) and competence level information, the system modifies standard motion information parameters to generate optimized motion information tailored to each individual user, resolving the contradiction between standardization and personalization.
2Measurement precision
If a therapist or coach provides manual correction for motion errors, then the feedback is accurate and personalized, but the training time increases significantly
Solution Approach 1:
The system implements automated feedback by capturing user motion through sensors, comparing it with optimized motion information, generating error information, and providing real-time correction guidance. This automated feedback loop replaces manual therapist intervention, maintaining measurement precision through objective sensor data while eliminating the time loss associated with continuous human supervision.
Solution Approach 2:
The system enables users to receive automated motion correction without continuous therapist intervention. By providing real-time error information and correction guidance through the smart wear device, the system allows users to self-correct their motions, significantly reducing the time therapists need to spend on individual correction while maintaining accurate feedback.
3Device complexity
If conventional motion capturing systems are used, then the system structure is simple, but it cannot provide real-time motion correction and prediction
Solution Approach 1:
The system performs preliminary action by estimating subsequent motion errors before they occur. By analyzing current motion data and comparing it with optimized motion information, the system predicts future errors and provides proactive correction guidance, enabling users to prevent errors rather than just react to them, thereby improving training efficiency without excessive complexity.
Solution Approach 2:
The system implements real-time feedback by continuously capturing motion data, generating error information, and providing correction guidance during training. This real-time feedback mechanism enables the system to improve productivity by allowing immediate correction of motion errors, eliminating the need for post-training analysis and significantly enhancing training efficiency while maintaining manageable system complexity.
Data Source
AI summary
Disclosed herein are an apparatus and method for controlling smart wear. The apparatus for controlling smart wear includes a motion capture unit, an error information unit, a motion estimation unit, and an actuation unit. The motion capture unit captures a motion of a user using sensors included in the smart wear. Then error information unit generates user error information using reference motion information and the results of the motion capture. The motion estimation unit estimates a subsequent motion of the user using the user error information. The actuation unit controls the smart wear of the user in real time using the estimated subsequent motion and the user error information.