IMU Motion Feedback for Remote Rehabilitation Form Correction
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Solution Overview
Problem
Current sensor-based monitoring devices are inadequate for accurately analyzing and predicting physical movements in real-time, failing to provide precise feedback on exercise form and injury prevention, especially in physical rehabilitation and athletic performance contexts.
Innovation Solution
A system comprising a suite of sensors, including inertia measurement units (IMUs) and additional sensors, connected to various body parts and equipment, which generate real-time feedback using machine learning to correct deviations in movement and prevent injuries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional in-person rehabilitation sessions are used, then therapists can provide direct physical cues and assessment, but patient convenience decreases and therapists cannot accurately assess repetitive exercises
Solution Approach 1:
The patent replaces the mechanical/physical assessment system (therapist physically touching and observing patient) with an electronic sensor-based system. Sensors attached to the patient's body capture movement data, which is then analyzed by a computing device to provide objective feedback on exercise performance, eliminating the need for physical therapist contact while maintaining assessment accuracy.
Solution Approach 2:
The patent introduces sensors and a computing device as intermediaries between the patient and therapist. These intermediaries capture and transmit movement data, allowing the therapist to remotely assess patient performance with high precision without requiring in-person physical evaluation.
2Measurement precision
If therapists physically touch patients to assess muscle contraction, then assessment accuracy improves, but patient comfort and appropriateness deteriorate
Solution Approach 1:
The patent substitutes physical therapist contact with electronic sensors that attach to the patient's body. These sensors detect muscle contraction and movement patterns through non-invasive means, providing accurate physiological data without the discomfort or impropriety of physical therapist touch.
3Measurement precision
If multiple sensors are used to track complex movements, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent combines multiple sensors (accelerometers, gyroscopes, magnetometers, barometers) into a single integrated sensor package. This merging approach maintains high measurement precision for complex three-dimensional motion tracking while reducing the number of separate components the patient must manage and simplifying the overall system architecture.
Solution Approach 2:
The sensor package is designed to perform multiple functions simultaneously - tracking position, orientation, acceleration, and environmental pressure. This multi-functionality allows comprehensive motion analysis with a single device rather than requiring multiple specialized sensors, thereby reducing system complexity while maintaining measurement precision.
4Reliability
If real-time feedback is provided to correct movement deviations, then injury prevention improves, but system complexity increases
Solution Approach 1:
The patent implements a real-time feedback loop where sensors continuously monitor patient movement, the computing device analyzes the data against proper form criteria, and immediate feedback is provided to correct deviations. This automated feedback system enhances injury prevention by detecting and alerting patients to improper form in real-time without requiring complex manual monitoring systems.
Solution Approach 2:
The system enables patients to self-monitor and self-correct their movements through automated feedback. The computing device compares sensor data with stored proper form patterns and provides guidance directly to the patient, reducing the need for constant therapist intervention and simplifying the overall system operation.
Data Source
AI summary
A system for optimizing mechanics and movements in remote or outpatient physical therapy, rehabilitation, sports training, and injury prevention that uses several inertia measurement units (IMUs) to measure a user's motion while performing an action. The IMUs can have additional sensors connected to improve the system's ability to detect flaws in the user's motion. Furthermore, the system uses machine learning to detect and determine flaw in a user's motion from the IMU data. The system may generate feedback to improve the user's motion based on the detected flaws. Different feedback communication may be provided based on the performance of the user after the feedback is provided.