AI Home Exercise Monitoring System for Injury Risk Prediction
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Solution Overview
Problem
Physical therapy patients face challenges in consistently adhering to home exercise plans due to variability in diagnosis, education, and progression, which can lead to inefficiencies and increased risk of injury, as current methods rely heavily on visual monitoring by physical therapists and lack standardized digital solutions.
Innovation Solution
A digital system that creates and modifies home exercise plans using machine learning models trained on patient data, allowing for continuous virtual monitoring, predicting risks, and adapting exercises based on patient input, thereby ensuring compliance and reducing injury likelihood.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physical therapists use visual monitoring and in-person visits to evaluate patient progress, then treatment accuracy and patient safety are improved, but treatment time and healthcare costs increase
Solution Approach 1:
The patent replaces the mechanical system of visual monitoring and in-person evaluation with an automated image processing system using cameras and computer algorithms. The system captures images of patient movements, processes them through algorithms to evaluate range of motion and exercise performance, and provides automated feedback, eliminating the need for continuous physical therapist observation while maintaining evaluation accuracy.
Solution Approach 2:
The system enables patients to perform self-monitoring of their own exercise performance through captured images and automated analysis. Patients receive immediate feedback on their form and range of motion without requiring physical therapist intervention, allowing them to self-correct and self-evaluate between professional visits.
2Reliability
If physical therapists provide personalized exercise prescriptions and monitoring, then patient outcomes are improved, but device complexity and operational requirements increase
Solution Approach 1:
The system uses a single multi-functional platform that can evaluate multiple different exercises, measure various range of motion parameters, and provide comprehensive feedback through one integrated system. The image processing algorithms are designed to analyze different types of movements (flexion, extension, abduction, etc.) using the same hardware and software infrastructure, eliminating the need for separate specialized equipment for each exercise type.
Solution Approach 2:
The system measures and monitors multiple parameters including range of motion angles, exercise repetition counts, movement speed, and form quality metrics. By tracking changes in these parameters over time, the system objectively quantifies patient progress and automatically adjusts exercise difficulty levels, providing personalized treatment without manual assessment.
3Stability of the object's composition
If standardized digital monitoring systems are implemented, then treatment consistency is improved, but initial implementation costs and system complexity increase
Solution Approach 1:
The system creates digital copies of physical assessments through captured images and processed data. Instead of relying on varying human judgment, the system generates standardized digital records of patient performance that can be consistently analyzed and compared across different patients and time points, ensuring uniform evaluation criteria are applied universally.
Data Source
AI summary
Systems, methods and computer readable media are provided for determining patient risk of participating in a physical therapy digital home exercise program. The patient risk is generated by one or more artificial intelligence/machine learning (AI/ML) models. Based on the patient risks compared to the benefits, one or more actions may be initiated to create or modify a digital home exercise program for a patient.