Camera-Based Exercise Form Analysis Using Deep Learning
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Solution Overview
Problem
Current home-based solutions for remote monitoring of patients in physical therapy and rehabilitation are expensive, complex, and lack user-friendliness, often failing to provide accurate results due to their reliance on multiple hardware components and sensors, making them impractical for widespread use.
Innovation Solution
A sensor-less system utilizing a computing device with a camera that employs a deep learning algorithm to analyze video exercise data, comparing user movements to a predictive model and providing real-time feedback on how to improve exercise performance, eliminating the need for face-to-face supervision and dedicated sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dedicated cameras and sensors are used for remote patient monitoring, then measurement precision is improved, but device complexity increases and ease of operation deteriorates
Solution Approach 1:
The patent extracts the motion tracking function from complex dedicated sensor systems and implements it using only a standard camera device. The camera captures video data that is then processed to extract skeletal movement information, eliminating the need for specialized sensors while maintaining measurement precision.
Solution Approach 2:
The patent makes the camera device universal by using a common consumer device (smartphone, tablet, or computer camera) for multiple purposes: capturing video, tracking movement, and providing feedback. This replaces the need for dedicated single-function medical equipment, reducing device complexity while maintaining functionality.
2Measurement precision
If multiple hardware components are deployed for home monitoring, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system enables self-service by allowing patients to perform exercises and receive automated feedback without requiring setup or calibration by professionals. The camera automatically captures movement data, the processing system analyzes exercise form, and feedback is provided in real-time, making the system easy to operate for patients at home.
Solution Approach 2:
The patent implements continuous feedback loops where the system monitors exercise performance in real-time and provides immediate corrective guidance. This automated feedback mechanism replaces the need for face-to-face supervision while maintaining measurement precision and improving ease of operation through instant, actionable insights.
3Reliability
If dedicated monitoring systems are implemented, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent replaces mechanical sensor-based systems with an optical system using a camera. Instead of using accelerometers, gyroscopes, or other motion sensors, the system uses video capture and image processing to track movement, thereby reducing hardware complexity while maintaining or improving reliability through software-based analysis.
Solution Approach 2:
The patent creates a digital copy of the patient's movement by capturing video data and generating skeletal models from the visual information. This optical copy replaces the need for physical sensors attached to the body, reducing hardware complexity while maintaining reliable movement tracking through sophisticated image processing algorithms.
Data Source
AI summary
A computing device for implementing a sensor-less method to improve exercise performance is described. An engine of the computing device receives, from a camera of the computing device, video exercise data associated with a user performing an active exercise and then converts the video via a deep learning algorithm to one or more movements of a user's skeleton in real-time performing the active exercise. A first movement of the one or more movements is compared to a predictive model associated with an ideal performance of the first movement. A confidence score is assigned to the comparison. Feedback data is transmitted to a graphical user interface of the computing device for display to the user, where the feedback data includes the confidence score and instructions on how the user can improve the first movement to raise the confidence score.