Vision-Based Motion Capture for Independent Rehabilitation Scoring
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Solution Overview
Problem
Existing rehabilitation training systems require direct professional supervision and specialized equipment, are costly, and lack scalability, leading to inefficiencies and potential injuries when patients train independently.
Innovation Solution
A vision-based motion capture system using a single camera on a mobile device for rehabilitation training, which estimates 3D human body keypoints and generates motion curves to provide performance scores and guidance, reducing the need for direct supervision and specialized equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional motion capture systems use reflective markers and specialized cameras, then measurement precision can be achieved, but device complexity and cost increase significantly
Solution Approach 1:
The patent replaces the mechanical/optical marker-based motion capture system with a vision-based system using standard cameras and computer vision algorithms. Instead of relying on reflective markers and specialized infrastructure cameras, the system uses off-the-shelf webcams and smartphones to capture motion data through image processing and machine learning techniques, thereby reducing device complexity while maintaining measurement precision
Solution Approach 2:
The patent creates a virtual copy of the patient's anatomy through 3D modeling based on 2D images captured by standard cameras. This digital twin approach allows the system to reconstruct motion patterns without requiring physical markers or specialized hardware, simplifying the overall system while preserving measurement accuracy through computational methods
2Productivity
If rehabilitation training monitors detailed motion patterns, then training effectiveness improves, but device complexity and cost increase
Solution Approach 1:
The patent replaces complex mechanical motion capture infrastructure with vision-based monitoring using standard cameras. The system captures detailed motion patterns through image processing and machine learning algorithms that analyze pixel data to extract kinematic information, enabling comprehensive rehabilitation monitoring without specialized hardware
Solution Approach 2:
The patent makes standard cameras and smartphones multi-functional by enabling them to perform motion capture, 3D modeling, and rehabilitation monitoring tasks. These everyday devices are equipped with software algorithms that allow them to function as sophisticated motion analysis tools, reducing the need for dedicated expensive equipment while maintaining monitoring capabilities
3Measurement precision
If specialized motion capture infrastructure is used, then measurement accuracy improves, but ease of operation decreases
Solution Approach 1:
The patent uses 2D image copies captured by standard cameras to create 3D anatomical models and motion representations. This approach eliminates the need for complex marker placement and specialized camera positioning, making the system easier to operate while maintaining measurement accuracy through computational 3D reconstruction from simple 2D images
Solution Approach 2:
The system automatically performs 3D modeling, motion analysis, and rehabilitation assessment without requiring manual configuration or specialized operator intervention. The machine learning algorithms automatically track motion patterns and generate reports, making the system user-friendly and accessible to clinicians without extensive training in motion capture techniques
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the usability and practicality of rehabilitation training by allowing patients to perform exercises independently with accurate feedback, improving recovery monitoring and reducing the risk of injuries.
Implementation Method 1
a camera is used to capture images of a patient's body parts
Data Source
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AI summary
There is included a method and apparatus comprising computer code configured to cause a processor or processors to perform obtaining video data including at least one body part of a person, selecting keypoints of the at least one body part based on a predetermined rehabilitation category, extracting a motion feature of the at least one body part from the video data, scoring the motion feature based on the predetermined rehabilitation category, and generating a display illustrating the motion feature and said scoring of the motion feature.