Computer Vision Upper Extremity Assessment
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Solution Overview
Problem
Current clinical assessment methods for upper extremity function in patients with sensorimotor deficits are subjective and lack sensitivity to detect subtle changes, leading to inadequate rehabilitation protocols and limited financial support for continued therapy, especially for mildly impaired patients.
Innovation Solution
The integration of computer vision algorithms for objective tracking of human motion without wearable sensors or markers, using cameras to record and analyze movement data, providing quantitative metrics such as movement smoothness and temporal coupling of hand and arm motion, and comparing it to normative data for precise clinical decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If subjective evaluation methods are used for clinical assessment, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent replaces subjective human evaluation with automated computer vision algorithms that process video data to extract objective motion metrics. The system uses machine learning models to analyze movement patterns, replacing the mechanical process of human visual assessment with an automated computational system that provides precise, quantifiable measurements without requiring complex manual intervention.
2Ease of operation
If macroscopic metrics such as time-to-task completion are used, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent segments the overall task performance into multiple fine-grained motion components by tracking specific body landmarks and movement phases. Instead of measuring only the total time to complete a task, the system divides the movement into discrete segments (e.g., initiation phase, transition phase, completion phase) and analyzes each segment's characteristics separately, providing detailed precision while maintaining operational simplicity through automated analysis.
3Ease of operation
If discrete grading levels are used for movement quality assessment, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent transforms the discrete grading scale into continuous parameter measurements by extracting multiple kinematic variables (velocity, acceleration, trajectory smoothness, joint angles) from video data. This allows the system to capture subtle variations in movement quality that discrete levels cannot detect, while the automated computation maintains ease of operation by processing these continuous parameters without manual intervention.
4Measurement precision
If computer vision algorithms are integrated for objective tracking, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent uses standard off-the-shelf cameras to capture video data, creating an optical copy of the patient's movement without requiring specialized or complex tracking equipment. The computer vision system processes these standard video copies to extract precise motion metrics, avoiding the need for expensive wearable sensors, markers, or specialized hardware, thereby maintaining simplicity while achieving high measurement precision.
5Measurement precision
If detailed motion analysis is performed, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs motion analysis continuously during the task performance by processing video frames in real-time or near-real-time. Rather than requiring separate analysis sessions or interrupting the clinical workflow, the system continuously extracts motion parameters as the patient performs the task, enabling detailed precision measurement without adding significant time loss to the assessment process.
Data Source
AI summary
Methods and systems are presented for kinematic tracking and assessment of upper extremity function of a patient. A sequence of 2D images is captured by one or more cameras of a patient performing an upper extremity function assessment tasks. The captured images are processed to separately track body movements in 3D space, hand movements, and object movements. The hand movements are tracked by adjusting a position, orientation, and finger positions of a three-dimensional virtual model of a hand to match the hand in each 2D image. Based on the tracked movement data, the system is able to identify specific aspects of upper extremity function that exhibit impairment instead of providing only a generalized indication of upper extremity impairment.