Artificial Intelligence Tracks Arm Movements With A Single Camera For Clinical Assessments

Researchers at institutions such as Shriners Children’s Lexington, the University of Kentucky, and Georgia Institute of Technology have developed a new method to quantify upper extremity reachable workspace (UERW) using artificial intelligence and markerless motion capture technology.
The proposed single-camera approach uses AI and a monocular system, which is simpler and less expensive compared to traditional marker-based motion capture tools.
Validation of this markerless motion capture system, through a study involving nine participants, showed a strong agreement with established methods, particularly when using a frontal camera perspective.
For the frontal camera setup, the system exhibited minimal mean bias and a low standard deviation, indicating high fidelity in measuring UERW. In contrast, the offset camera view had a significant underestimation of the workspace.
The study demonstrates the feasibility of using a single, frontal-facing camera to assess the Upper Extremity Reachable Workspace, especially useful in clinical settings where traditional methods can be cost-prohibitive.
This innovation addresses longstanding barriers in clinical biomechanical analysis by making quantitative movement assessments more accessible and affordable.
Future work includes fine-tuning the system for different clinical environments and evaluating its practicality in treating patients with various movement disorders.