Real Time Elbow Angle Estimation Using Single RGB Camera
This provides a more accessible solution for applications like rehabilitation and gaming by reducing reliance on expensive or marker-based systems, though it is incremental in improving accuracy with a simpler setup.
The paper tackled real-time elbow angle estimation in human motion capture by proposing a markerless, cost-effective method using a single RGB camera and part affinity fields, achieving median RMS errors of 3.06° and 0.95° in sagittal and coronal planes compared to Microsoft Kinect.
The use of motion capture has increased from last decade in a varied spectrum of applications like film special effects, controlling games and robots, rehabilitation system, animations etc. The current human motion capture techniques use markers, structured environment, and high resolution cameras in a dedicated environment. Because of rapid movement, elbow angle estimation is observed as the most difficult problem in human motion capture system. In this paper, we take elbow angle estimation as our research subject and propose a novel, markerless and cost-effective solution that uses RGB camera for estimating elbow angle in real time using part affinity field. We have recruited five (5) participants to perform cup to mouth movement and at the same time measured the angle by both RGB camera and Microsoft Kinect. The experimental results illustrate that markerless and cost-effective RGB camera has a median RMS errors of 3.06° and 0.95° in sagittal and coronal plane respectively as compared to Microsoft Kinect.