Real Time Elbow Angle Estimation Using Single RGB Camera

TL;DR

This paper presents a real-time, markerless method for estimating elbow angles using a single RGB camera, achieving accuracy comparable to Kinect in dynamic movements.

Contribution

The study introduces a novel, cost-effective approach for elbow angle estimation using RGB cameras and part affinity fields, eliminating the need for markers or specialized environments.

Findings

Median RMS error of 3.06° in sagittal plane

Median RMS error of 0.95° in coronal plane

Comparable accuracy to Microsoft Kinect

Abstract

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{\deg} and 0.95{\deg} in sagittal and coronal plane respectively as compared to Microsoft Kinect.