MediaPipe Pose Estimation for Basic Human Movement Patterns Across Different Camera Views

Accurate analysis of human movement is essential for developing effective human-robot interaction (HRI) interfaces. Vision-based pose estimation tools like MediaPipe, which operate markerlessly with a single camera, offer a low-cost alternative but lack extensive validation across different capture conditions. This study evaluates the consistency of MediaPipe’s pose estimations during three basic human movements (squatting, pulling, pushing), captured from three camera angles ($0^{\circ}, 45^{\circ}$, and 90°). Joint angles were analyzed, and Pearson’s correlations were computed between time series across views. Results reveal how MediaPipe’s accuracy varies with movement and camera placement, offering insights into its applicability for HRI and human movement analysis.