Hand tracking for clinical applications: Validation of the Google MediaPipe Hand (GMH) and the depth-enhanced GMH-D frameworks

Gianluca Amprimo, Giulia Masi, Giuseppe Pettiti, Gabriella Olmo, Lorenzo Priano, Claudia Ferraris

Abstract: Highlights•Hands are vital for carrying out daily-life tasks without external aid.•Measuring objectively hand dexterity can support new digital health treatments.•Deep Learning allows for video-based hand tracking of fine motor tasks.•We validate accuracy of Google Mediapipe Hand and GMH-D against motion capture.•Both frameworks achieve good-to-high spatial and temporal hand tracking performance.