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Accurate 3D tracking of hand and fingers movements poses significantchallenges in computer vision. The potential applications span across multipledomains, including human-computer interaction, virtual reality, industry, andmedicine. While gesture recognition has achieved remarkable accuracy,quantifying fine movements remains a hurdle, particularly in clinicalapplications where the assessment of hand dysfunctions and rehabilitationtraining outcomes necessitate precise measurements. Several novel andlightweight frameworks based on Deep Learning have emerged to address thisissue; however, their performance in accurately and reliably measuring fingersmovements requires validation against well-established gold standard systems.In this paper, the aim is to validate the handtracking framework implemented byGoogle MediaPipe Hand (GMH) and an innovative enhanced version, GMH-D, thatexploits the depth estimation of an RGB-Depth camera to achieve more accuratetracking of 3D movements. Three dynamic exercises commonly administered byclinicians to assess hand dysfunctions, namely Hand Opening-Closing, SingleFinger Tapping and Multiple Finger Tapping are considered. Results demonstratehigh temporal and spectral consistency of both frameworks with the goldstandard. However, the enhanced GMH-D framework exhibits superior accuracy inspatial measurements compared to the baseline GMH, for both slow and fastmovements. Overall, our study contributes to the advancement of hand trackingtechnology, the establishment of a validation procedure as a good-practice toprove efficacy of deep-learning-based hand-tracking, and proves theeffectiveness of GMH-D as a reliable framework for assessing 3D hand movementsin clinical applications.

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