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Preliminary findings support the feasibility of mobile coaching-based exergames for combined motor and cognitive training and motivate further exploring of presence-enhancing features.
This work introduces TransHands, a backbone-agnostic transfer learning framework that enables pre-trained human motion encoders to be effectively adapted for 3D hand pose estimation from 2D pose inputs, and evaluates TransHands across four state-of-the-art motion modeling architectures.
This study proves that GMH-D is a reliable framework for assessing 3D hand movements in clinical applications and contributes to the advancement of hand tracking technology, and the establishment of a validation procedure as a good-practice to prove efficacy of deep-learning-based hand-tracking.
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