A Biomechanical Hand Model to Quantify Finger Joint Kinematics Using a 3D Motion Capture System

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Abstract

The kinematics of rhythmic, speed-modulated finger and grasp-like movements were analyzed using a reduced biomechanical model of the hand and a marker-based optical motion-capture system. Twenty-one healthy participants performed eight hand motor tasks involving metacarpophalangeal (MCP) joint flexion–extension (F–E) and carpometacarpal (CMC) thumb opposition–reposition (O–R) at two movement frequencies (0.50 and 0.75 Hz). Kinematic analysis quantified the range of movement (RoM), mean speed, and normalized total harmonic distortion (TDH N ). Statistical analysis identified task type as the primary factor modulating all three metrics across digits, with large effect sizes . Movement frequency significantly influenced mean speed and moderately affected TDH N , while thumb RoM remained statistically unchanged across frequencies ( p = 0.063). Participants consistently reproduced the intended sinusoidal trajectories, as indicated by low TDH N values (below 19%). The findings support the analysis of coordinated hand movements across various tasks under controlled time conditions. They also demonstrate that the simplified biomechanical model accurately captured both individual and co-ordinated finger movements. This provides a valuable reference for studies on motor control and for applications in rehabilitation and assistive technology.
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Abstract The kinematics of rhythmic, speed-modulated finger and grasp-like movements were analyzed using a reduced biomechanical model of the hand and a marker-based optical motion-capture system. Twenty-one healthy participants performed eight hand motor tasks involving metacarpophalangeal (MCP) joint flexion–extension (F–E) and carpometacarpal (CMC) thumb opposition–reposition (O–R) at two movement frequencies (0.50 and 0.75 Hz). Kinematic analysis quantified the range of movement (RoM), mean speed, and normalized total harmonic distortion (TDHN). Statistical analysis identified task type as the primary factor modulating all three metrics across digits, with large effect sizes . Movement frequency significantly influenced mean speed and moderately affected TDHN , while thumb RoM remained statistically unchanged across frequencies (p = 0.063). Participants consistently reproduced the intended sinusoidal trajectories, as indicated by low TDHN values (below 19%). The findings support the analysis of coordinated hand movements across various tasks under controlled time conditions. They also demonstrate that the simplified biomechanical model accurately captured both individual and co-ordinated finger movements. This provides a valuable reference for studies on motor control and for applications in rehabilitation and assistive technology. Competing Interest Statement The authors have declared no competing interest.

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[{'doi': None, 'name': 'Brazilian Public Ministry of Labor', 'awards': ['002118.2019']}, {'doi': '10.13039/501100002322', 'name': 'CAPES', 'awards': ['88887.842775/2023-00']}, {'doi': '10.13039/501100001807', 'name': 'FAPESP', 'awards': ['2020/13293-0']}]

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