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Human level of dexterity has not been duplicated in a robotic form to date. Dexterity is achieved in part due to the biomechanical structure of the human body and in part due to the neural control of movement. We have developed an anatomically correct testbed (ACT) hand to investigate the importance and behavioral consequences of anatomical features and neural control strategies of the human hand. One of the critical aspects of understanding dexterity is the analysis of the relationships between the hand muscle movements and joint movements, defined by the moment arms of the muscles. It is known that the moment arms for the hand muscles are configuration-dependent and vary substantially with change in posture. This paper presents a methodology for determining continuous variations in the moment arms with respect to multiple joints moving simultaneously. To determine variations in the moment arms of the ACT hand index finger muscles, we employed a nonparametric regression method called Gaussian processes (GPs). GPs give a functional mapping between the joint angles and muscle excursions, and the gradients of these mappings are the muscle moment arms. We compared the moment arm relationships of the ACT hand with those determined from the available cadaver data. We present the implications of the determination of variable moment arms toward understanding of the biomechanical properties of the human hand and for the neuromuscular control for the ACT hand index finger movements.