The purpose of this work is to develop a human hand model that will work in conjunction with the myoelectric signals from the arm muscles, for those people who have lost their upper extremity. Though there are many prostheses available on the market with variable cost and functional accuracy, it is hard to find a prosthesis that mimics the complete functionality of the human hand, due to the complex hand motion, the complex dynamics of the myoelectric signals, and the difficulty involved in the acquisition of these signals, which complicates the implementation. In order to overcome some of these problems, the proposed hand model mimics most of the hand movements and it is used together with a kinematic synthesis process to identify the motion of the hand, obtained from visual data. In this paper, the human hand is modeled as a collection of five serial chains. For each movement performed by the joints in the finger/wrist, revolute joints are considered in different configurations, which yield movements similar to those of the human hand. The forward kinematics in matrix form is formulated using Denavit-Hartenberg parameters and expressed using Clifford Algebra exponentials. Kinematic synthesis is used to adjust the dimensions of the proposed model to the hand of the subject, and to identify the angles at each joint for a given hand motion. In the kinematic synthesis process, the forward kinematics equations of the hand are solved for both the angles of the joints and the dimensions of the hand. The synthesis equations obtained from the kinematic synthesis process are solved using a Levenberg-Marquardt nonlinear least-squares algorithm. The experimental setup for the real-time motion capturing consists of three camerasand is to be used in future work to relate the joint motion to the myoelectric signals acquired from the subject's arm.

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