Kinematic synthesis for smart hand prosthetics

The dream of a bionic replacement appendage is becoming reality through the use of mechatronic prostheses that utilize the body’s myoelectric signals. This paper presents a process to accurately capture the motion of the human hand joints; the obtained information is to be used in conjunction with myoelectric signal identification for motion control. In this work, the human hand is modeled as a set of links connected by joints, which are approximated to standard revolute joints. Using the methods of robotics, the motion of each finger is described as a serial robot, and expressed as Clifford algebra exponentials. This representation allows us to use the model to perform kinematic synthesis, that is, to adapt the model to the dimensions of real hands and to obtain the angles at each joint, using visual data from real motion captured with several cameras. The goal is to obtain an adaptable motion tracking system that can follow as many different motions as possible with sufficient accuracy, in order to relate the individual motions to myoelectric signals in future work.Postprint (author’s final draft

Kinematic synthesis for smart hand prosthetics

The dream of a bionic replacement appendage is becoming reality through the use of mechatronic prostheses that utilize the body’s myoelectric signals. This paper presents a process to accurately capture the motion of the human hand joints; the obtained information is to be used in conjunction with myoelectric signal identification for motion control. In this work, the human hand is modeled as a set of links connected by joints, which are approximated to standard revolute joints. Using the methods of robotics, the motion of each finger is described as a serial robot, and expressed as Clifford algebra exponentials. This representation allows us to use the model to perform kinematic synthesis, that is, to adapt the model to the dimensions of real hands and to obtain the angles at each joint, using visual data from real motion captured with several cameras. The goal is to obtain an adaptable motion tracking system that can follow as many different motions as possible with sufficient accuracy, in order to relate the individual motions to myoelectric signals in future work