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

Fusion of hard and soft control strategies for the robotic hand

Long considered the stuff of science fiction, a prosthetic hand capable of fully replicating all of that appendage's various functions is closer to becoming reality than ever before. This book provides a comprehensive report on exciting recent developments in hybrid control techniques—one of the most crucial hurdles to be overcome in creating smart prosthetic hands. Coauthored by two of the world's foremost pioneering experts in the field, Fusion of Hard and Soft Control Strategies for the Robotic Hand treats robotic hands for multiple applications. It begins with an overview of advances in main control techniques that have been made over the past decade before addressing the military context for affordable robotic hand technology with tactile and/or proprioceptive feedback for hand amputees. Kinematics, homogene us transformations, inverse and differential kinematics, trajectory planning, and dynamic models of two-link thumb and three-link index finger are discussed in detail. The remainder of the book is devoted to the most promising soft computing techniques, particle swarm optimization techniques, and strategies combining hard and soft controls. In addition, the book: Includes a report on exciting new developments in prosthetic/robotic hand technology, with an emphasis on the fusion of hard and soft control strategies Covers both prosthetic and nonprosthetic hand designs for everything from routine human operations, robotic surgery, and repair and maintenance, to hazardous materials handling, space applications, explosives disposal, and more Provides a comprehensive overview of five-fingered robotic hand technology kinematics, dynamics, and control Features detailed coverage of important recent developments in neuroprosthetics F sion of Hard and Soft Control Strategies for the Robotic Hand is a must-read for researchers in control engineering, robotic engineering, biomedical sciences and engineering, and rehabilitation engineering

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