Soft hand exoskeleton actuated with SMA fibres

The current project is based on developing a wearable and comfortable soft hand exoskeleton actuated with Shape Memory Alloy (SMA) fibres. The main purpose of this device is both, to be involved in rehabilitation exercises and assistive therapies for patients suffering from hands’ damage. This innovative idea presents an affordable and convenient alternative in the exoskeletons’ field, combining a light and non-expensive actuation along with biocompatible materials specifically tailored to patient’s hand anatomy. To generate the perfectly fitting glove, plastic moulds were 3D-printed after sketching them with Creo Parametric software. Then, silicone was poured into the casts and it cured maintaining the desired shape. Taking advantage of Joule’s effect, the current which flows though the SMA wires is capable of increasing temperature, causing a microstructure change and thus inducing contraction. This motion can be accurately controlled by a MATLAB-Simulink interface, achieving both flexion and extension so as to perform pincer grip. Furthermore, a force sensor embedded on silicone finger’s tip is used as a force feedback to evaluate the pressure applied by the subject when holding distinct objects.Ingeniería Biomédica (Plan 2010

Real-time EMG Control for Hand Exoskeletons

The goal of this project is to develop a system for hand exoskeletons control in real time. Robotic systems are useful for rehabilitation therapies due to their ability to help patients perform repetitive movements. Hand recovery is especially critical because hands are necessary to perform many daily life activities. Exoskeletons developed for hand rehabilitation can benefit from a real-time control system that activates the robotic devices at the same time as the patient is performing a movement. Real-time control of these systems can be achieved using different methods. In this project, electromyographic (EMG) signals from the patient’s forearm are used. The controlled robotic systems are soft hand exoskeletons actuated with Shape-Memory Alloys (SMA) wires. The SMA wires are controlled with a microcontroller. The main objective of these exoskeletons is to help the patient with the movement of grasping an object and releasing it afterwards. Machine learning is used to detect the intention of the patient to grasp or release an object based on the patient’s EMG signals. Once one of these movements is detected, real-time communication with the microcontroller is achieved and the necessary SMA wires are activated. The system is developed in Matlab, and it involves signal acquisition, signal rectification, signal segmentation, feature extraction, dimensionality reduction, signal classification, and communication with the microcontroller. To differentiate between the movements of grasping and releasing an object, three different classifiers will be tested: Artificial Neural Networks (ANN), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). Their performance will be compared using a confusion matrix and the best one will be selected for the system’s algorithm. Control is achieved with a time delay of less than 1 second for the action of grasping and of less than 2 seconds for the action of releasing is achieved, almost accomplishing the objective of developing a real-time control system. Several improvements are proposed to decrease this time delay.Ingeniería Biomédic

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research

Reduction of dimensionality of a cellular actuator array for driving a robotic hand

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2007.Includes bibliographical references (p. 89-93).In an attempt to explore an alternative to today's robot actuators, a new approach to artificial muscle actuator design and control is presented. The objective of this research is to coordinate the multitude of artificial muscle actuator axes for a large DOF (degree of freedom) robotic system based on dimensionality reduction. An array of SMA actuators is segmented into many independently controlled, spatially discrete volumes, each contributing a small displacement to create a large motion. Segmented Binary Control is proposed where each segment is controlled in an on-off manner, creating a stepper-motor like actuator. This overcomes hysteresis and other nonlinearities of the actuator material. The segmented cellular architecture of SMA wires is extended to a multi-axis actuator array by arranging the segments in a two-dimensional array. The multi-axis control is streamlined and coordinated using a grouping of segments called C-segments in order to activate multiple links of a robot mechanism in a coordinated manner. This allows control of large DOF with a small number of controls. The proposed approach is inspired by the segmented architecture of biological muscles and synergies, a strategy of grouping output variables to simplify the control of large number of muscles. Data from various hand postures are collected using data glove and used in creating the C-segment design that is capable of performing the given postures. A lightweight Robotic Hand with 16 DOF is built using shape memory alloy actuators. This hand weighs less than 1kg including 32 SMA actuators and control circuitry. Eight C-segments that are ON-off controlled are used to create sixteen given postures. In the future, this approach can be applied to applications where the control signal is inherently limited due to limited amount of information that can be extracted or transferred to the robot, such as brain machine interface and tele-operation.by Kyu-Jin Cho.Ph.D

Sensors for Robotic Hands: A Survey of State of the Art

Recent decades have seen significant progress in the field of artificial hands. Most of the surveys, which try to capture the latest developments in this field, focused on actuation and control systems of these devices. In this paper, our goal is to provide a comprehensive survey of the sensors for artificial hands. In order to present the evolution of the field, we cover five year periods starting at the turn of the millennium. At each period, we present the robot hands with a focus on their sensor systems dividing them into categories, such as prosthetics, research devices, and industrial end-effectors.We also cover the sensors developed for robot hand usage in each era. Finally, the period between 2010 and 2015 introduces the reader to the state of the art and also hints to the future directions in the sensor development for artificial hands