Development of a Novel EMG-Based Neural Interface for Control of Transradial Prostheses with Gripping Assistance

North Carolina State University Raleigh, Raleigh NC

Investigators

Abstract

PROJECT SUMMARY An upper limb amputation can make many basic tasks difficult or nearly impossible. In recent years, research in algorithms that can predict motion intentions from electromyographic (EMG) signals of a residual limb has led to the development of prosthetic hands that allow control of multiple degrees of freedom (DOF) and has restored the basic functionality of an upper-limb. Some of the most advanced commercially available prosthetic hands use a machine learning-based control scheme known as EMG pattern recognition (PR). Many EMG PR approaches predict a motion class (e.g. hand open/close) and set the velocity of the motors proportional to the magnitude of EMG signals. Some new proposed approaches involve simultaneously controlling the position of multiple DOF. However, all of these control schemes allow users minimal control of the force applied to objects grasped by the prosthetic hand which makes holding and transporting fragile objects difficult. The overall objective of this project is to develop a novel control scheme that allows simultaneous control of the positions of multiple DOF of a transradial prosthesis as well as control of grip force when an object has made contact with the fingertips of the prosthesis. To achieve this objective, this proposal consists of the following 2 aims: 1) Develop a novel shared control framework for real-time upper limb prosthesis control and gripping and 2) Evaluate the performance and cognitive workload of the shared control framework. The shared controller will use an artificial neural network (ANN) to map the features of EMG signals to joint torque and a forward dynamics model to calculate joint kinematics. EMG and joint motion data will be collected from subjects and a reinforcement learning algorithm will be used to train the ANN to minimize the error between estimated and measured joint positions. A force sensor attached to the fingertip of a prosthetic hand will detect when contact with an object has been made and measure the grip force. The estimated torque of the metacarpophalangeal (MCP) joint will be used to estimate a desired grip force and a PID controller will drive the measured grip force to this desired grip force. To evaluate the framework, a virtual task will be used to test a subjects’ ability to control the grip force of the hand by having them follow a given force trajectory displayed on a monitor. Then, subjects will use both the shared controller and EMG PR to complete 2 functional tasks involving transporting fragile/deformable objects. Tasks will be completed with and without a mentally demanding dual task and the differences in performance will be used to estimate cognitive loads. This proposed work is expected to introduce a method of controlling transradial prostheses that provides reliable position-based control of multiple DOF and precise control of the grip force the prosthetic hand applies to objects with various levels of compliance. This method can reduce the difficulties and mental demands of object grasping tasks and lead to a higher acceptance rate of powered upper limb prostheses.