Abstract
Transhumeral amputation has a significant effect on a person’s independence and quality of life. Myoelectric prostheses have the potential to restore upper limb function, however their use is currently limited due to lack of intuitive and natural control of multiple degrees of freedom. The goal of this study was to evaluate a novel transhumeral prosthesis controller that uses a combination of kinematic and electromyographic (EMG) signals recorded from the person’s proximal humerus. Specifically, we trained a time-delayed artificial neural network to predict elbow flexion/extension and forearm pronation/supination from six proximal EMG signals, and humeral angular velocity and linear acceleration. We evaluated this scheme with ten able-bodied subjects offline, as well as in a target-reaching task presented in an immersive virtual reality environment. The offline training had a target of 4° for flexion/extension and 8° for pronation/supination, which it easily exceeded (2.7° and 5.5° respectively). During online testing, all subjects completed the target-reaching task with path efficiency of 78% and minimal overshoot (1.5%). Thus, combining kinematic and muscle activity signals from the proximal humerus can provide adequate prosthesis control, and testing in a virtual reality environment can provide meaningful data on controller performance.
Highlights
There are approximately 5-6000 major limb amputations carried out each year in the UK (NASDAB, 2005)
We developed a transhumeral prosthesis controller that combines EMG and kinematic signals from the proximal humerus to predict the movement of the forearm
Panel A shows the rectified and filtered EMG data, and panels B and C show the humerus IMU velocity and acceleration data. These 12 signals were the inputs to the artificial neural networks (ANN), while the outputs were elbow flexion/extension and forearm pronation/supination, calculated from the IMU on the humerus and forearm, shown in panel D
Summary
There are approximately 5-6000 major limb amputations carried out each year in the UK (NASDAB, 2005). The proportion of amputees referred with upper limb amputations is only about 5%, they are a population with high functional demands. Trauma is the major reason for upper limb amputation, and this is reflected in the age group affected by the condition, with 66% aged less than 55 years (NASDAB, 2005). Loss of the upper limb can have a significant effect on the ability to work, independence, and overall quality of life. Amputees who choose to fit a prosthetic limb onto the remaining arm have two options: a passive (cosmetic) prosthesis, which offers little functional benefit, or an active prosthesis that has the potential to restore upper limb function. Active prostheses are either body-powered, which are controlled by upper body movements through straps and cables, or myoelectric, which are electrically powered and use the residual neuromuscular system for control
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