Dexterous hand prostheses controlled via surface electromyography represent the most advanced non invasive functional restorative solution for hand amputees. However, control difficulties, comfort problems and high costs are still the main limitations of such commercial devices. The high cost can represent a barrier that is difficult to overcome, especially for pediatric populations and in developing countries. Low-cost technology was successfully used in the hand prosthetics field in recent years. In previous work, a low-cost gesture recognition armband called Myo showed promising results for hand gesture classification tasks in intact subjects. Most of these applications were based on machine learning techniques applied to the Myo raw data. However, the classifier provided with the Myo is able to identify five hand gestures, providing capabilities as a myoelectric control system. No studies have quantitatively investigated its performance in subjects with hand amputation, yet. The aim of this study is to quantitatively evaluate the performance of the Myo hand gesture classifier in hand amputees. Three subjects with hand amputation were asked to attempt performing the five pre-set hand gestures. Each gesture was repeated three times with the arm in three different postures. The subjects did not perform any training and did not receive any feedback. Overall classification accuracy for the four hand gestures based on electromyographic data ranged between 50% and 97%. A clear relation between the length of the residual limb and the classification accuracy was observed. The results show that the Myo built-in classifier can provide good performance when tested on hand amputees, supporting its applicability as a low-cost myoelectric control system.

Hand Gesture Classification in Transradial Amputees Using the Myo Armband Classifier

Atzori M.;Faccio D.;
2018

Abstract

Dexterous hand prostheses controlled via surface electromyography represent the most advanced non invasive functional restorative solution for hand amputees. However, control difficulties, comfort problems and high costs are still the main limitations of such commercial devices. The high cost can represent a barrier that is difficult to overcome, especially for pediatric populations and in developing countries. Low-cost technology was successfully used in the hand prosthetics field in recent years. In previous work, a low-cost gesture recognition armband called Myo showed promising results for hand gesture classification tasks in intact subjects. Most of these applications were based on machine learning techniques applied to the Myo raw data. However, the classifier provided with the Myo is able to identify five hand gestures, providing capabilities as a myoelectric control system. No studies have quantitatively investigated its performance in subjects with hand amputation, yet. The aim of this study is to quantitatively evaluate the performance of the Myo hand gesture classifier in hand amputees. Three subjects with hand amputation were asked to attempt performing the five pre-set hand gestures. Each gesture was repeated three times with the arm in three different postures. The subjects did not perform any training and did not receive any feedback. Overall classification accuracy for the four hand gestures based on electromyographic data ranged between 50% and 97%. A clear relation between the length of the residual limb and the classification accuracy was observed. The results show that the Myo built-in classifier can provide good performance when tested on hand amputees, supporting its applicability as a low-cost myoelectric control system.
Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
978-1-5386-8183-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3394655
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