Online Myoelectric Control of a Dexterous Hand Prosthesis by Transradial Amputees

Abstract

A real-time pattern recognition algorithm based on k-nearest neighbors and lazy learning was used to classify, voluntary electromyography (EMG) signals and to simultaneously control movements of a dexterous artificial hand. EMG signals were superficially recorded by eight pairs of electrodes from the stumps of five transradial amputees and forearms of five able-bodied participants and used online to control a robot hand. Seven finger movements (not involving the wrist) were investigated in this study. The first objective was to understand whether and to which extent it is possible to control continuously and in real-time, the finger postures of a prosthetic hand, using superficial EMG, and a practical classifier, also taking advantage of the direct visual feedback of the moving hand. The second objective was to calculate statistical differences in the performance between participants and groups, thereby assessing the general applicability of the proposed method. The average accuracy of the classifier was 79% for amputees and 89% for able-bodied participants. Statistical analysis of the data revealed a difference in control accuracy based on the aetiology of amputation, type of prostheses regularly used and also between able-bodied participants and amputees. These results are encouraging for the development of noninvasive EMG interfaces for the control of dexterous prostheses.

Evidence of muscle synergies during human grasping

“…In this case the situation seems much better even for amputees: residual muscle activity of excellent quality has recently been found in long-term amputees [30,10,33,13].…”

Building the Ninapro database: A resource for the biorobotics community

“…Even worse, there is no publicly available database so far, in which both intact subjects and amputees are classified according to their clinical history. As a result, each research group chooses a different way to record, store, and process the data, while the few experiments performed on amputees (among which [3], [13], [25], [26]) are only conclusive for the (small) set of subjects considered in the corresponding study.…”

Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees

“…These classifiers were selected due to their high performance in classification problems and low computational complexity (Chowdhury et al, 2013), being recommended as robust classifiers in several studies (Chowdhury et al, 2013;Cipriani et al, 2011;Guo et al, 2015;Khushaba et al, 2012;Oskoei and Hu, 2008;Phinyomark et al, 2012a;Wang et al, 2013). In addition, five-fold cross validation with all trials of the experiments was used to assess the performance of the classifiers.…”

“…Some studies with amputees using few number of electrodes have been conducted in order to fulfil this gap, such as done in Al-Timemy et al (2013), Cipriani et al (2011), Li et al (2011, Kumar et al (2013) and Tenore et al (2009). In particular, in Kumar et al (2013) a method based on wavelet maxima density was proposed as a non-linear parameter to extract relevant from sEMG signals using only one channel, but no grasp gestures were considered.…”