Classification of Finger Movements for the Dexterous Hand Prosthesis Control With Surface Electromyography

Abstract

A method for the classification of finger movements for dexterous control of prosthetic hands is proposed. Previous research was mainly devoted to identify hand movements as these actions generate strong electromyography (EMG) signals recorded from the forearm. In contrast, in this paper, we assess the use of multichannel surface electromyography (sEMG) to classify individual and combined finger movements for dexterous prosthetic control. sEMG channels were recorded from ten intact-limbed and six below-elbow amputee persons. Offline processing was used to evaluate the classification performance. The results show that high classification accuracies can be achieved with a processing chain consisting of time domain-autoregression feature extraction, orthogonal fuzzy neighborhood discriminant analysis for feature reduction, and linear discriminant analysis for classification. We show that finger and thumb movements can be decoded accurately with high accuracy with latencies as short as 200 ms. Thumb abduction was decoded successfully with high accuracy for six amputee persons for the first time. We also found that subsets of six EMG channels provide accuracy values similar to those computed with the full set of EMG channels (98% accuracy over ten intact-limbed subjects for the classification of 15 classes of different finger movements and 90% accuracy over six amputee persons for the classification of 12 classes of individual finger movements). These accuracy values are higher than previous studies, whereas we typically employed half the number of EMG channels per identified movement.

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

“…However, their work included high-density sEMG signals (twelve channels). Moreover, Al-Timemy et al (2013) included twelve different based-finger gestures with six amputees and six sEMG channels, with 89% of accuracy. However, their study did not include grasp gestures, a drawback that our research has overcome.…”

“…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.…”

Index Finger Motion Recognition Using Self-Advise Support Vector Machine

“…The EMG signals used in this paper were obtained from [10]. EMG signals of ten normal people Parameters were included.…”

“…Nowadays, SVM has been used frequently in the various classification problems, including the pattern-recognition-based MCS [9][10][11] due to its generalization performance in the classification cases over other aforementioned methods. Some improvements on SVM have been proposed like Self-advise SVM (SA-SAVM) [12].…”

Myoelectric feature extraction using temporal-spatial descriptors for multifunction prosthetic hand control

“…The EMG signals were originally used in [15], with 11 surface EMG sensors sampled at 2000 Hz with a custombuilt data acquisition system (NI USB 6210, National Instruments, USA) with 16-bit resolution. A Virtual Instrument (VI) was developed in LABVIEW (National Instruments, USA) to display and store the EMG signals.…”