Optimization of EMG-based hand gesture recognition: Supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees

Abstract

a b s t r a c tWe propose a methodological study for the optimization of surface EMG (sEMG)-based hand gesture classification, effective to implement a human-computer interaction device for both healthy subjects and transradial amputees. The widely commonly used unsupervised Principal Component Analysis (PCA) approach was compared to the promising supervised common spatial pattern (CSP) methodology to identify the best classification strategy and the related tuning parameters. A low density array of sEMG sensors was built to record the muscular activity of the forearm and classify five different hand gestures. Twenty healthy subjects were recruited to compute optimized parameters for ("within" analysis) and to compare between ("between" analysis) the two strategies. The system was also tested on a transradial amputee subject, in order to assess the robustness of the optimization in recognizing disabled users' gestures.Results show that RMS-WA/ANN is the best feature vector/classifier pair for the PCA approach (accuracy 88.81 ± 6.58%), whereas M-RMS-WA/ANN is the best pair for the CSP methodology (accuracy of 89.35 ± 6.16%). Statistical analysis on classification results shows no significant differences between the two strategies. Moreover we found out that the optimization computed for healthy subjects was proven to be sufficiently robust to be used on the amputee subject. This motivates further investigation of the proposed methodology on a larger sample of amputees. Our results are useful to boost EMG-based hand gesture recognition and constitute a step toward the definition of an efficient EMG-controlled system for amputees.

Experimental Study of Real-Time Classification of 17 Voluntary Movements for Multi-Degree Myoelectric Prosthetic Hand

“…As can be seen, the number of electrode is greater than or equal to the number of classes in nearly all of these studies. Also, almost all of the current works only deals with small number of classes (less than 10) [11,[17][18][19][20]23,29,30]. Only an extremely small number of studies have reported classification results for more than 10 classes but they require large number of electrodes [21,[31][32][33].…”

“…Over the past decades, different electrode placement strategies have been investigated. Some researchers study the use of multichannel electrode arrays [20] or high-density EMG (large number of electrodes) strategy [21,22], while others explore the precise anatomical positioning approach [18,23].…”

“…The functions of each muscle are summarized in Table 1. Muscles acting on the hand can be divided [20] Number of Electrodes In this study, six EMG sensors are positioned on the flexor carpi radialis muscle, flexor carpi ulnaris muscle, flexor digitorum profundus muscle, flexor pollicis longus muscle, extensor carpi radialis longus muscle and extensor pollicis longus muscle of the left arm as shown in Figure 4 [35]. The functions of each muscle are summarized in Table 1.…”

“…The functions of each muscle are summarized in Table 1. Muscles acting on the hand can be divided [20] Number of Electrodes …”

Longitudinal tracking of physiological state with electromyographic signals.

“…A similar, custom forearm armband device was employed by Riillo et al [46] to examine the use of gesture-recognition on healthy subjects and a transradial amputee.…”