Spatial correlation of high density EMG signals provides features robust to electrode number and shift in pattern recognition for myocontrol.
Research on pattern recognition for myoelectric control has usually focused on a small number of electromyography (EMG) channels because of better clinical acceptability and low computational load with respect to multi-channel EMG. However, recently, high density (HD) EMG technology has substantially improved, also in practical usability, and can thus be applied in myocontrol. HD EMG provides several closely spaced recordings in multiple locations over the skin surface. This study considered the use of HD EMG for controlling upper limb prostheses, based on pattern recognition. In general, robustness and reliability of classical pattern recognition systems are influenced by electrode shift in dons and doff, and by the presence of malfunctioning channels. The aim of this study is to propose a new approach to attenuate these issues. The HD EMG grid of electrodes is an ensemble of sensors that records data spatially correlated. The experimental variogram, which is a measure of the degree of spatial correlation, was used as feature for classification, contrary to previous approaches that are based on temporal or frequency features. The classification based on the variogram was tested on seven able-bodied subjects and one subject with amputation, for the classification of nine and seven classes, respectively. The performance of the proposed approach was comparable with the classic methods based on time-domain and autoregressive features (average classification accuracy over all methods ∼ 95% for nine classes). However, the new spatial features demonstrated lower sensitivity to electrode shift ( ± 1 cm) with respect to the classic features . When even just one channel was noisy, the classification accuracy dropped by ∼ 10% for all methods. However, the new method could be applied without any retraining to a subset of high-quality channels whereas the classic methods require retraining when some channels are omitted. In conclusion, the new spatial feature space proposed in this study improved the robustness to electrode number and shift in myocontrol with respect to previous approaches.
Antonietta
A
Stango
Francesco
F
Negro
Dario
D
Farina
D000328:Adult; D000465:Algorithms; D016477:Artifacts; D004566:Electrodes; D004576:Electromyography / Q000295:instrumentation* / Q000379:methods*; D025461:Feedback, Physiological; D005260:Female; D006801:Humans; D008875:Middle Aged; D009119:Muscle Contraction / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D062211:Spatio-Temporal Analysis