Robust hand gesture recognition with a double channel surface EMG wearable armband and SVM classifier.
Pedro Alhais Lopes
Luis Bica Osorio
Anibal T de Almeida
Integration of surface EMG sensors as an input source for Human Machine Interfaces (HMIs) is getting an increasing attention due to their application in wearable devices such as armbands. For a wearable device, comfort and lightness are important factors. Therefore, in this article we focus on a minimalistic approach, in which we try to classify four gestures with only 2 EMG channels installed on the flexor and extensor muscles of the forearm. We adopted a two-channel EMG system, together with a high dimensional feature-space and a support vector machine (SVM) as a classifier.
The purpose of this article is then to explore a minimalistic approach in order to achieve hand gestures. Therefore, we explore recognition of four hand gestures (open, close, wave in, wave out)
with only two EMG channels.
Indicative zones for electrodes positioning.
The forearm anatomy, Wrist Flexor and Extensor muscles.
FFT coefficients: The figure shows the FFT coefficients for no gestures (blue), close gestures (red), and open gestures (green), ranging from 0 to 500 Hz. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
Armband structure: The figure shows an elastic band with velcro attached to the 3d printed case which encloses the dry electrodes and the pre-processing board.
Robust hand gesture recognition with a double channel surface EMG wearable armband and SVM classifier
M. Tavakoli, C. Benussi, P. Lopes, L. Osorio, A.Almeida, “Robust hand gesture recognition with a double channel surface EMG wearable armband and SVM classifier”, Biomedical Signal Processing and Control, 46, 2018, 121-130, ISSN 1746-8094