Robust hand gesture recognition with a double channel surface EMG wearable armband and SVM classifier. (September 2018)

• Record Type: Journal Article
• Authors: Tavakoli, Mahmoud; Benussi, Carlo; Alhais Lopes, Pedro; Osorio, Luis Bica; de Almeida, Anibal T.
• Is Part Of: Biomedical signal processing and control. Volume 46(2018)
• Page Start: 121
• Page End: 130
• Publication Date: 2018-09
• Subjects: EMG -- Gesture recognition -- Human machine interfaces -- Wearable armband -- Support vector machine -- Classification; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.07.010
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Minimalist two channel surface EMG system for hand gesture detection is demonstrated. Support vector machine classification can detect five hand gestures with two sEMG channels. Overall classification rate of over 90% is demonstrated. A gesture is defined for locking/unlocking of the system which adds to the robustness. Abstract: 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. In addition, tolerance of the system for rejection of unsolicited gestures during the body movement was evaluated, and the two methods were implemented to ensure this; one based on an SVM threshold and another one based on the addition of a locking gesture. The resulting system is able to recognize up to 5 gestures (hand closing, hand opening, wrist flexion, wrist extension and double wrist flexion), presenting a classification accuracy of between 95% and 100% for a trained user and robustness against different body movements, guaranteed with the locking feature. We showed that misclassification of other gestures as the unlocking never happened for expert users.