Depth vision guided hand gesture recognition using electromyographic signals. (2nd August 2020)

• Record Type: Journal Article
• Authors: Su, Hang; Ovur, Salih Ertug; Zhou, Xuanyi; Qi, Wen; Ferrigno, Giancarlo; De Momi, Elena
• Is Part Of: Advanced robotics. Volume 34:Number 15(2020)
• Page Start: 985
• Page End: 997
• Publication Date: 2020-08-02
• Subjects: Depth vision -- hand gesture recognition -- clustering -- classification -- electromyographic signals; Robotics -- Periodicals; Robotics -- Japan -- Periodicals; Robotics; Japan; Periodicals; 629.89205
• DOI: 10.1080/01691864.2020.1713886
• Languages: English
• ISSNs: 0169-1864

Abstract

Abstract : Hand gesture recognition has been applied to many research fields and has shown its prominent advantages in increasing the practicality of Human-Robot Interaction (HRI). The development of advanced techniques in data science, such as big data and machine learning, facilitate the accurate classification of the hand gestures using electromyography (EMG) signals. However, the processing of the collection and label of the large data set imposes a high work burden and results in time-consuming implementations. Therefore, a novel method is proposed to combine the benefits of depth vision learning and EMG based hand gesture recognition. It is capable of labeling the class of the collected EMG data under the guidance of depth vision automatically, without consideration of the hand motion sequence. Finally, we demonstrated the proposed method for recognizing the ten hand gestures using a Myo armband. The experiment is set in a supervised learning way to evaluate the performance of the designed Hk-means algorithm. It shows that the proposed method can succeed in hand gesture recognition without labeling the data in advance. GRAPHICAL ABSTRACT: