sEMG Signal-Based Lower Limb Human Motion Detection Using a Top and Slope Feature Extraction Algorithm

• Published in: IEEE signal processing letters 2017-07, Vol.24 (7), p.929-932
• Main Authors: Ryu, Jaehwan, Byeong-Hyeon Lee, Deok-Hwan Kim
• Format: Article
• Language: English
• Subjects: Electromyography (EMG); Feature extraction; gait recognition; human–computer interaction; Legged locomotion; locomotion mode; Motion detection; Muscles; Reactive power; Signal processing algorithms; Timing
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2016.2636320

This letter presents lower limb human motion detection using a surface electromyogram (sEMG) with a top and slope (TAS) feature extraction algorithm. Lower limb human motion detection using sEMG signal is generally divided into gait subphase detection, locomotion mode recognition, and mode change detection. Existing feature extraction algorithms using sEMG signal have several innate problems in recognizing lower limb human motion detection. With respect to time-domain features, their values may be analogous because two different gait subphases and locomotion mode may have similar muscle activity pattern. Therefore, it is not easy to select the proper feature set of sEMG signals. The TAS feature extraction algorithm reflects better timing characteristics of sEMG signals than the existing time-domain feature extraction algorithm. Therefore, it can provide high accuracy in lower limb human motion detection. Experimental results show that the average detection accuracy values of the proposed method in terms of the gait subphase detection, locomotion mode recognition, and mode change detection are increased by 8%, 5%, and 4% better than those of feature values of the Willison amplitude, respectively.