A novel fatigue detection method for rehabilitation training of upper limb exoskeleton robot using multi-information fusion. (26th November 2020)

• Record Type: Journal Article
• Authors: Wang, Wendong; Li, Hanhao; Kong, Dezhi; Xiao, Menghan; Zhang, Peng
• Is Part Of: International journal of advanced robotic systems. Volume 17:Number 6(2020:Nov./Dec.)
• Publication Date: 2020-11-26
• Subjects: sEMG -- ECG -- multi-information fusion -- fatigue judgment -- upper extremity exoskeleton robot -- fuzzy logic controller; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881420974295
• Languages: English
• ISSNs: 1729-8806

Abstract

The utilization of upper extremity exoskeleton robots has been proved to be a scientifically effective approach for rehabilitation training. In the process of rehabilitation training, it is necessary to detect the fatigue degree during rehabilitation training in order to formulate a reasonable training plan and achieve better training efficiency. Based on the integral value of surface electromyography (sEMG), heart rate variability, and instantaneous heart rate, this article proposes a fatigue judgment method for multi-information fusion. Based on the integral value data, the feature extraction of the bioelectrical signals were implemented separately, then the fatigue recognition was conducted using the decision-level data fusion method. The bioelectrical signal acquisition system of electromyogram signals and electrocardiograph signals was developed for upper limb exoskeleton rehabilitation robot, and the acquisition and processing of electromyogram signals and electrocardiograph signals were completed. Finally, the fuzzy logic controller with instantaneous heart rate, heart rate variability, and surface electromyography signal was designed to judge fatigue degree, including the fuzzy device, fuzzy rule selector, and defuzzifier. The moderate fatigue state data were selected for testing, and the experimental results showed that the error of fatigue judgment is 4.3%, which satisfies the requirements of fatigue judgment.