Recognition of upper limb movements based on hybrid EEG and EMG signals for human-robot interaction | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Articles in Press, Accepted Manuscript, Available Online from 30 March 2023 | ||
| Document Type: Research Paper | ||
| Authors | ||
| Huda Mustafa Rada* 1; Alia Karim Abdul Hassan2; Ali H. Al-Timemy3 | ||
| 1Computer Science, Science Collège, Baghdad University. Iraq, Baghdad | ||
| 2University of Technology - Iraq | ||
| 3Biomedical Eng. Department, Al-Khwarizmi College of Eng., University of Baghdad, Iraq | ||
| Abstract | ||
| Upper limb amputation severely limits a person's ability to move. Patients who have lost the use of one or more of their upper extremities have difficulty performing activities of daily living. To help improve the control of upper limb prosthesis with pattern recognition, non-invasive approaches (electroencephalography (EEG), and electromyography (EMG)) signals are proposed to be used in this paper. They are integrated with machine learning techniques to recognize the upper-limb motions of the considered body part. EMG and EEG signals are combined, and five features are utilized to classify seven hand movements such as (wrist flexion (WF), outward part of the wrist (WE), hand open (HO), hand close (HC), pronation (PRO), supination (SUP), and rest (RST)). Experiments demonstrated that using mean absolute value (MAV), waveform length (WL), Wilson Amplitude (WAMP), Sine Slope Changes (SSC), and Cardinality features of the proposed algorithm achieve a classification accuracy of 89.6% by using an LDA classifier when classifying seven distinct types of hand and wrist movement. | ||
| Keywords | ||
| Human Robot Interaction,,; ,،,؛Bio-signals analysis,,; ,،,؛LDA classifier | ||
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