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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Booranawong, Apidet
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Computer Science & IT Electrical & Electronics Engineering

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Processing of real-time surface electromyography signals during knee movements of rehabilitation participants Sengchuai, Kiattisak; Sittiruk, Thantip; Jindapetch, Nattha; Phukpattaranont, Pornchai; Booranawong, Apidet
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6526-6537

Abstract

In this work, we present a knee rehabilitation system focusing on the processing of surface electromyography (sEMG) signals measured from the vastus lateralis (VL) and vastus medialis (VM) muscles of rehabilitation participants. A two-channel electromyography (EMG) device and the NI-myRIO embedded device are used to collect real-time sEMG signals in accordance with pre-designed rehabilitation programs. The novelty and contribution of this work is that we develop an sEMG processing function where real-time sEMG data are automatically processed and sEMG results of both VL and VM in terms of root mean square value (RMS), different RMS levels of VL and VM, and maximum RMS for each round of knee movements are provided. The results here indicate how well the rehabilitation users can move their knees during rehabilitation, referring to knee and muscle performances. Experimental results from healthy participants show that we can automatically and efficiently collect and monitor rehabilitation results, allowing rehabilitation participants to know how their knees performed during testing and medical experts to evaluate and design treatment.
Statistical analysis of range of motion and surface electromyography data for a knee rehabilitation device Sengchuai, Kiattisak; Sittiruk, Thantip; Booranawong, Apidet; Jindapetch, Nattha
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp268-278

Abstract

This work introduces a statistical analysis of knee range of motion (ROM) and surface electromyography (EMG) data gathered from a knee extension rehabilitation device. Real-time ROM and EMG signals of rehabilitation users are measured using a single angle sensor and a two-channel EMG device (for the vastus lateralis and vastus medialis muscles). These signals are collected by the NI-myRIO embedded device in accordance with the designed rehabilitation program. The main contribution and novelty of this study is that real-time signals are automatically processed and transformed into statistical data for use by users and medical experts. A solution for extracting raw signals is proposed, in which several statistical functions such as range, mean, standard deviation, skewness, percentiles, interquartile range, and total knee holding times above the threshold level, are implemented and applied. The proposed solution is tested using data acquired from healthy people, which includes gender, age, body size, knee side, exercise behavior, and surgical experience. Results indicated that real-time signals and related statistical data on the knee’s performance can be efficiently monitored. With this solution, rehabilitation users can practice and learn about their knee performance, while medical experts can evaluate the data and design the best rehabilitation program for users.
A system for monitoring human postures, seizures, and falls from bed using radio and surface electromyography signals Intongkum, Chawakorn; Sengchuai, Kiattisak; Booranawong, Apidet
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7241

Abstract

In this work, a system for monitoring human postures, seizures, and falls from bed using received signal strength indicator (RSSI) and surface electromyography (sEMG) signals is studied through experiments. In this proposed system, a person who is located inside a wireless link is monitored by considering the change in measured RSSI signals as the 2.4 GHz IEEE 802.15.4 signals received at a receiver. Human motions in bed that affect RSSI levels can be captured. Thus, with this technique, it does not raise a privacy concern compared with vision-based technology. Additionally, sEMG signals associated with muscle movements from human postures are recorded from the human body’s abdominal muscles. Eight different activities, including normal and critical events, are tested and evaluated. Experimental results indicate that the proposed system could automatically monitor different human postures in real-time. RSSI and sEMG signals correlated to each posture have their own patterns. Furthermore, the relationship between human behaviors and RSSI and sEMG levels is summarized.
Co-Authors Intongkum, Chawakorn Jindapetch, Nattha Phukpattaranont, Pornchai Sengchuai, Kiattisak Sittiruk, Thantip