Summary
International Workshop on Smart Info-Media Systems in Asia
2022
Session Number:SS1
Session:
Number:SS1-4
Biceps and Triceps Electrical Activity Analysis based on using Low-Cost Sensor: Case Study
Hassan Qassim, Wan Zuha Wan Hasan, H. R. Ramli, Hazreen Harith, Liyana Najwa Inche Mat,
pp.15-20
Publication Date:2022/9/15
Online ISSN:2188-5079
Summary:
Upper limb's Surface Electromyography (sEMG) has been widely investigated and used for controlling of rehabilitation and prosthetic robots. However, accurate acquiring and processing of the sEMG signals requires an expensive commercial EMG system. In addition, proper analysis of the processed sEMG signal is also required to obtain beneficial information. Therefore, this research investigates the efficiency of using a low-cost EMG sensor, specifically MyoWare Muscle Sensor from advancer technology, in collecting an accurate sEMG signal. The electrical activity of biceps and triceps muscles were collected using Myoware Muscle sensor to investigate how accurate the aforementioned sensor in fulfilling two objectives. Firstly, interpreting the subject's intention in terms of flexing and extending the upper limb at elbow joint. Secondly, assessing the localized muscle fatigue that accompanies biceps and triceps during contraction. The collected sEMG signals were processed, filtered, segmented and specific features were extracted in time and frequency domains. Six features, namely, MAV, RMS, SAV, SD, ZC, and SSC, were extracted from the segmented sEMG in time domain to predict the user's intention of flexing and extending the elbow joint. In addition, frequency-domain features, specifically the mean frequency MNF and the median frequency MDF, were also extracted to evaluate the sensor's efficiency in assessing the localized muscle fatigue. In terms of predicting the user's intention, results showed that only particular features, specifically SAV and SD, were able to efficiently interpret the flexion and the extension of the elbow joint. However, MNF and MDF have both accurately assessed the localized muscle fatigue over the time. Consequently, special attention should be taken when dealing with low-cost EMG sensor.