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EMG based classification for continuous thumb angle and force prediction

Siddiqi, Abdul Rahman and Sidek, Shahrul Na'im and Roslan, Muhammad Rozaidi (2015) EMG based classification for continuous thumb angle and force prediction. In: 2015 IEEE International Symposium on Robotics and Intelligent Sensors (IEEE IRIS20I5), 18th-20th October 2015, Langkawi, Kedah.

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Human hand functions range from precise-minute handling to heavy and robust movements. Remarkably, 50 percent of all hand functions are made possible by the thumb. Therefore, developing an artificial thumb which can mimic the actions of a real thumb precisely is a major achievement. Despite many efforts dedicated to this area of research, control of artificial thumb movements in resemblance to our natural movement, still poses as a challenge. Most of the development in this area is based on discontinuous thumb position control, which makes it possible to recreate several of the most important functions of the thumb but does not result in totalimitation. This work looks into the classification of Electromyogram (EMG) signals from thumb muscles for the prediction of thumb angle and force during flexion motion. For this purpose, an experimental setup is developed to measure the thumb angle & force throughout the range of flexion and simultaneously gather the EMG signals. Further various different featuresare extracted from these signals for classification and the most suitable feature set is determined and applied to different classifiers. A ‘piecewise- discretization’ approach is used for continuous angle prediction. Breaking away from previous researches, the frequency-domain features performed better than the timedomain features, with the best feature combination turning out to be MDF-MNF-MNP. As for the classifiers, the Support Vector Machine proved to be the most accurate classifier giving about 70% accuracy for both angle and force classification and close to 50% for joint angle-force classification.

Item Type: Conference or Workshop Item (Other)
Additional Information: 3028/47317
Uncontrolled Keywords: Electromyogram (EMG), Biosignal Processing, Classification, Frequency domain feature, Motion Prediction.
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA164 Bioengineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User: Dr. Shahrul Naim Sidek
Date Deposited: 16 Mar 2017 10:39
Last Modified: 10 Jan 2019 13:01
URI: http://irep.iium.edu.my/id/eprint/47317

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