The Classification of Elbow Extension and Flexion: A feature selection investigation

Authors

  • Mohamad Ilyas Rizan 1Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, 26600 Pahang, Malaysia.
  • Muhammad Nur Aiman Shapiee Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, 26600 Pahang, Malaysia.
  • Muhammad Amirul Abdullah Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, 26600 Pahang, Malaysia.
  • Mohd Azraai Mohd Razman Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, 26600 Pahang, Malaysia.
  • Anwar P. P. Abdul Majeed Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, 26600 Pahang, Malaysia.

DOI:

https://doi.org/10.15282/mekatronika.v2i2.7017

Keywords:

Mechanomyography, Elbow movement, Machine Learning, Classifier models, Classififcation

Abstract

Nowadays, the worldwide primary reasons of long-term disability is stroke. When the blood supply to your brain is interupted and reduced, stroke occurs as it depriving brain tissue of nutrients and oxygen. In the modern world, advanced technology are revolutionizing the rehabilitation process. This research uses mechanomyography (MMG) and machine learning models to classify the elbow movement, extension and flexion of the elbow joint. The study will aid in the control of an exoskeleton for stroke patient's rehabilitation process in future studies. Five volunteers (21 to 23 years old) were recruited in Universiti Malaysia Pahang (UMP) to execute the right elbow movement of extension and flexion. The movements are repeated five times each for two active muscles for the extension and flexion motion, namely triceps and biceps. From the time domain based MMG signals, twenty-four features were extracted from the MMG before being classified by the machine learning model, namely k-Nearest Neighbors (k-NN). The k-NN has achieved the classification accuracy (CA) with 88.6% as the significant features are identified through the information gain approach. It may well be stated that the suggested process was able to classify the elbow movement well

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Published

2020-12-17

How to Cite

[1]
M. I. . Rizan, M. N. A. Shapiee, M. A. . Abdullah, M. A. Mohd Razman, and A. P. P. Abdul Majeed, “The Classification of Elbow Extension and Flexion: A feature selection investigation”, MEKATRONIKA, vol. 2, no. 2, pp. 68–73, Dec. 2020.

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Original Article

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