by Abdullah Y. Al-Maliki 1,* 
, Kamran Iqbal 1 and Gannon White 2
1 Department of Electrical and Computer Engineering, University of Arkansas at Little Rock, Little Rock, 72204, USA
2 Department of Kinesiology, Colorado Mesa University, Grand Junction, 81501, USA
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 3, Issue 4, Page # 1-9, 2024; DOI: 10.55708/js0304001
Keywords: Elbow Angle Estimation, ANN, Softmax Classifier, sEMG, Signal Processing
Received: 21 January 2024, Revised: 22 March 2023, Accepted: 23 March 2023, Published Online: 16 April 2024
APA Style
Al-Maliki, A. Y., Iqbal, K., White, G., & Al-Maliki, A. (2024). Estimation of Elbow Joint Movement Using ANN-Based Softmax Classifier. Journal of Engineering Research and Sciences, 3(4), 1-9. https://doi.org/10.55708/js0304001.
Chicago/Turabian Style
Al-Maliki, Abdullah Y, Kamran Iqbal, Gannon White, and Abdullah Al-Maliki. “Estimation of Elbow Joint Movement Using ANN-Based Softmax Classifier.” Journal of Engineering Research and Sciences 3, no. 4 (2024): 1-9. https://doi.org/10.55708/js0304001.
IEEE Style
A. Y. Al-Maliki, K. Iqbal, G. White, and A. Al-Maliki, “Estimation of Elbow Joint Movement Using ANN-Based Softmax Classifier,” Journal of Engineering Research and Sciences, vol. 3, no. 4, pp. 1-9, 2024, doi: 10.55708/js0304001.
Estimating the natural voluntary movement of human joints in its entirety is a challenging problem especially when high accuracy is desired. In this paper, we build a modular estimator to estimate the elbow joint motion including angular displacement and direction. Being modular, this estimator can be scaled for application to other joints. We collected surface Electromyographic (sEMG) signals and motion capture data from healthy participants while performing elbow flexion and extension in different arm positions and at different effort levels. We preprocessed the sEMG signals, extracted features array, and used it to train an ANN-based Softmax classifier to estimate the angular displacement and movement direction. When compared against the motion cap-ture data, the classifier achieved estimation accuracy ranging from 80% to 90% with a resolution of 5°, which translates into Pear-son Correlation Coefficient (PCC) ranging from 0.91 to 0.95. Such high PCC values in mimicking the voluntary movement of the upper limb may help toward building intuitive prostheses, exoskeletons, remote-controlled robotic arms, and other Human Ma-chine Interface (HMI) applications.
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