Affiliation:
1. Department of Electronics and Communication Engineering, Goverment Engineering College, Hassan, India
Abstract
The EMG signals that have been processed can mimic human movements. For this study, raw EMG data obtained when the hands are in repose (rest), in a clasp, and when the wrist is buckled and stretched were used to categorise four distinct forms of hand gestures using a MATLAB-based intelligent framework (open access data set). Statistical-time-domain features are applied to sort various hand gestures in this investigation. The K-Nearest-Neighbor (KNN) and Support-Vector-Machine (SVM) classifiers are used for classification and comparison. Furthermore, our method outperforms a state-of-the-art method on other data sets of hand gestures.
Publisher
World Scientific and Engineering Academy and Society (WSEAS)
Subject
Computer Networks and Communications,Computer Vision and Pattern Recognition,Signal Processing,Software
Reference22 articles.
1. Diane W. Braza, Jennifer N. Yacub Martin, “Upper Limb Amputations,” Essentials of Physical Medicine and Rehabilitation (Fourth Edition), Elsevier, 2020, page 651-657
2. “EMG Signal for gesture recognition| Kaggle.” https://www.kaggle.com/sojanprajapati/emgsignal-for-gesturerecognition (accessed Feb. 08, 2022).
3. Marco E. Benalcázar, Andrés G. Jaramillo, Jonathan A. Zea, and Andrés Páez, “Hand Gesture Recognition Using Machine Learning and the Myo Armband”, 25th European Signal Processing Conference (EUSIPCO), 2017.
4. Jingxiang Chen, Chao Liu, Rongxin Cui, Chenguang Yang, “Hand Tracking Accuracy Enhancement by Data Fusion Using Leap Motion and Myo Armband”, 2019 International Conference on Unmanned Systems and Artificial Intelligence pages 256-261
5. Tony Chau “Machine Learning for Gesture Recognition with Electromyography,”, Norwegian University of Science and Technology,Dept. of CS, June 2017, unpublished.