Efficient Early Prediction of Hand Movements using Two-Channel EMG Signal Analysis

Document Type : Research Article

Authors

1 Biomechatronics and Cognitive Engineering Research Lab, School of Mechanical Engineering, Iran University of Science and Technology

2 Biomechatronics and Cognitive Engineering Research Lab, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran

3 biomechatronics and cognitive engineering research lab, school of mechanical engineering, iust

Abstract

The electrical activity produced by muscles is recorded by the Electromyography (EMG) signal, a dynamic biosignal. This dynamic nature represents the complex muscular behavior patterns. EMG-based recognition systems have far-reaching effects, revolutionizing human-computer interactions, enabling sign language detection, and giving amputees greater control over their devices.

This study aims to predict specific hand movements by analyzing electromyography (EMG) signals from a minimal number of channels. The computational cost efficiency of this approach makes it suitable for real-time applications. EMG signals were collected from the forearm muscles of 15 healthy subjects (5 women and 10 men) aged between 19 and 24. Preprocessing tasks were applied to the raw signals, including high-pass filtering, Butterworth filtering, notch filtering, and signal rectification.

The dataset consists of recordings from four channels, of which two channels were selected for predicting hand movements. Two 500 ms time windows from both channels were used as inputs for a weighted k-nearest neighbors (KNN) algorithm. The task involved predicting and classifying the intentions of three hand movements: pinching, finger abduction, and grasping.

The procedure's overall results demonstrate 83.1% detection accuracy for the weighted KNN indicating high precision and a relatively short response time for predicting these hand movements in healthy subjects.

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AUT Journal of Mechanical Engineering
Volume 8, Issue 3
In Progress
September 2024
Pages 5-5

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