Analysis of EMG based on different types of hand movement for exoskeleton hand application

Mohamad Fuad, Muhammad Shahmi (2025) Analysis of EMG based on different types of hand movement for exoskeleton hand application. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)

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Abstract

Hand movement impairments significantly affect individuals' quality of life, highlighting the need for advanced assistive technologies. This study addresses the challenge of enabling intuitive and precise control of an exoskeleton hand by analyzing electromyography (EMG) signals. The objective is to develop a robust system capable of accurately interpreting muscle activity to facilitate seamless and natural interaction with an exoskeleton device. To achieve this, EMG signals were acquired using surface electrodes and underwent rigorous preprocessing to eliminate noise and artifacts, ensuring clean and reliable data for analysis. Sophisticated machine learning algorithms were employed to classify and differentiate between a range of hand movements, including grasping, pinching, and releasing objects. The system's performance was evaluated in terms of its accuracy in movement classification and its ability to translate these classifications into real-time control of the exoskeleton hand. The results demonstrated high classification accuracy and effective real-time translation of hand movements, underscoring the system’s potential for practical applications. This research highlights the viability of EMG signal analysis as a tool for enhancing the functionality of exoskeleton hands, offering a promising solution for individuals with hand movement impairments.

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