STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Mechatronics and Automation Review Article

Smart Bionic Hand: Combining Hardware and AI For Adaptive Prosthetic Functionality

  1. Sujeet More Department of Computer Engineering, KJEI's Trinity College of Engineering and Research
  2. Mandar Karnik Department of Computer Engineering, KJEI's Trinity College of Engineering and Research
  3. Vaishnavi Ramgir Department of Computer Engineering, KJEI's Trinity College of Engineering and Research
  4. Saraswati Swar Department of Computer Engineering, KJEI's Trinity College of Engineering and Research
  5. Vaijanti Rajure Department of Computer Engineering, KJEI's Trinity College of Engineering and Research

Abstract

This research paper presents the development and integration of a bionic arm that leverages both hardware components and artificial intelligence (AI) models for enhanced functionality and user interaction. The hardware design includes key components such as servo motors, an Arduino UNO microcontroller, electromyography (EMG) sensors, and a lithium-ion battery. The EMG sensors detect muscle signals, which serve as inputs for controlling the arm's movements, while the servo motors are responsible for translating these signals into mechanical motion. On the software side, AI and machine learning (ML) algorithms are employed to interpret EMG sensor data, enabling the bionic arm to learn and adapt to the user's muscle patterns over time. This integration aims to improve the arm's accuracy, responsiveness, and customization, offering a more natural user experience. The system's adaptability enhances motor control and opens the door for future prosthetic devices to become more intuitive. This research explores the potential of combining hardware with AI to develop more functional, user-friendly prosthetics, addressing challenges such as signal processing, energy efficiency, and real-time adaptability.

Keywords

References (11)

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