Design and Development of a 3D-Printed Gesture Controlled Robotic Hand Using EMG Signals

Parth V Kanani; Prajapati Karan Vinodbhai; Preet Panchal; Apexa Purohit; Mayur Chavda; Mayank Dev Singh; Jai Bahadur Balwanshi

doi:10.69968/4z2ee328

Authors

Parth V Kanani UG Student, Department of Mechatronics Engineering, ITM Vocational University, Vadodara, Gujarat, India
Prajapati Karan Vinodbhai UG Student, Department of Mechatronics Engineering, ITM Vocational University, Vadodara, Gujarat, India
Preet Panchal UG Student, Department of Mechatronics Engineering, ITM Vocational University, Vadodara, Gujarat, India
Apexa Purohit Assistant Professor, Department of Mechatronics Engineering, ITM Vocational University, Vadodara, Gujarat, India
Mayur Chavda Assistant Professor, Department of Mechatronics Engineering, ITM Vocational University, Vadodara, Gujarat, India
Mayank Dev Singh Associate Professor, Department of Mechatronics Engineering, ITM Vocational University, Vadodara, Gujarat, India
Jai Bahadur Balwanshi Dean, Faculty of Engineering & Technology, ITM Vocational University, Vadodara, Gujarat, India

DOI:

https://doi.org/10.69968/4z2ee328

Keywords:

Anthropomorphic Robotic Hand, PCA9685, Electromyography, Prosthetics, Human-Robot Interaction, 3D Printing

Abstract

The rapid advancement in human-machine interaction and biomedical engineering has created unprecedented opportunities for the development of highly intuitive and cost-effective robotic prosthetics. This research paper presents the comprehensive design, development, and implementation of a 3D-printed, anthropomorphic robotic hand controlled through input using Electromyography (EMG) signals. The primary objective of this work is to accurately mimic human finger kinematics in real-time, providing an intuitive, low-latency control interface suitable for amputees, remote teleoperation, and human-robot collaboration. The proposed system employs a microcontroller-based architecture that processes analog data obtained from a data glove equipped with varying-resistance flex sensors, coupled with an EMG module capable of reading surface muscle potentials.
To achieve high-fidelity fine motor control, the processed bio signals and physical kinematic data are filtered, digitized, and transmitted via I2C communication to a PCA9685 16-channel PWM servo driver, which subsequently actuates a network of SG90 servo motors attached to the 3D-printed phalanges via a mechanical tendon system. The integration of 3D printing technology significantly reduces the weight and manufacturing costs while preserving structural integrity and allowing customized modularity. Experimental validation demonstrates that the robotic hand successfully mimics complex gestures with an average system latency of under 45 milliseconds. This paper details the hardware topology, software algorithms, circuit design, and the experimental results regarding load capacity, response time, and grasping efficiency.

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