Design and Experimental Validation of a Low-Cost EMG-Controlled Upper Limb Exoskeleton Arm Using Mechanical Advantage Pulley System
Design and Experimental Validation of a Low-Cost EMG-Controlled Upper Limb Exoskeleton Arm Using Mechanical Advantage Pulley System
Authors:
Dr. S.K.Shikalgar1, Tejas Parnakar2, Siddharaj Patil3 , Shrimant Khandekar4
1Professor, Department of Mechatronics Engineering, Sharad Institute of Technology, College of Engineering, Yadrav. Maharashtra, India.
2Student, Department of Mechatronics Engineering, Sharad Institute of Technology, College of Engineering, Yadrav. Maharashtra, India.
3 Student, Department of Mechatronics Engineering, Sharad Institute of Technology, College of Engineering, Yadrav. Maharashtra, India.
4 Student, Department of Mechatronics Engineering, Sharad Institute of Technology, College of Engineering, Yadrav. Maharashtra, India.
Abstract - This paper presents the design, development, and implementation of a biomechanical exoskeleton arm aimed at assisting and augmenting human upper-limb movement. The proposed system integrates electromyography (EMG) sensors, a microcontroller-based control unit, and a motor-driven pulley mechanism to detect user muscle activity and provide real-time motion assistance. The exoskeleton is designed to be lightweight, ergonomic, and cost-effective, ensuring usability across applications such as rehabilitation, industrial support, and assistive care. The system utilizes an ESP32 controller to process EMG signals and actuate a motor, enabling smooth and responsive movement that mimics natural arm motion. A feedback-based control mechanism is incorporated to enhance safety and accuracy, while a backup wireless control system ensures reliability in case of sensor failure. Experimental observations demonstrate improved lifting capability of up to 40–50%, reduced user fatigue, and stable performance. The proposed design addresses key limitations in existing systems, including affordability and accessibility, making it a viable solution for real-world deployment. Future improvements include integration of machine learning algorithms and further miniaturization for enhanced adaptability and performance.