Myoelectric Control of a Robotic Manipulator Using Processed Electromyography Signals

The purpose of this paper is to address the limitations by simulating human hand movements using an Arduino Uno, OLIMEX SHIELD-EMG-EKG, and steering gear, aiming to enhance the precision and reliability of biomedical applications. The proposed system captures surface electromyography (sEMG) signals and processes them with advanced filtering techniques to reduce power line interference and motion artefacts. These refined signals are then used to control a robotic hand, aiming for accurate real-time replication of human hand movements, focusing on overcoming the challenges of intuitive, adaptive, and robust control. Despite promising laboratory results, many systems in the literature fail to meet the stringent criteria for widespread patient acceptance, such as low sensitivity to electrode repositioning and minimal training requirements. However, this study presents a low-cost, flexible solution that demonstrates significant potential in bridging this gap. The system has successfully tracked and reproduced complex hand movements, proving its effectiveness as a platform for further research in human-machine interfaces and neurorehabilitation technologies. The findings suggest that this approach could offer a viable path forward in enhancing the precision and dependability of prosthetic control systems, thus contributing to the ongoing efforts to meet the critical demands of real-world biomedical applications.