Electromyography (EMG) Signal Acquisition for Exoskeleton Control: Enhancing Precision in Human-Machine Interaction

Electromyography (EMG) signals, which measure the electrical activity of muscles during contraction, deal with the hope of controlling the actions of the exoskeleton. This paper exposes the acquisition and application of EMG signals for precise and responsive control of exoskeletons. Moreover, the natural neuromuscular signals generated by the user aim to create a seamless interface between the human body and the assistive device, enhancing functionality and user experience. The EMG signals are obtained by placing the electrodes in the respective area of our body, filtering the data, signal amplification, and using noise reduction techniques. Furthermore, EMG data is processed for noise removal, where artefacts ensure the clarity and reliability of the acquired signals. Those signals were used to develop a control algorithm to translate muscle activity into corresponding exoskeleton movements. The challenges and limitations associated with EMG-based controls are signal variability, cross-talk, and calibration. Finally, this paper presents results, which demonstrate that EMG-based control offers a high degree of accuracy and responsiveness, especially requiring to control the fine motor rotation and varying force levels. This approach not only improves the usability of exoskeletons for individuals with mobility impairments but also has certain potential applications like EMG-controlled exoskeletons in various domains, including rehabilitation, assistive technology, various military settings and human-machine interfaces. EMG-driven systems to revolutionize the field of wearable robotics, paving the way for more intuitive and effective human-machine interfaces.