ADAPTIVE CONTROL SYSTEM USING ELECTROMYOGRAPHIC SIGNALS FOR A STANDING ASSIST DEVICE FOR INDIVIDUALS WITH MOTOR IMPAIRMENTS

According to the 2022 World Health Organization report, over 1.3 billion people worldwide live with some form of disability, highlighting the growing need for effective rehabilitation technologies.Verticalization-assisting patients in achieving and maintaining an upright posture-is a critical component of rehabilitation for individuals with motor impairments.This paper presents the design, development, and initial evaluation of an adaptive standing frame control system that utilizes electromyographic (EMG) signals to enable semi-active patient participation in postural transitions.The system integrates hardware components such as a MyoWare EMG sensor, a high-precision analogue-to-digital converter (ADS1115), a Raspberry Pi 4B single-board computer, and a motor driver to control a lifting mechanism.The control algorithm calibrates individual muscle activity, filters real-time EMG data, and adjusts motor speed accordingly, providing responsive assistance during sitting-to-standing movements.The prototype was successfully assembled and activated, demonstrating the feasibility of using electromyographic signals for real-time control of rehabilitation devices, and the flexibility of the system allows for further enhancements and adaptation for user-specific needs.The results of internal testing indicate that the system responds reliably to muscle-generated signals and may serve as a foundation for future rehabilitative devices based on human-machine interaction.With continued refinement, extended functional testing, and clinical validation, it has the potential to evolve into a fully certified assistive device suitable for therapeutic use in rehabilitation centres or home-based care settings.