JOINT TORQUEESTIMATION OF sEMG SIGNALS BASED ON INTELLIGENT TECHNIQUES FOR ROBOTIC ASSISTIVE SYSTEM

Stroke is the leading cause of disability of people that influences the quality of
their daily life where an effective method is required for post-stroke rehabilitation.
Research has shown that robot is a good alternative where the electromyography
(EMG) signals have been proposed as an input to control the jointtorque of robotic
assistive system for rehabilitation.
This research focuses on implementation of intelligent algorithms to convert the
sEMG signals tojoint torque for task movement ofelbow flexion and knee extension
for robotic assistive system. In order to achieve this, several investigations were
carried out. Initially, experimental for sEMG signals acquisition from different test
subjects was conducted using a wireless EMG system. Subsequently, the signal
processing that involved processes such as filtering and feature extraction was
conducted. Thereafter, the actual joint torque was acquired through dynamic
modeling. Finally, simulation was conducted on nine mathematical models to convert
the sEMG signals to estimated joint torque using optimization techniques such as
Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).