Comparison Study Between Different Classifiers’ Techniques For Human Hand Grasping Force Using sEMG

Researchers in the field of assistance and rehabilitative devices have used a range of robotic techniques to enhance the quality of life for individuals with disabilities through improving assistive equipment. These tools aid in recovering some of the abilities required for the damaged limb to function in its environment. EMG signals are now often employed in the field of prostheses. The challenge is to classify various grasping force levels so that people with disabilities can support various weights. With no requirement for a human element, this would enable people with disabilities to handle a range of weights. The current study proposes a grasping system that have succeeded in holding four different weights (5 10 15 20 ) securely with a mean square error not greater than 0.044 and a standard deviation not greater than 0.203 .