IDENTIFICATION OF ACTIVE HUMAN UPPER LIMB MUSCLE FOR INTENTION DETECTION ACTIVITY

Abstract

The upper limb comprises various muscle groups that control movement through contraction and extension. These actions, which help in the joint movement of the human upper limb, can be used for intention detection activities. Intention detection is a method of extracting the information from the muscle movement of the upper limb that will help in classifying the type of muscle group for each different type of movement of the upper limb. This study investigates the use of Electromyography (EMG) to identify the muscle groups activated during specific upper limb movements based on their electrical activity.  In this work, the participant performed various upper limb movements while wearing an EMG sensor kit. The captured signal were processed and visualized using software to identify the activated muscles. The experiment was then repeated with additional weight of 5 kg to analyze the effect of load on muscle activation. The results demonstrate that distinct upper limb movements activate different muscle groups. For instance, flexion primarily activates the biceps brachii, while the triceps brachii remains inactive. Furthermore, the study reveals increased muscle activity during movements with added load compared to unloaded movements.