Assistive device to control prosthetic hand movements using machine learning approach

Abstract

Smart Assistive devices developed to support the disabled people suffered with Monoplegia. The Monoplegia kind of a paralysis which have the impact on one arm or one leg. The person affected with this disease unable to move the hand or leg to perform their regular activities. The rehabilitation process of paralysis affected patient involves make them to perform their activities by own with the help of an assistive smart device. The device must be capable to identify the user intension of activity and assist them to perform the same task. The proposed work makes use of 3D prosthetic hand to replace the inactive hands of the patients. The prosthetic hands are synthetic extensions that are used to support or supplement the affected or disabled parts. The Electromyography sensors installed on this prosthetic hand produces the biomedical signal that records muscle contractions. These sensors are capable of detecting muscle movements and high variations in real time. The proposed work makes use of Electromyography (EMG) signals to build an assist system and identify the intended activity. These enables patients with hand paralyzed to perform the inactive hand functions.