Myoelectric Signal Based Multiple Grip Pattern Prosthetic Arm

Abstract

In today’s time there are a lot of skilled people who cannot work due to the loss of limb. These people are known as arm amputees. They need help of someone even to perform their mundane task. In other words they are capable to perform a task yet not self-sufficient due to their disability. This paper aims to provide a feasible solution to the amputee patients. Even though the patient has lost his/her limb, the muscles are still functioning near the amputated part. This projects focuses on the electromyography signals generated by the user near the amputated part. These ultra low level signals are picked up by the EMG sensor, the noise is filtered and important features are extracted out of it. A fuzzy logic system is created for various grip patterns on the LabVIEW software using the extracted features. The Arduino controller is then instructed to actuate the servo motors as per the grip pattern output from the LabVIEW. The servo motors in turn is used to actuate the fingers of the prosthetic arm via fishing lines as tendons. The prosthetic arm is 3D printed using ABS plastic to reduce the weight of the overall system and to reduce the cost factor. In this way different gripping patterns is controlled on the prosthetic arm based on the features extracted. The versatility of this project lies in distinguishing the EMG signals depending on the intensity of the signal, thus performing various gripping patterns on the prosthetic arm