Varshitha K, Praveen LS, Nagananda SN and Preetham S
Bionic arm is a robotic arm that offers many of human arm features such as hand grasp and release, flexionextension, elbow flexion-extension, supination-pronation etc. which is integrated with the nervous system and controlled by Electromyogram signals. Invasive and non-invasive methods are used to collect the EMG signal from amputees. In spite of difficulty caused by invasive methods, non-invasive methods are being opted in today's recent Bionic Arms. To overcome the some drawbacks of non-invasive methods proper classification algorithms has to be chosen for controlling individual finger movements in Bionic Arm. In this paper, initially various feature extraction; reduction and classification algorithms are implemented on EMG data of different subjects which is available from Ninapro database. From the results obtained, MAV algorithm for feature extraction, PCA algorithm for feature reduction and KNN algorithm for feature classification are chosen since they gave more accuracy compared to others after implementing on EMG data of different subjects. By employing this algorithms 95% accuracy is achieved for controlling individual finger movements in Bionic Arm. Response time between grasp and release actions of fingers in Bionic Arm obtained after implementing on processor is less than 1ms.
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