EMG finger movement classification based on ANFIS

• Main Authors: Caesarendra, Wahyu, Tjahjowidodo, Tegoeh, Nico, Yohanes, Wahyudati, S., Nurhasanah, L.
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2018
• Subjects: EMG Signal; Finger Movement; DRNTU::Engineering::Mechanical engineering
• Online Access: https://hdl.handle.net/10356/88131; http://hdl.handle.net/10220/45630
• Institution: Nanyang Technological University

An increase number of people suffering from stroke has impact to the rapid development of finger hand exoskeleton to enable an automatic physical therapy. Prior to the development of finger exoskeleton, a research topic yet important i.e. machine learning of finger gestures classification is conducted. This paper presents a study on EMG signal classification of 5 finger gestures as a preliminary study toward the finger exoskeleton design and development in Indonesia. The EMG signals of 5 finger gestures were acquired using Myo EMG sensor. The EMG signal features were extracted and reduced using PCA. The ANFIS based learning is used to classify reduced features of 5 finger gestures. The result shows that the classification of finger gestures is less than the classification of 7 hand gestures.