Paper Title
FUZZY C-MEANS CLUSTERING BASED MOVEMENT DETECTION WITH BRACELET TYPE SENSOR

Abstract
One important technology in wearable devices involves the detection of human movement intention, of which the most common method utilizes the body’s EMG signals. An EMG signal is an electrical stimulus transmitted from the brain to a muscle fiber to contract the muscle. Signal measurement methods include an invasive method in which a needle-shaped electrode is inserted into the peripheral nervous system of the body and a non-painful, non-invasive method in which an electrode is attached to the surface of the skin to measure the signal. In general, a non-invasive method that quantitatively analyzes the overall synergistic activities of a set of muscular movement units has been utilized. However, the sEMG signal measurement method has restrictions in real applications. This study presents a method for fabricating the bracelet type wearable sensor module using a carbon-based conductive layer-polymer composite film to detect the movement intention of human body. The integral material used for the composite film is PET (polyethylene terephthalate) polymer film with a conductive layer made using a carbon paste, capable of detecting the changes in the resistance corresponding to the flexion changes of the surface of body due to muscle contraction and relaxation. The variations of surface resistance were converted into voltage signals and used to detect movement intention of upper arm. The developed bracelet-type wearable sensor, incorporating the fuzzy c-means clustering algorithm, effectively detected the intention of forearm movement. Biography: Kiwon Park received his PhD degree in biomedical engineering from the South Dakota School of Mines and Technology, USA, in 2011. Since 2018, he has been an associate professor in the Smart Automobile Engineering Department, Youngsan University, Yangsan, Rep. of Korea. His research interests include sensors and devices in mechanical and biomedical engineering application.