Design of Fuzzy Logic Motion Detection Algorithm for the Bracelet Type Sensor Consisting of Conductive Layer-Polymer Composite Film

• Published in: Polymers Vol. 14; no. 12; p. 2309
• Main Author: Park, Kiwon
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 07.06.2022; MDPI
• Subjects: Algorithms; Carbon; Composite materials; Conducting polymers; Electrodes; Forearm; Fuzzy logic; Human mechanics; Internet of Things; Jewelry; Modules; Motion perception; Muscle contraction; Muscles; Muscular function; Polyethylene terephthalate; Polymer films; Polymer matrix composites; Polymers; Sensors; Signal processing; Strain gauges; Surface resistance; Wearable computers; sensor fabrication; sensor mechanism; carbon-based conductive layer-polymer composite film; bracelet type sensor; fuzzy logic motion detection algorithm
• ISSN: 2073-4360; 2073-4360
• DOI: 10.3390/polym14122309

To improve the motion detection performance of a bracelet-type sensor that uses only two tiny sensor modules developed using carbon-based conductive polymer composite films, a fuzzy-logic algorithm was developed in this study. A polyethylene terephthalate polymer film with a conductive layer composed of carbon paste was used as the integral material utilized for the composite film; a small sensor module composed of mechanical parts mounted on the film was developed to effectively detect the surface resistance variations of the film. A participant wore a bracelet sensor, which consisted of two sensor modules, on their forearm, and the resistance variations of the contact area between the forearm and the sensor modules corresponding to the flexion changes of the surface of the body due to muscle contraction and relaxation were detected. The surface resistance variations of the film were converted to voltage signals, which were used as inputs to the fuzzy logic algorithm to detect four consecutive motions of the forearm. The results demonstrated that the fuzzy-logic algorithm attained an accuracy of 94%. The fuzzy algorithm successfully detected four motions and the resting state of the forearms; moreover, it showed improved performance compared to previous research.