Uluslararası Mühendislik Bilimleri ve Multidisipliner Yaklaşımlar Kongresi, İstanbul, Türkiye, 23 - 24 Şubat 2021
Today electromyogram (EMG) and accelerometer (MEMS) based signals can be used in the clinical diagnosis of physical states of muscle activities such as fatigue, pain, and tremors. These systems are exoskeletal systems used in rehabilitation areas as well as external or wearable robotic systems based on human movement. During the recording of these signals taken from the skin through non-invasive processes, integrity of meaningful signals deteriorates due to the electrodes attached to the skin not fully contacting, involuntary body movements, and noises from peripheral muscles. In addition, these noises can change from person to person when environmental factors and sweating or dryness conditions on the skin surfaces of people are considered. In consideration of all these negative factors, a new method based on Extended Kalman Filtering (EKF) model in effectively filtering the signals obtained from the muscles based on both EMG and MEMS is proposed in this study. This model can be used in all skeletal muscles of individuals of all age groups. For this reason, in this study teeth clenching and random chewing movements in the lower jaw muscles were examined experimentally by considering the fact that the movable skin surface tissue attached to the lower jaw and the head are not fixed.