Gökçesu, K.Ergeneci, M.Ertan, E.Alkilani, Abdallah ZaidKosmas, P.2019-02-212019-02-212018-101550-4816http://hdl.handle.net/11693/50331Date of Conference: 08-12 October, 2018Conference name: ISWC '18 Proceedings of the 2018 ACM International Symposium on Wearable ComputersMuscle fatigue detection and tracking has gained significant attention as the sports science and rehabilitation technologies developed. It is known that muscle fatigue can be evaluated through surface Electromyography (sEMG) sensors, which are portable, non-invasive and applicable for real-time systems. There are plenty of fatigue tracking algorithms, many of which uses frequency, time and time-frequency behaviors of sEMG signals. An example to most commonly used sEMG-based fatigue detection methods can be mean frequency (MNF), median frequency (MDF), zero-crossing rate (ZCR) and continuous wavelet transform (CWT). However, all of these muscle fatigue calculation methods are adversely affected by the dynamically changing sEMG contraction amplitude, since EMG spectrum also demonstrates a shift with the changing signal RMS; powerful contractions lead a shift to high frequency bounds and the opposite happens for the weak. To overcome that, we propose an adaptive algorithm, which learns the effect of contraction power on sEMG power spectral density (PSD) and subtracts that amount of frequency shift from the PSD. Copyright c 2018 ACM.EnglishMuscle Fatigue TrackingSEMG Signal ProcessingSpectral AnalysisWearable ComputingConference Paper10.1145/3267242.3267292