A tensor-based method for completion of missing electromyography data

Muhammad Akmal, Syed Zubair, Mads Jochumsen, Ernest Nlandu Kamavuako, Imran Khan Niazi
2019 IEEE Access  
This paper discusses the recovery of missing data in surface Electromyography (sEMG) signals that arise during the acquisition process. Missing values in EMG signals occur due to either disconnection of electrodes, artifacts, muscle fatigue or incapability of instruments to collect very low amplitude signals. In many real-world EMG related applications, algorithms need complete data to make accurate and correct predictions, or otherwise, the performance of prediction reduces sharply. We employ
more » ... sharply. We employ tensor factorization methods to recover unstructured and structured missing data from EMG signals. In this paper, we use firstorder weighted optimization (WOPT) of PARAFAC decomposition model to recover missing data. We tested our proposed framework against Non-Negative Matrix Factorization (NMF) and Parallel Factor Analysis (PARAFAC) on simulated as well as on offline EMG signals having unstructured missing values (randomly missing data ranging from 60% to 95%) and structured missing values. In the case of structured missing data having different channels, the percentage of missing data of a channel goes up to 50% for different movements. It has been observed empirically that our proposed framework recovers the missing data with relatively much improved accuracy in terms of Relative Mean Error (up to 50% and 30 % for unstructured and structured missing data respectively) as compared to matrix factorization methods even when the portion of unstructured and structured missing data reaches up to 95% and 50%, respectively.
doi:10.1109/access.2019.2931371 fatcat:qeqnpkk76zguhnbwxvg5m6mnmu