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Article type: Research Article
Authors: Akmal, Muhammada; * | Zubair, Syedb | Jochumsen, Madsc | Zia ur rehman, Muhammadd | Nlandu Kamavuako, Erneste | Irfan Abid, Muhammadh | Niazi, Imran Khanc; f; g
Affiliations: [a] Department of Electrical Engineering, Riphah International University, I-14 Islamabad, Pakistan | [b] Deparment of Computer Science, University of Sialkot, Sialkot, Pakistan | [c] Department of Health Science and Technology, SMI, Aalborg university, Aalborg, Denmark | [d] Department of Biomedical Engineering, Riphah International University, I-14 Islamabad, Pakistan | [e] Department of Engineering, Centre for Robotics Research, King’s College London, London, UK | [f] Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland, New Zealand | [g] Health and Rehabilitation Research Institute, AUT University, Auckland, New Zealand | [h] Department of Electrical Engineering, Riphah International University, Faisalabad, Pakistan
Correspondence: [*] Corresponding author. Muhammad Akmal, Department of Electrical Engineering, Riphah International University, I-14 Islamabad, Pakistan. E-mail: muhammad.akmal@riphah.edu.pk.
Abstract: To design a prosthetic hand which can classify movements based on the electromyography (EMG) signals, complete and good quality signals are essential. However, due to different reasons such as disconnection of electrodes or muscles fatigue the recorded EMG data can be incomplete, which degrades the classification of test movements. In this paper, we first acquire multiday intramuscular EMG (iEMG) signals (which are invasive) with higher Signal-to-Noise Ratio (SNR) compared to surface EMG (sEMG) signals; followed by application of matrix (non-negative matrix factorization – NMF) and tensor factorization methods (Canonical Polyadic Decomposition (CPD), Tucker decomposition (TD) & Canonical Polyadic-Weighted Optimization (CP-WOPT)) for recovering structured missing data i.e., chunks of missing samples in channels. Furthermore, we tested the scalability of NMF, CPD, TD and CP-WOPT by employing them on the large multiday (seven days) iEMG data where the size of missing data is increased from day 1 to day 7, and for each day a fixed percentage of missing data is introduced from 10% to worst case of 50%. Results show that CP-WOPT outperformed NMF, CPD and TD to recover large percentage of missing data in terms of Relative Mean Error (RME) even when 7 days of data is considered. CP-WOPT showed robustness even for the worse case even when 50% iEMG data is removed from day 1 to day 7 where it’s RME degraded slightly from 0.08 to 0.1.
Keywords: Multiday intramuscular EMG, missing data, tensor factorization
DOI: 10.3233/JIFS-212715
Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1177-1187, 2022
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