TY - JOUR
T1 - Scalable tensor factorization for recovering multiday missing intramuscular electromyography data
AU - Akmal, Muhammad
AU - Zubair, Syed
AU - Jochumsen, Mads Rovsing
AU - Rehman, Muhammad Zia ur
AU - Kamavuako, Ernest Nlandu
AU - Irfan Abid, Muhammad
AU - Niazi, Imran Khan
PY - 2022/4/25
Y1 - 2022/4/25
N2 - 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.
AB - 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.
KW - Multiday intramuscular EMG
KW - missing data
KW - tensor factorization
U2 - 10.3233/JIFS-212715
DO - 10.3233/JIFS-212715
M3 - Journal article
SN - 1064-1246
VL - 43
SP - 1177
EP - 1187
JO - Journal of Intelligent and Fuzzy Systems
JF - Journal of Intelligent and Fuzzy Systems
IS - 1
ER -