Real-Time Classification of Hand Movements from Surface Electromyography Signals Using Feature Engineering and an Ensemble of Machine-Learning Classifiers for Rehabilitation

Turimov Dilmurod; Kilichev Dusmurod; Saydirasulov Norkobil; Mukhamadiyev Abdinabi

doi:10.65164/46rx7x24

Mualliflar

Turimov Dilmurod Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Gyeonggi-do, Republic of Korea, Department of Control Theory and Information Security, Samarkand State University named after Sharof Rashidov, Samarkand 140104, Uzbekistan ##default.groups.name.author##
Kilichev Dusmurod Department of Information Security, National University of Uzbekistan, Tashkent 100174, Uzbekistan ##default.groups.name.author##
Saydirasulov Norkobil Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Gyeonggi-do, Republic of Korea, Department of Technologies of Mobile Communication, Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi, Tashkent 100200, Uzbekistan ##default.groups.name.author##
Mukhamadiyev Abdinabi Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Gyeonggi-do, Republic of Korea ##default.groups.name.author##

DOI:

https://doi.org/10.65164/46rx7x24

Kalit so‘zlar:

surface electromyography; hand-movement classification; feature engineering; machine learning; ensemble classifier; real-time; myoelectric prosthesis; rehabilitation; spinal cord injury.

Abstrak

Myoelectric control of prostheses and biosignal-driven rehabilitation require handmovement recognition that is simultaneously accurate, computationally light, and fast enough for
real-time operation on embedded hardware. This paper presents an end-to-end methodology for
classifying object-interaction hand movements from surface electromyography (sEMG) signals.
Four contributions are combined into a single deployable pipeline: (i) a real-time active-potential
onset/offset detection algorithm based on rectified-signal variance thresholding (threshold = μ +
hσ); (ii) an optimal, low-dimensional feature vector obtained through feature engineering in the
time, frequency and time-frequency domains; (iii) a real-time algorithm for automatically building
the training set during signal acquisition; and (iv) an ensemble classifier that fuses k-Nearest
Neighbours, Support Vector Machine and Random Forest decisions with a confidence gate and
temporal confirmation. Two datasets were recorded - a single-channel BITalino dataset (20
subjects, 4 movements) and a five-channel BTS FreeEMG dataset (42 subjects, 7 movements). The
proposed onset detector achieved the lowest mean error (15.45 samples) among six competing
methods.