Publications

* indicates equal contribution.

Comprehensive Review of Feature Extraction Techniques for sEMG Signal Classification: From Handcrafted Features to Deep Learning Approaches
This survey aims to provide a comprehensive overview of feature extraction techniques for sEMG signal classification ranging from the handcrafted to the learned features.
Elsevier-IRBM
Paper
A Comprehensive Review of sEMG-IMU Sensor Fusion for Upper Limb Movements Pattern Recognition
This review provides a comprehensive analysis of sEMG-IMU sensor fusion techniques for upper limb movement pattern recognition. It offers detailed insights into the signal generation mechanisms of both surface electromyography (sEMG) and inertial measurement units (IMU), and critically explores multisensory fusion strategies aimed at enhancing recognition accuracy and reliability.
Information Fusion
Paper
Spectral Selective Canonical Correlation Analysis to Remove the Power Line Interference from HD-sEMG Signals
In this study, we propose a new variant of the CCA denoising technique to more effectively identify and eliminate all harmonics of the PLI while preserving the HD-sEMG signal originating from other frequencies.
EUSIPCO 2024
Paper
On exploring age difference using HD-sEMG signals during STS exercise
This study investigates the impact of aging on HDsEMG signals to enhance understanding and management of muscle aging. It aims to demonstrate the potential of HD-sEMG technology in identifying and mitigating the effects of muscle aging during a Sit To Stand (STS) exercise.
49th Congress of the Society of Biomechanics, 2024
Paper
Active aging prediction from muscle electrical activity using HD-sEMG signals and machine learning
In this study, using High-Density Surface Electromyography (HD-sEMG) signals and extracted features, we propose a Machine Learning architecture for predicting active aging through motor functional age (MFA) estimation.
International Symposium on Computer-Based Medical Systems (CBMS) 2023
Paper
Time-domain features for sEMG signal classification: A brief survey
A short rewiew on the used time-domain features for sEMG signal classification in different applications.
JETSAN 2023
Paper

Teaching

Statistics and Data Analysis (SY02)

Graduate Level Course
Lab sessions with R

Microcomputers and applications (NF22)

Graduate Level Course

Service

Journal Reviewer

Multimedia Tools and Applications, Bioengineering & Translational Medicine, PeerJ Computer Science, Computers and Electrical Engineering

Conference Reviewer

IJCNN, EMBC, RTIP2R

Contact

Please feel free to reach to me for any query or interesting collaborations.

  • sidimohamed [dot] sidelmoctar [at] univ-rennes [dot] fr