Sidi Mohamed Sid'El Moctar

About Me

Hi! I am a Postdoctoral Researcher at the CNRS, working in the Institute Genetics & Development of Rennes. I recently completed my PhD at the University of Technology of Compiègne, where my research focused on Machine Learning and Deep Learning, exploring their applications in Health/Biomedical fields, with a particular interest in Biomedical Signal and Image Processing. Before joining UTC, I worked as a Data Analyst at the Centre Oscar Lambret in the Clinical Research and Innovation team.

I hold a MSc in Data Science, a BSc in Mathematics and Computer Science from the University of Lille, and a BSc in applied mathematics from the University of Nouakchott.

Interests

Machine Learning
Computer Vision
Biomedical Signal Processing
Biomedical Image Processing

Education

PhD in Computer Science, 2021-2025

Université de Technologie de Compiègne, France
Mediterranean Machine Learning Summer School, 2024

Università di Milano-Bicocca, Italy
MSc in Data Science, 2019-2021

Université de Lille, France
Bsc in Mathematics and Computer science, 2018-2019

Université de Lille, France
Bsc in Applied Mathematics, 2015-2018

Université de Nouakchott, Mauritania

Selected Publications

Full list in Google Scholar, * 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.

Sidi Mohamed Sid'El Moctar, Imad Rida, Sofiane Boudaoud

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.

Honglei Zhang, Sidi Mohamed Sid'El Moctar, Sofiane Boudaoud, Imad Rida

Information Fusion

Paper

iEMG-Based Diagnosis of ALS and Myopathy using 1D-CNN

In this study, we present a lightweight, high-performance 1D-CNN approach for multiclass classification of EMG signals—capable of accurately distinguishing ALS, myopathy, and healthy subjects with minimal preprocessing.

Sidi Mohamed Sid'El Moctar, Chawki Nasrallah, Imad Rida, Sofiane Boudaoud

EMBC 2025

Paper

Teaching

Statistics and Data Analysis (SY02)

Graduate Level Course, UTC
Lab sessions with R

Microcomputers and applications (NF22)

Graduate Level Course, UTC

Biological Image Analysis

Graduate Level Course, Univ. Rennes

Service

Journal Reviewer

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

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