C
Cheikh Latyr Fall
Researcher at Laval University
Publications - 19
Citations - 936
Cheikh Latyr Fall is an academic researcher from Laval University. The author has contributed to research in topics: Wearable computer & Gesture recognition. The author has an hindex of 10, co-authored 18 publications receiving 579 citations. Previous affiliations of Cheikh Latyr Fall include École Polytechnique de Montréal.
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Journal ArticleDOI
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning
Ulysse Côté-Allard,Cheikh Latyr Fall,Alexandre Drouin,Alexandre Campeau-Lecours,Clément Gosselin,Kyrre Glette,François Laviolette,Benoit Gosselin +7 more
TL;DR: The proposed transfer learning scheme is shown to systematically and significantly enhance the performance for all three networks on the two datasets, achieving an offline accuracy of 98.31% and real-time feedback allows users to adapt their muscle activation strategy which reduces the degradation in accuracy normally experienced over time.
Proceedings ArticleDOI
Transfer learning for sEMG hand gestures recognition using convolutional neural networks
Ulysse Côté-Allard,Cheikh Latyr Fall,Alexandre Campeau-Lecours,Clément Gosselin,François Laviolette,Benoit Gosselin +5 more
TL;DR: The results show that the proposed classifier is robust and precise enough to guide a 6DoF robotic arm (in conjunction with orientation data) with the same speed and precision as with a joystick.
Proceedings ArticleDOI
A convolutional neural network for robotic arm guidance using sEMG based frequency-features
Ulysse Cote Allard,François Nougarou,Cheikh Latyr Fall,Philippe Giguère,Clément Gosselin,François Laviolette,Benoit Gosselin +6 more
TL;DR: This paper demonstrates their viability to the problem of gesture recognition for a low-cost, low-sampling rate (200Hz) consumer-grade, 8-channel, dry electrodes sEMG device called Myo armband on able-bodied subjects and assesses the robustness of this machine learning oriented approach.
Journal ArticleDOI
A Wireless Respiratory Monitoring System Using a Wearable Patch Sensor Network
Tamer Elfaramawy,Cheikh Latyr Fall,Soodeh Arab,Martin Morissette,François Lellouche,Benoit Gosselin +5 more
TL;DR: A low-power wireless respiratory monitoring system with cough detection is proposed to measure the breathing rate and the frequency of coughing using wearable wireless multimodal patch sensors, designed using off-the-shelf components.
Proceedings ArticleDOI
Intuitive wireless control of a robotic arm for people living with an upper body disability
Cheikh Latyr Fall,Philippe Turgeon,Alexandre Campeau-Lecours,Véronique Maheu,Mounir Boukadoum,Sebastien Roy,Daniel Massicotte,Clément Gosselin,Benoit Gosselin +8 more
TL;DR: The design of a highly intuitive wireless controller for people living with upper body disabilities with a residual or complete control of their neck and their shoulders is described.