Ulysse Côté-Allard received the integrated B.A. degree in mathematics and informatics in 2014 from Laval University, Quebec City, QC, Canada, where he is currently working toward the Ph.D. degree in electrical engineering with the Biomedical Microsystems Laboratory and the Groupe de Recherche en Apprentissage Automatique de l’Universite Laval (GRAAL). ´ His main research interests include rehabilitation engineering, EMG-based pattern recognition, and human–robot learning. Mr. Côté-Allard is the recipient of the Best Paper ´ Award from the IEEE Systems, Man, and Cybernetics conference.
Project : Long-term guidance of hand prosthesis using electromyographic signals
My research is oriented towards the development of algorithms for the guidance of a prosthesis-hand that is resistant to short and long-term noise without the need for constant recalibration. This is in an effort to create new hand prostheses that are smarter and more easily integrated into day-to-day life, to ultimately improve the quality of life of amputees by giving them a wider array of option to interact with the world. In this endeavor, I am particularly interested in integrating convolutional networks with unsupervised transfer learning algorithms to generate robust features representations that adapt to the non-stationary nature of surface electromyography.