Dr. Philippe Dixon
Title:
Assistant Professor
Department:
Kinesiology & Physical Education
Professional activities:
- Adjunct professor School of Kinesiology and Physical Activity Sciences (University of Montreal)
- Adjunct professor Department of Kinesiology (University of Laval)
Area(s):
Biomechanics, Neuroscience & Physiology
Areas of expertise:
- Biomechanics
- Motion capture
- Gait analysis
- Machine learning
- Wearable sensors
- Running
Biography:
Philippe Dixon is an assistant professor at McGill University. His research focuses on the analysis of human movement biomechanics, such as running, using motion capture systems and wearable devices, with the aim of improving the health and mobility of patients and athletes. He is also very active in the application of machine learning techniques for the detection and prediction of physiological events and states, and in the development of software for data analysis. Previously, Dr. Dixon worked as an assistant professor at the University of Montreal and as a biomedical data and artificial intelligence researcher at Hexoskin, a smart-garment start-up.
Link(s):
Degree(s):
- Post-doctoral fellowship, Public Health, Harvard University, USA
- Doctor of philosophy, Engineering Science, University of Oxford, UK
- Master of Science, Biomechanics, McGill University, Canada
- Bachelor of Education, Mathematics and Physics, McGill University, Canada
- Bachelor of Science, Physics, McGill University, Canada
Awards, honours, and fellowships:
- 2022-2027, Discovery grant, NSERC, Canada
- 2022, AUDACE grant, FRQSC, Canada
Selected publications:
- Ippersiel P, Dussault-Picard C, Mohammadyari SG, De Carvalho G, Chandran VD, Pal S, Dixon PC. Muscle coactivation during gait in children with and without cerebral palsy. Gait & Posture. 2024 Feb;108:110–6.
- Dussault-Picard C, Cherni Y, Ferron A, Robert MT, Dixon PC. The effect of uneven surfaces on inter-joint coordination during walking in children with cerebral palsy. Sci Rep. 2023 Dec 8;13(1):21779.
- Lam G, Rish I, Dixon PC. Estimating individual minimum calibration for deep-learning with predictive performance recovery: An example case of gait surface classification from wearable sensor gait data. Journal of Biomechanics. 2023 Apr;111606.
- Shah V, Flood MW, Grimm B, Dixon PC. Generalizability of deep learning models for predicting outdoor irregular walking surfaces. Journal of Biomechanics. 2022 Jun;139:111159.
- Luo Y, Coppola SM, Dixon PC, Li S, Dennerlein JT, Hu B. A database of human gait performance on irregular and uneven surfaces collected by wearable sensors. Scientific Data. 2020 Jul 8;7(1):1–9.
- Dixon PC, Schütte KH, Vanwanseele B, Jacobs JV, Dennerlein JT, Schiffman JM, et al. Machine learning algorithms can classify outdoor terrain types during running using accelerometry data. Gait & Posture. 2019 Oct;74:176–81.
Graduate supervision:
Taking new students during the upcoming application period.
