Mathieu Lempereur
Artificial intelligence for clinical decision-making.

Artificial intelligence (AI) has recently emerged as a promising tool in Quantified Gait Analysis (QGA). Deep learning methods now enable the classification of gait patterns, automatic detection of locomotor anomalies, and prediction of patients’ functional progression. In this presentation, we will explore the main advances related to the integration of AI in QGA. After introducing the fundamental principles of these methods, we will discuss their practical applications, current limitations, and the perspectives they offer for more precise and personalized evaluation of locomotor disorders.

Mathieu Lempereur is a hospital engineer in the Physical Medicine and Rehabilitation department at Brest University Hospital. He is head of the movement analysis laboratory and an associate researcher at the INSERM U1101 Laboratory for Medical Information Processing (LaTIM). He coordinates the “Technologies for Rehabilitation” axis of the IMAGINE team at LaTIM. His clinical work focuses on the acquisition and computational processing of gait in children with cerebral palsy and adults with stroke. His research activities involve the use of artificial intelligence in QGA for children and adults with motor disorders, aiming at automatic detection of gait events, diagnostic support, and gait prediction.