On the Assessment of Gray Code Kernels for Motion Characterization in People with Multiple Sclerosis: A Preliminary Study

Motor deficits in the lower limbs are common in people with multiple sclerosis (MS), impacting mobility and quality of life. Objective and quantitative metrics are crucial for effective identification and monitoring of motor deficits. Recent advancements in computer vision and human pose estimators allow for automatic extraction of movement information from video data, offering potential insights into human motion patterns. This exploratory study investigates the use of Gray-Code Kernels (GCKs) in characterizing gait patterns in individuals with advanced-stage MS compared to age- and sex-matched unimpaired controls. The preliminary results obtained demonstrate the promising potential of combining GCKs and pose estimators in characterizing gait patterns, warranting further investigation in this area.