Abstract
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.
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Acknowledgment
Funding information: This research was funded by: the European Union—NextGenerationEU within the framework of the project “RAISE—Robotics and AI for Socio-economic Empowerment”. However, the views and opinions expressed are those of the authors alone and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them; Fondazione Italiana Sclerosi Multipla (FISM-2019/PRsingle050).
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Moro, M., Cellerino, M., Inglese, M., Casadio, M., Odone, F., Noceti, N. (2024). On the Assessment of Gray Code Kernels for Motion Characterization in People with Multiple Sclerosis: A Preliminary Study. In: Bellotti, F., et al. Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2023. Lecture Notes in Electrical Engineering, vol 1110. Springer, Cham. https://doi.org/10.1007/978-3-031-48121-5_34
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DOI: https://doi.org/10.1007/978-3-031-48121-5_34
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