Paolo Tasca
A machine learning approach for spatio-temporal gait analysis based on a head-mounted inertial sensor.
Rel. Andrea Cereatti, Gabriella Balestra, Samanta Rosati, Francesca Salis. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2022
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Abstract
Gait is fundamental for the person’s mobility, as it is crucial for many activities in the workplace, domestic environment, and social life. In the last decades, several studies proved the relevance of instrumented gait analysis for clinical and wellness applications based on quantitative metrics (e.g., spatial-temporal parameters of gait, kinematics) to provide deeper insights about individual walking ability, especially when analyzing gait in free-living conditions, where motor performances can be assessed. In this sense, magnetic-inertial measurement units (MIMU) represent the most convenient solution in terms of ease of use and affordability. The most used locations include trunk/lower back and wrist and have been widely explored.
Conversely, less attention has been given to other sites, such as the head, which offers the possibility of integrating the MIMU with a wide range of devices, such as VR visors or earbuds
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