Rosita Rabbito
Using deep learning-based pose estimation algorithms for markerless gait analysis in rehabilitation medicine.
Rel. Danilo Demarchi, Paolo Bonato. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2021
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Abstract
Walking is one of the most natural human activities and certainly the most impactful on one's quality of life. However, the human ability to walk can be compromised by neurological, orthopedic, or traumatological factors. When the gait is impaired by one or multiple of these factors, a key objective of modern rehabilitation interventions is to help people with gait impairments to regain or improve their ability to walk, minimizing the negative impact on their quality of life at both a social and personal level. Nowadays, gait analysis laboratories use multiple technologies to evaluate and improve the individual's gait patterns. Among the various tools available for gait analysis, Motion Capture systems based on cameras and infrared-reflective markers positioned on the individual are used as gold standard, due to their high accuracy.
However, these systems have the disadvantage of being cumbersome, costly, complex, and not time-efficient
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