Andrea Crobu
An Optimisation Algorithm for enhancing precision in stride segmentation using Multi-Dimensional Subsequence Dynamic Time Warping on sensor data.
Rel. Marco Knaflitz. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2021
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Abstract
Gait analysis is the systemic study of human locomotion and plays an important role in detecting patterns in such activity. Inertial Measurement Units (IMUs), due to their very low consumption, can be battery powered and are promising tools for long-term ambulatory monitoring outside clinical facilities or laboratories; moreover, considering their low cost, inertial sensors have become particularly popular in the gait analysis research and development field. Stride segmentation, answers the specific clinical needs for analysing human gait. Stride segmentation is the procedure of dividing the gait into strides, where a stride begins with one ’heel strike’ (i.e when the heel makes contact with the ground) and ends with the heel strike of the following step.
The ability to automatically and robustly segment individual strides from gait sequences derived using inertial sensors during different gait activities is crucial for the estimation of gait parameters and for the creation of a reliable gait dataset, without requiring the manual segmentation of recordings
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