Gait pattern classification in walking with crutches through spatio-temporal parameters
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tfm-miguelsalazar-final.pdf (12,08Mb) (Accés restringit)
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/372849
Tipus de documentProjecte Final de Màster Oficial
Data2022-07-18
Condicions d'accésAccés restringit per decisió de l'autor
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Abstract
The monitoring of biomechanical data during the crutch gait is essential when assessing the progression of the patient during rehabilitation. However, the complexity of this data may hidden embedded patterns on the user gait that could provide additional information about their health condition, and may affect the evolution of the patient. During the last years, research focusing on the implementation of ML techniques for biomechanical analysis have been gaining relevance, due to their capacity for identifying gait disorders, detecting specific gait events or classifying user activity. The main objective of this project, therefore, is to classify 4 different gait patterns through the extraction of relevant spatio-temporal parameters applying ML techniques. To achieve this objective, gait data were collected from a set of instrumented crutches, which allowed to record the trials of 27 healthy participants, along a 10 meter corridor. The collected gait signals were processed and segmented in cycles, by the identification of swing and stance phases using the pitch angle signal of the crutch. The final dataset obtained after this segmentation was composed by a total of 74 extracted features and 6014 samples. The acquisition of this dataset allowed to build a ML classifier to accurately identify the different gait patterns. In order to reduce the data dimensionality, a feature selection process is performed, comparing the most relevant methods applied in literature, and demonstrating the importance of forward acceleration, pitch and force signals for crutch gait identification. The reduced dataset was evaluated against different classifiers through a hyperparameter optimization process, resulting in a SVM classifier able to identify the crutch patterns with a 93.85% of accuracy. Additionally, a solution to reject samples not belonging to one of the expected gait patterns is presented
MatèriesGait disorders -- Classification -- Mathematical models, Pattern recognition systems -- Diagnostic use, Artificial intelligence -- Medical applications -- Software, Trastorns de la marxa -- Classificació -- Models matemàtics, Reconeixement de formes (Informàtica) -- Ús diagnòstic, Intel·ligència artificial -- Aplicacions a la medicina, Intel·ligència artificial -- Aplicacions a la medicina -- Programari
Col·leccions
Fitxers | Descripció | Mida | Format | Visualitza |
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tfm-miguelsalazar-final.pdf | 12,08Mb | Accés restringit |