Divulgação - Defesa Nº 238
Aluna: Rebeca Sarai Aguiar da Silva
Título: "Explainable feature extraction to support the evaluation of dual-task gait protocols."
Orientador: Fernando Buarque de Lima Neto (PPGEC)
Corientador: Rafael Rego Caldas (UPE)
Examinador Externo : Tsang Ing Ren - Doutor - (UFPE)
Examinador Interno : Bruno José Torres Fernandes (PPGEC)
Data-hora: 29/Julho/2021 (16:30h) - PM
Local: Formato Remoto (meet.google.com/iia-mmhf-rxj)
Resumo:
Gait patterns have emerged as a window into brain function in the early stages of cognitive decline. However, in clinical practice, the most common evaluation methods are still the universal goniometer and the observational gait analysis. The success of the analysis is highly dependent on the professional’s experience and background, resulting in a subjective and often inaccurate evaluation process. Such lack of accuracy is mainly caused by the difficulty to perceive atypical variants in the early stages of diseases. This work presents a set of qualitative and quantitative methods to aid the analysis of gait movement in older adults. Using a secondary database of a dual-task protocol assessment clinical trial, we attempt to help health professionals to make more informed and data-driven decisions based on the individual condition of each patient. On the quantitative front, we carried a classification benchmark to clarify the significance of priority on dual-task exercises. Our extensive experiments highlighted that different protocols of dual-task exercises have undetectable impacts on the development of community-dwelling older adults. However, our best results were driven by one specific type of dual-task exercise, thus we were able to demonstrate the significance of dual-task exercises with variable priority for the classification of other types of dysfunctions such as falls. On the qualitative front, we were able to create new semantic groups highlighting irrelevant, leading, synchronic and stagnant features. Results point out that a small group of features produce significant changes during the course of the clinical trial, similarly, a big group of features is considered irrelevant and therefore can be disregarded by health professionals on evaluation scenarios. The contributions described in this dissertation demonstrate that it is possible to include machine learning algorithms on the arsenal of tools of health professionals to indicate points that require close attention. Our work brings visibility to areas that were out of the spectrum of health professionals. Taken together, we believe that these methods help pave the way for the successful application of advanced machine learning techniques to support a wide range of health professionals in their clinical practice.
O Programa
