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Increased Fall Risk Evaluation in the Elderly: A Video-based Approach for Gait Analysis

datacite.subject.fosDepartamento de Físicapt_PT
dc.contributor.advisorSoares, José Luís Ducla, 1949-
dc.contributor.advisorMatela, Nuno Miguel de Pinto Lobo e, 1978-
dc.contributor.authorPonte, Sara Viveiros
dc.date.accessioned2022-07-21T12:30:43Z
dc.date.available2022-07-21T12:30:43Z
dc.date.issued2022
dc.date.submitted2021
dc.descriptionTese de mestrado integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica) Universidade de Lisboa, Faculdade de Ciências, 2022pt_PT
dc.description.abstractWorldwide, falls are a major public health problem. Their risk assessment occurs predominantly in clinical environments, having a great dependency on the healthcare professionals who perform the evalu ation. Hence, the aim of this thesis was to develop a quantitative and objective approach to assess fall risk through two-dimensional (2D) video gait analysis. Data was acquired from two groups with antagonistic risk evaluations, a control group and an elderly group with increased fall risk, during two separate activ ities: walking and a standard Time Up and Go (TUG) test. The video sequences acquired were further pre-processed with the purpose of obtaining human skeletons for each frame of the video, followed by the computation of gait and time parameters for both activities. The estimated time parameters by the developed algorithm for the walking activity were compared with those estimated by the gait analysis Contemplas software, and the mean difference measured among both systems ranged between 0s and 0.02s. In addition, automatically extracted TUG time parameters were validated with those manually extracted for every TUG test video, and they proved to be strongly correlated (rho > 0.8). The same parameters were also in agreement with a study from the literature with similar characteristics, in which the TUG phases duration estimated were within the same range of the study. Furthermore, a Wilcoxon Rank Sum test was conducted to determine how well a given parameter could distinguish between young and elderly groups, and results showed a total of 15 out of 22 discriminant parameters (ρ-value < 0.05), with a greater representation of TUG time parameters (9/15). Apart from video capture, personal in formation such as age, sex, weight, height, history of falls, use of walking aid and presence of motor diseases were also acquired, and a Pearson correlation was computed between this supplementary data and the fall risk binary evaluation for each subject. This correlation revealed a strong linear relationship between age and increased fall risk, corroborated with a coefficient of 0.98. Results demonstrated that the developed algorithm was able to detect differences between people who had or not risk of falling, providing promising fall risk indicators that could contribute to a more objective and detailed evaluation of fall risk.pt_PT
dc.identifier.tid203217780pt_PT
dc.identifier.urihttp://hdl.handle.net/10451/53903
dc.language.isoengpt_PT
dc.subjectavaliação de risco de quedapt_PT
dc.subjectanálise de marcha em vídeopt_PT
dc.subjectesqueletos em 2Dpt_PT
dc.subjectidosospt_PT
dc.subjectteste TUGpt_PT
dc.subjectTeses de mestrado - 2022pt_PT
dc.titleIncreased Fall Risk Evaluation in the Elderly: A Video-based Approach for Gait Analysispt_PT
dc.typemaster thesis
dspace.entity.typePublication
rcaap.rightsopenAccesspt_PT
rcaap.typemasterThesispt_PT
thesis.degree.nameTese de mestrado integrado em Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica)pt_PT

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