Avaliação do desempenho de classificadores na discriminação de indivíduos adultos e idosos saudáveis a partir da função manual

• Ano de defesa: 2023
• Autor(a) principal: Silva, Gabriella Lelis
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Engenharia Elétrica
• Palavras-chave em Português: Envelhecimento humano; Sensores inerciais; Classificadores; Human aging; Inertial sensors; Classifer; CNPQ::ENGENHARIAS; Engenharia elétrica; Envelhecimento - Aspectos genéticos; Marcadores genéticos; Detectores
• Link de acesso: https://repositorio.ufu.br/handle/123456789/38794; http://doi.org/10.14393/ufu.te.2023.7042

The identifcation of features that aid in the assessment of processes related to aging is an area of ever-increasing study. The use of biomarkers and tools that ofer greater specifcity and sensitivity to quantify and characterize motor tasks, aid in the understanding of biological changes that occur due to advancing age, and as such predefne phenotypes related to age or health outcomes. The proposal behind this study is thus to assess the performance of diferent classifers in the discrimination of both healthy individual adults and senior citizens from the characterization of functional ftness tasks, using inertial sensors. Method: Ninety-nine healthy participants were recruited, with ages ranging from 20 to 98 years old. The collection of data was performed by means of two inertial measurement units (IMUs), positioned in the region of the distal third of the forearm of the dominant hand and on the back of the dominant hand. The participants performed three successive tasks with the forearm fexed, (i) at rest, (ii) pulp to pulp pinch, and (iii) supination/pronation. Diferent features were extracted from the signals that were then used in the comparison of the values for specifcity, sensitivity, precision, and accuracy. The classifers random forest (RF), support vector machine (SVM), knearest neighbor (KNN), and naive Bayes (NB) were used to classify the groups. Results: Through use of the features extracted by the IMUs in the region of the distal third of the forearm of the dominant hand, and on the back of the dominant hand of the volunteers, the classifer that presented the best sensitivity was the SVM, when fed with 25% of the features, with a rate of 89.6%. The RF classifer was the one that obtained the best specifcity (72.8%), when fed with all the features. However, the NB obtained the best precision and accuracy (75.5% and 79.3% respectively), when fed with 60% of the features. Conclusion: The results obtained in this study demonstrate that the use of classifcation algorithms from machine learning in the discrimination of healthy adult and senior citizen groups, with high rates of sensitivity and specifcity, provide valuable information for clinical assessment concerning the prediction of motor changes related to advancing age regarding the characterization of motorrelated tasks.