TCC Sistemas de Informação
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Navegando TCC Sistemas de Informação por Orientador "Medeiros, Leonardo Melo de"
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Item Análise do monitoramento dos sinais motores da doença de Parkinson: uma revisão sistemática(Instituto Federal de Educação Ciência e Tecnologia de Alagoas, 2023-05-31) Lima, Mayara Rysia de Assis; Medeiros, Leonardo Melo de; http://lattes.cnpq.br/1080593968001453; Costa, Breno Jacinto Duarte da; http://lattes.cnpq.br/7418697922506495; Passos, Frederico Salgueiro; http://lattes.cnpq.br/6590059682506348Parkinson’s Disease (PD) is a chronic degenerative disease that causes a motor disorder affecting the individual’s routine tasks and destabilizing their quality of life. The most notable motor signs make up a tetrad: rigidity, bradykinesia, postural and gait changes, and resting tremor. Thus, the medical examination requires an assessment of motor movements, emphasizing the speed, amplitude and rhythm of each movement, in order to monitor the symptoms and improve the clinical diagnosis. In order to distinguish improvements or evolutions of PD, motor assessment methods become increasingly indispensable, and in the literature, systems or mechanisms for quantifying these symptoms are found, the most recurrent being for the signs of bradykinesia, aided by the most well-known skill test, the Finger-Tapping Test (FTT). However, none of them is yet able to prove a definitive diagnosis, therefore relying on the interpretation of a neurologist. This research presents a Systematic Literature Review (SLR) with academic artifacts produced, which demonstrate relevant data on motor movements and their quantification, using mostly the MDS UPDRS part III scale as a guide, in order to extract a set of requirements to be developed in a system for monitoring the progress of Parkinson’s motor signs, using the finger tapping test (FTT) as a technique. As a result, the RSL aimed to identify the most current techniques and approaches that quantify motor assessments, in addition to clarifying whether these methods demonstrate positive interference in the assessment process with the FTT technique.Item Quantificação do sinal do tremor parkinsoniano em exames manuscritos em espiral utilizando Machine Learning(Instituto Federal de Educação Ciência e Tecnologia de Alagoas, 2023-10-27) Correia, Igor Matheus Barros; Medeiros, Leonardo Melo de; http://lattes.cnpq.br/1080593968001453; Costa, Breno Jacinto Duarte da; http://lattes.cnpq.br/7418697922506495; Costa, Alex Emanuel Barros; http://lattes.cnpq.br/2231272728491909Parkinson’s disease (PD) is a progressive neurological disease that develops gradually and causes motor symptoms, such as tremors, slow movement (bradykinesia) and postural imbalance. PD affects millions of people worldwide, especially the elderly population. Data from the World Health Organization (WHO) shows that approximately 1% of the world’s population over the age of 65 has the disease. Diagnosing PD is difficult and misdiagnosis is common, especially in the early stages. Micrography is a technique commonly used in the diagnosis of Parkinson’s disease and essentially consists of performing handwritten examinations. In this context, Machine Learning (ML) emerges as a powerful tool to apply data classification techniques that can identify patterns and assist in the detection of PD. This study uses two Machine Learning models, namely Support Vector Machine (SVM) and Random Forest Classifier (RFC), with the aim of classifying handwritten exams and contributing to improving the accuracy and effectiveness in diagnosing PD. The results show that both models achieved promising performance, with high accuracy rates, reaching 86% for the RFC and 82% for the SVM. These results suggest that the application of Machine Learning to classify handwritten exams can be a valuable tool in diagnosing PD.