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 de movimentos motores finos dos dedos utilizando visão computacional: um estudo de caso do Finger Tapping Test (FTT) na doença de Parkinson(2025-11-26) Campos, Herbert Douglas Silva da Silva; Nascimento, Arlyson Alves do; http://lattes.cnpq.br/9395417554768580; Medeiros, Leonardo Melo de; http://lattes.cnpq.br/1080593968001453; Passos, Frederico Salgueiro; Calado, Ivo Augusto; http://lattes.cnpq.br/5748220882915553Parkinson’s disease (PD) is characterized as a neurodegenerative and progressive disorder that affects a significant portion of the elderly population worldwide. The most common symptoms in PD include cognitive problems, which predominantly begin to appear in more advanced stages, unlike the motor symptoms that manifest at the onset of the disease. The Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) is one of the assessments that aid in diagnosing and monitoring the levels of Parkinson’s disease in patients. Within the MDS-UPDRS III, which assesses the motor aspects of patients, there is the Finger Tapping Test (FTT), used to assess bradykinesia and rigidity of movement. FTT is typically administered by a healthcare professional who observes the patient performing the test. However, the use of computer vision can provide a more effective alternative by accurately capturing the movement’s characteristics. The aim of this study was to develop a computer vision system capable of acquiring data from fine finger movements during the FTT and subsequently classify Parkinson’s disease levels according to the MDS-UPDRS. For this study, a computer vision algorithm was created to record data resulting from the abduction and adduction of hand movements during the FTT, calculating the amplitude generated by these movements. A dataset comprising 532 videos of both Parkinson’s and non-Parkinson’s patients, classified into four levels according to the MDS-UPDRS, was used. Subsequently, biomechanical signal processing procedures were conducted. The results obtained indicate that the use of computer vision in assisting healthcare professionals is promising. Data collection from movements allows for the creation of a database that can be used in machine learning models to predict the patient’s disease level based on the MDS-UPDRS. Additionally, a binary classifier was developed to determine the level at which each patient falls, using a Support Vector Machine (SVM) along with the leave-one-out cross-validation technique. This resulted in an accuracy of 73.3% when using the entire available dataset and 76.6% when using only the right-hand FTT.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.