TCC Sistemas de Informação
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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 Uma análise dos desafios organizacionais e técnicos na adequação da operação de um hospital particular à lei geral de proteção de dados(2025-06-03) Moura , Ícaro Rodolpho de Farias; Bezerra, Tarcio Rodrigues; http://lattes.cnpq.br/5285201763618981; Cunha, Mônica Ximenes Carneiro da; http://lattes.cnpq.br/1775024859845111; Nunes Filho, Ricardo Rubens Gomes; http://lattes.cnpq.br/1760182180822152The Brazilian General Data Protection Law (LGPD) presents significant challenges for healthcare institutions, especially regarding the handling of patients' sensitive data. This study investigates the compliance process of a large private hospital with the LGPD, focusing on information systems and information security practices to identify the main technical and organizational challenges encountered. The research adopts the Goal-Question-Metric (GQM) model, which enabled a structured analysis based on defined objectives, investigative questions, and measurable metrics. The results reveal gaps in areas such as security infrastructure, access management, contract updates, and staff training, indicating weaknesses in data governance and the implementation of internal controls. Based on the evidence gathered through document analysis and validation with institutional professionals, this study proposes recommendations to enhance regulatory compliance and strengthen the protection of personal data in hospital environments.