Home e-Care: monitoramento de quedas em idosos através de smartwatches / Home e-Care: fall monitoring using smartwatches

Pessoas da terceira idade normalmente possuem a saúde mais frágil, elas ficam cada vez mais dependentes de ajuda na medida em que vão envelhecendo, principalmente na ocorrência de acidentes como quedas. Por esse motivo, torna-se necessário a existência de métodos automatizados de monitoramento e notificação da ocorrência de tais acidentes. OBJETIVO: Este trabalho apresenta uma metodologia para detecção automatizada de quedas em idosos, através do uso do acelerômetro presente em smartwatches com Android Wear e de detecção de quedas baseada em limiares. As ocorrências de queda são notificadas automaticamente para contatos de emergência. RESULTADOS: A aplicação criada alcançou resultados satisfatórios, obtendo 89,29% de especificidade, 75% de sensibilidade e 83,33% de acurácia. CONCLUSÃO: Concluiu-se que a abordagem adotada caracteriza uma alternativa robusta e viável para a detecção automatizada de quedas que contribui para a qualidade de vida para pessoas da terceira idade.

The elderly population is growing worldwide, and with that the concerns about their quality of life becomemore important. As the elderly normally have frail health, which tends to get worse as they age, they grow even more dependent on someone else, specifically in the event of an accident such as a fall. For that reason, an automated monitoring and notification mechanism for accidents is necessary. OBJECTIVE: This work aims to present an automated fall detection and notification methodology through the use of the android smartwatches embedded accelerometer andthreshold based algorithms. Notifications of falls will be sent to emergency contacts. RESULTS: The developed system reached satisfactory results, with 89,29% of specificity, 75% of recall and 83,33% of accuracy. CONCLUSION: We could observe that Home e-Care and the TBA are a robust and viable solution for automated fall detection, and can improve the elderly's quality of life.

Visualização imersiva e interação gestual com dados médicos / Immersive visualization and gestural interaction withmMedical data

As tecnologias de Realidade Virtual vêm se desenvolvendo bastante nos últimos anos e com elas a sua utilização em diversas áreas, dentre as quais, a medicina. Testes, treinamentos, e alguns tipos de tratamento que seriam complicados de serem ser feitos com abordagens tradicionais agora podem ser produzidos graças aos elementos disponíveis nas tecnologias de realidade virtual. OBJETIVO: Propor uma ferramenta de visualização volumétrica em realidade virtual que possua interação gestual e ferramentas de segmentação de imagens e que facilite o processo de análise de dados médicos. MÉTODOS: Aquisição das imagens, geração dos dados volumétricos, desenvolvimento das ferramentas de interação e desenvolvimento da interface gestual. RESULTADOS: O sistema obteve êxito na geração e visualização de dados médicos tendo bom desempenho em testes realizados na avaliação de usabilidade de sua interface gestual. CONCLUSÃO: O sistemas e mostra como uma promissora alternativa para a visualização de dados médicos em realidade virtual.

The Virtual Reality technologies have been developing greatly in recent years and with them their use in various fields, among which the medicine. Some testings, trainings, and some types of treatments that would be complicated to be made with traditional approaches can now be produced thanks to the elements available in the virtual reality technologies. OBJECTIVE: To propose a volume visualization tool in virtual reality that has gestural interaction and image segmentation tools and facilitates the process of analysis of medical data. METHODS: Image acquisition volumetric data generation, development of the interaction tools and development of the gestural interface. RESULTS: The system was successful in the generation and visualization of medical data, having good performance in usability tests of its gestural interface. CONCLUSION: The system is a promising alternative for viewing medical data in virtual reality.

Texture analysis of masses in digitized mammograms using Gleason and Menhinick diversity indexes

INTRODUCTION: Breast cancer is the second most common type of cancer in the world, being more common among women and representing 22% of all new cancer cases every year. The sooner it is diagnosed, the better the chances of a successful treatment are. Mammography is one way to detect non-palpable tumors that cause breast cancer. However, it is known that the sensitivity of this exam can vary considerably due to factors such as the specialist's experience, the patient's age and the quality of the images obtained in the exam. The use of computational techniques involving artificial intelligence and image processing has contributed more and more to support the specialists in obtaining a more precise diagnosis. METHODS: This paper proposes a methodology that exclusively uses texture analysis to describe features of masses in digitized mammograms. To increase the efficiency of texture feature extraction, the diversity index's capability to detect patterns of species co-occurrence is used. For this purpose, the Gleason and Menhinick indexes are used. Finally, the extracted texture is classified using the Support Vector Machine, looking to differentiate the malignant masses from the benign. RESULTS: The best result was obtained using the Gleason index, with 86.66% accuracy, 90% sensitivity, 83.33% specificity and an area under the ROC Curve (Az) of 0.86. CONCLUSION: Both indexes showed statistically similar performance; however, the Gleason index was slightly superior.