ABSTRACT
: This paper presents an IoT network system, using fog computing to identify agglomerations from IP camera images, processing for pattern recognition and distance calculations. [...]monitoring is done efficiently, as there is no need to send the images to be processed by a centralized system (data center or cloud), bringing savings in terms of sending and storing data. Having said that, situations that need attitude from a monitoring manager to avoid breaks in social distance can easily be managed. O sistema é composto por por dispositivos inteligentes, ou SBC (Single Board Computers), que sao responsáveis por processar e identificar as aglomeraçoes nas imagens enviadas pelas câmeras IP, assim como também um computador, que é responsável por receber todo o fluxo de imagens das possíveis aglomeraçoes detectadas pelos dispositivos, validar as imagens e notificar as quebras de distanciamento social. 3.1.Arquitetura do Projeto O sistema foi construido em cima de paradigmas IoT, Fog/Cloud Computing e Vis&acaron;o Computacional.
ABSTRACT
Automatized scalable healthcare support solutions allow real-time 24/7 health monitoring of patients, prioritizing medical treatment according to health conditions, reducing medical appointments in clinics and hospitals, and enabling easy exchange of information among healthcare professionals. With recent health safety guidelines due to the COVID-19 pandemic, protecting the elderly has become imperative. However, state-of-the-art health wearable device platforms present limitations in hardware, parameter estimation algorithms, and software architecture. This paper proposes a complete framework for health systems composed of multi-sensor wearable health devices (MWHD), high-resolution parameter estimation, and real-time monitoring applications. The framework is appropriate for real-time monitoring of elderly patients' health without physical contact with healthcare professionals, maintaining safety standards. The hardware includes sensors for monitoring steps, pulse oximetry, heart rate (HR), and temperature using low-power wireless communication. In terms of parameter estimation, the embedded circuit uses high-resolution signal processing algorithms that result in an improved measure of the HR. The proposed high-resolution signal processing-based approach outperforms state-of-the-art HR estimation measurements using the photoplethysmography (PPG) sensor.