Connected health is expected to introduce an improvement in providing healthcare and doctor-patient communication while at the same time reducing cost. Connected health would introduce an even more significant gap between healthcare quality for urban areas with physical proximity and better communication to providers and the portion of rural areas with numerous connectivity issues. We identify these challenges using user scenarios and propose LoRa based architecture for addressing these challenges. We focus on the energy management of battery-powered, affordable IoT devices for long-term operation, providing important information about the care receivers’ well-being. Using an external ultra-low-power timer, we extended the battery life in the order of tens of times, compared to relying on low power modes of the microcontroller.

One of the main goals of Ambient Assisted Living (AAL) is to provide supportive environment for the elderly or disabled. Such environments are not feasible without correctly identifying states and activities of the persons receiving the care. They rely on the interaction and processing of data originating from many components and objects in the surrounding. In order to collect the data, various sensors are used to monitor the environment, as well as the person's health parameters. One of the main concerns in AAL is preservation of user's privacy. In this paper we address that by proposing a non-intrusive approach for data collection and identification of daily activity and risks. We describe the wiring of such system based on cheap non-intrusive sensors, deployment in a real environment, the protocols for data fusion and processing, and explain how machine learning could be employed for detecting risks and activities. The main contribution of this paper is development of non-intrusive sensor kits that can be easily deployed in real-life environments and are capable of collecting data that can reliable detect activities and risk.

Pneumonia caused by COVID-19 is a severe health risk that sometimes leads to fatal outcomes. Due to constraints in medical care systems, technological solutions should be applied to diagnose, monitor, and alert about the disease’s progress for patients receiving care at home. Some sleep disturbances, such as obstructive sleep apnea syndrome, can increase the risk for COVID-19 patients. This paper proposes an approach to evaluating patients’ sleep quality with the aim of detecting sleep disturbances caused by pneumonia and other COVID-19-related pathologies. We describe a non-invasive sensor network that is used for sleep monitoring and evaluate the feasibility of an approach for training a machine learning model to detect possible COVID-19-related sleep disturbances. We also discuss a cloud-based approach for the implementation of the proposed system for processing the data streams. Based on the preliminary results, we conclude that sleep disturbances are detectable with affordable and non-invasive sensors.