On the Design of Low-Cost IoT Sensor Node for e-Health Environments

Abstract

The proliferation of Internet of Things (IoT) devices for patient monitoring has gained much attention in clinical care performance, proficient chronic disease management, and home caregiving. This work presents the design of efficient medical IoT sensor nodes (SNs) in terms of low-cost, low power-consumption, and increased data accuracy based on open-source platforms. The method utilizes a Sensor Controller (SC) within the IoT SN, which is capable of performing medical checks supporting a broad coverage of medical uses. A communication protocol has been developed for data and command exchange among SC, local gateways, and physicians’ or patients’ mobile devices (tablets, smart phones). The SC supports moving average window (MAW) and principle component analysis (PCA) filtering algorithms to capture data from the attached low-cost body sensors of different sampling profiles. Significant extensions in SN’s portability is achieved through energy consumption minimization based on the idle time gaps between sensors’ activations. SN’s components are either deactivated or set to low activity operation during these idle intervals. A medical case study is presented and the evaluated results show that the proposed SN can be incorporated into e-health platforms since it achieves comparable accuracy to its certified and high-cost commercial counterparts.

Symptom Tracking and Experimentation Platform for Covid-19 or Similar Infections

“…Local processing is encouraged to minimize the risk of data exposure during wireless or even wired communication. This is the reason why a local Sensor Controller (Gateway) such as the one used in [29] is proposed for the sensor infrastructure of the Coronario platform. The supported alerts defined in the medical protocol can check various parameters locally and perform certain actions avoiding the move of sensitive data to the cloud.…”

“…Only the important sensor values are uploaded to the cloud for higher data privacy and they can be isolated for example, through appropriate thresholds. The experiments conducted here, were based on the sensor infrastructure described in [29]. After user consent, some sensor data may be anonymously used by the ScientificApp in the context of research about Covid-19 or other similar infections.…”

“…The research concerning the nature of Covid-19 requires an abundance of information about the pre-and post-hospitalization phase. An eHealth infrastructure that is, a number medical sensors on the side of the patient is employed to support a more reliable diagnosis based on the overall patient condition and his environment [29]. The anonymous medical data that are used in the Coronario platform can also be exploited in the context of research on Covid-19.…”

A COVID-19 Multipurpose Platform

“…Image processing will be employed in future versions to monitor example skin disorders caused by an infection [17]. In the current version, the supervising doctor is responsible for the diagnosis.…”

Enhancement of the Au/ZnO-NA Plasmonic SERS Signal Using Principal Component Analysis as a Machine Learning Approach

“…Numerous methods, including principal component analysis (PCA) and independent component analysis (ICA), can be used [8]. These methods are well-established methods for reducing the dimensionality, and are commonly used for avoiding redundancy due to high-dimensional data [9]. Thus, PCA as a machine learning approach was used to compute multivariate statistics to identify specific diseases with a strong enhanced signal.…”