Towards assessing reliability of next‐generation Internet of Things dashboard for anxiety risk classification

The ubiquitous Internet of Things (IoT) and sensing technologies provide an interestingopportunity of remote health monitoring and disease risk categorisation of populations.An end‐to‐end architecture is proposed to facilitate real‐time digital dashboards tovisualise general anxiety risks of patients, especially during a pandemic, such as COVID‐19. To collect physiological data related to anxiety (heart rate, blood pressure, andsaturation of peripheral oxygen [SPO₂]) and communicate them to a centralised dash-board, dubbed ‘X‐DASH’, a hardware prototype of the proposed architecture wasdeveloped using Node‐MCU and diverse sensors. The dashboard presents a smart cat-egorisation of users' data, assessing their anxiety risks, to provide medical professionalsand state authorities a clear visualisation of health risks in populations belonging todifferent regions. We categorised the risk levels as Normal, Mild, Moderate, Elevated,Severe, and Extreme, based on the collected physiological data and pre‐defined thresholdvalues. The developed hardware prototype in this work was used to collect data fromabout 500 patients present at cardiac clinic of a leading general hospital in Karachi(Pakistan) and the anxiety risk levels were assigned based on pre‐defined threshold values.To validate the reliability of the X‐DASH, the personal physician of each patient wasconsulted and was requested to identify each of their anxiety risk levels. It was found thatthe risk levels suggested by X‐DASH, (based on data of heart rate, blood pressure, and SPO₂ were more than 90% accurate when compared with diagnoses of physicians.Subsequently, packet loss, delay and network overhead for the platform was comparedwhen using MQTT, CoAP and Modbus. Although MQTT has shown higher delays, but itis still recommended due to having a higher reliability.

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Published in: IET Wireless Sensor Systems
License: http://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://doi.org/10.1049/wss2.12090