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 interesting opportunity of remote health monitoring and disease risk categorisation of populations. An end-to-end architecture is proposed to facilitate real-time digital dashboards to visualise general anxiety risks of patients, especially during a pandemic, such as COVID-19. To collect physiological data related to anxiety (heart rate, blood pressure, and saturation of peripheral oxygen [SPO2]) and communicate them to a centralised dashboard, dubbed 'X-DASH', a hardware prototype of the proposed architecture was developed using Node-MCU and diverse sensors. The dashboard presents a smart categorisation of users' data, assessing their anxiety risks, to provide medical professionals and state authorities a clear visualisation of health risks in populations belonging to different regions. We categorised the risk levels as Normal, Mild, Moderate, Elevated, Severe, and Extreme, based on the collected physiological data and pre-defined threshold values. The developed hardware prototype in this work was used to collect data from about 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 was consulted and was requested to identify each of their anxiety risk levels. It was found that the 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 compared when using MQTT, CoAP and Modbus. Although MQTT has shown higher delays, but it is still recommended due to having a higher reliability. An anxiety risk assessment dashboard is presented based on data collected from Internet of Things sensors. We present the data obtained from patients, during the clinical trials where the authors' devices were used to collect data. Moreover, the validation of data collected by pscyhologists has also been included. image