Abstract
A variety of biosensors have been recently introduced as wearable devices to collect physiological data, with applications ranging from personalized medicine and point-of-care diagnostics to home and fitness monitoring, among others, garnering substantial interest. This interest has been fueled by the increasing demand for ubiquitous, continuous, and pervasive vital signs monitoring, coupled with advancements in biosensor technology and IoT-enabled capabilities. Existing research studies have only relied on a limited number of health- and physiological-related indicators (thus, do not offer a comprehensive health monitoring and assessment system) due to the technical difficulties to integrate multiple sensors. In fact, the issues of multimodality, heterogeneity, and complexity of data as well as the interoperability among sensors make it challenging to seamlessly integrate multiple sensors into one system. This study overcame these technical challenges by leveraging multi-sensor fusion capabilities to develop an intelligent, IoT-enabled wearable multi-modal biosensing device and cloud-based digital dashboard for real-time, comprehensive health, physiological, emotional, and cognitive monitoring. First, 18 different health- and physiological-related indicators were identified. Second, 14 different sensors were used to acquire the entire data for the 18 different indicators using a hardware sensing system designed using four ESP32 microcontroller boards integrated with Wi-Fi and Bluetooth connectivity by fusing the various data from the 14 different sensors. Third, the designed system was developed as a wearable device that can be installed on the hip as well as the right and left feet using 3D printed parts. Fourth, a web-based digital dashboard was created onan edge computing server that was hosted on a microprocessor to instantly publish the data, and a graphical user interface (GUI) was developed to provide intuitive and real-time visualization of the various health-related indicators using the Django and JavaScript-based React.js web development frameworks. The accuracy of the developed IoT-enabled biosensing system was tested and validated by benchmarking and comparing the obtained results from the proposed system with those aquired from various commercially used sensors. The validation outcomes reflected that the proposed system achieved an accuracy of more than 90 % for most of the 18 considered indicators and an accuracy greater than 85 % for all indicators. This study adds to the body of knowledge by being the first research capable of reporting the following 18 indicators into a single biosensing system in real-time: Electrocardiogram (ECG or EKG), Electroencephalogram (EEG), Electrooculogram (EOG), Electromyography (EMG), Photoplethysmography (PPG), heart rate (HR), heart rate variability (HRV), respiratory rate (RR), skin temperature (ST), skin humidity (SH), blood glucose level (BGL), blood pressure (BP), oxygen saturation (SpO2), body weight pressure (BWP), body motion (BM), electrodermal activity (EDA), galvanic skin response (GSR), and skin conductance responses (SCR). The proposed system provides rich information on various vital signs and could be used for a wide window of applications, including monitoring and assessing health status; emotional and arousal status; mental and cognitive status; behavioral, physical, and attention status; and physiological status. The developed system is not specific to a particular industry but rather could be used for any sector of interest. This paper lays the ground to significant advancements in wearable sensor technology, data visualization techniques, and health monitoring practices.
Published Version
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