IoT-Enabled Smartwatches for Secure Examinations and Healthcare Assessments

Smart devices and wearable technologies have become indispensable equipment in our daily activities related to education, healthcare, and recreation, including augmented and virtual reality. Considering the educational field, to monitor and assess students’ physiological and stress levels during exams, an experimental setup was developed based on an STM32 board that simulates a smartwatch worn for students. The system measures diverse biosignals, including heart rate (HR), electrodermal activity (EDA), blood volume pulse (BVP), and temperature (TEMP). Data are collected and transmitted via a Bluetooth module to a mobile app, which records and displays live data. Experimental results obtained using a publicly available dataset have shown that the system ensure low estimation errors, a mean absolute error (MAE) of 0.512 and a mean squared error (MSE) of 0.287, enabling precise monitoring of students’ physiological states. Moreover, a model based on deep learning is then used to evaluate the student’s stress level following their physiological signals. Thus, the IoT device can be adopted in real-world educational settings, providing timely insights into student well-being and performance.

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Publication Details

Published
2025-08-17
DOI
https://doi.org/10.1109/case58245.2025.11164074
Primary Topic
Concrete and Cement Materials Research
Type
article
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article

IoT-Enabled Smartwatches for Secure Examinations and Healthcare Assessments

Concrete and Cement Materials Research
article

IoT-Enabled Smartwatches for Secure Examinations and Healthcare Assessments

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Abstract

Smart devices and wearable technologies have become indispensable equipment in our daily activities related to education, healthcare, and recreation, including augmented and virtual reality. Considering the educational field, to monitor and assess students’ physiological and stress levels during exams, an experimental setup was developed based on an STM32 board that simulates a smartwatch worn for students. The system measures diverse biosignals, including heart rate (HR), electrodermal activity (EDA), blood volume pulse (BVP), and temperature (TEMP). Data are collected and transmitted via a Bluetooth module to a mobile app, which records and displays live data. Experimental results obtained using a publicly available dataset have shown that the system ensure low estimation errors, a mean absolute error (MAE) of 0.512 and a mean squared error (MSE) of 0.287, enabling precise monitoring of students’ physiological states. Moreover, a model based on deep learning is then used to evaluate the student’s stress level following their physiological signals. Thus, the IoT device can be adopted in real-world educational settings, providing timely insights into student well-being and performance.

University of Southern California (US), Transylvania University of Brașov (RO)
Openalex Percentile: Top 49%
Concrete and Cement Materials Research
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