Stress And Anxiety Monitoring System For Early Detection Of Mental Health Using Iot Based Biomedic Sensors And Deep Neural Networks

Ramadhan, Mohammad Erlangga Syahri (2025) Stress And Anxiety Monitoring System For Early Detection Of Mental Health Using Iot Based Biomedic Sensors And Deep Neural Networks. ELKOM (Jurnal Elektronika dan Komputer), 18 (1). pp. 309-317. ISSN 2714-5417

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Official URL: https://journal.stekom.ac.id/index.php/elkom/artic...

Abstract

Mental health is a crucial aspect of modern life, with stress and anxiety being among the most common and impactful psychological disorders. This research proposes a stress and anxiety monitoring system based on the Internet of Things (IoT), integrating biometric sensors and Deep Neural Networks (DNN) for early detection and in-depth analysis. The system is designed using MAX30102 (heart rate and SpO₂), GSR (Galvanic Skin Response), and DS18B20 (body temperature) sensors, managed by an ESP32 microcontroller and communicating through the MQTT protocol. Physiological data is collected in real-time, formatted in JSON, and transmitted to both Android and web-based applications for visualization. The DNN model is developed using the TensorFlow framework with a layered architecture and ReLU activation functions to classify four mental states: relaxed, calm, anxious, and highly stressed. The training dataset comprises both primary and secondary data, including the WESAD dataset. Model performance is evaluated through k-fold cross-validation, showing high accuracy and strong generalization capabilities. The results indicate that the integration of sensor technology and deep learning significantly improves the effectiveness of stress and anxiety detection compared to traditional methods. This system demonstrates great potential for the development of AI-based wearable devices for autonomous, real-time, and adaptive mental health monitoring.

Item Type:	Article
Uncontrolled Keywords:	Stress, Anxiety, Biometric Sensor, ESP32, Internet of things
Subjects:	Engineering > Electronical Engineering Engineering
Divisions:	Faculty of Engineering > Electronical Engineering Study Program
Depositing User:	Mohammad Erlangga Syahri Ramadhan
Date Deposited:	17 Jun 2026 02:39
Last Modified:	17 Jun 2026 02:39
URI:	http://eprints.umg.ac.id/id/eprint/16634

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