A Multimodal Digital Health Prototype for Real-Time Student Stress Monitoring Using Internet of Things and Artificial Intelligence

Salsabilla Mulyabudiman; Agmisyaniah Agmisyaniah; Sebtina Cinta Anugrahini; Ajeng Azzahroh; Annisa Zhafira Adhya

Authors

Salsabilla Mulyabudiman Universitas Pembangunan Nasional Veteran Jakarta
Agmisyaniah Agmisyaniah Universitas Pembangunan Nasional Veteran Jakarta
Sebtina Cinta Anugrahini Universitas Pembangunan Nasional Veteran Jakarta
Ajeng Azzahroh Universitas Pembangunan Nasional Veteran Jakarta
Annisa Zhafira Adhya Universitas Pembangunan Nasional Veteran Jakarta

Keywords:

Internet of Things , Artificial Intelligence , Stress Detection , Health Monitoring , Mobile Health , Digital Mental Health

Abstract

Academic stress has become an important concern among university students because of its potential impact on physical health, psychological well-being, and academic performance. Conventional stress-assessment approaches are often limited by subjective evaluation and the lack of continuous monitoring capabilities. This study aimed to design SynBioSense, a conceptual framework and prototype for student stress monitoring that integrates Internet of Things and Artificial Intelligence technologies. A Design Research approach was employed to develop the system architecture, monitoring workflow, conceptual verification framework, and mobile application prototype. The proposed framework was designed to integrate multimodal physiological sensing, cloud-based infrastructure, Artificial Intelligence-assisted analysis, and mobile-based visualization within a unified digital-health architecture. The study resulted in conceptual system architecture, workflow model, and user-interface prototype that illustrated how monitoring, visualization, recommendation, and user-support functionalities could be integrated into a student-oriented platform. The study contributed an integrated conceptual design that may serve as a foundation for future implementation, validation, and deployment of digital stress-monitoring systems in higher-education environments. Future research should focus on system implementation, Artificial Intelligence model development, and empirical evaluation involving university students

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