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All Journal International Journal of Electrical and Computer Engineering Program Kreativitas Mahasiswa - Karsa Cipta Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Technomedia Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EDUMATIC: Jurnal Pendidikan Informatika JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT) LOGIC: Jurnal Penelitian Informatika
Gamma Kosala
Telkom University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Other

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Journal : International Journal of Electrical and Computer Engineering

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Stress detection through wearable sensors: a convolutional neural network-based approach using heart rate and step data Wijaya, Rifki; Kosala, Gamma
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1880-1888

Abstract

With the current technological advancements, particularly in sensing technologies, monitoring various health aspects, including heart rate, has become feasible. The problem addressed in this study is the need for effective stress detection methods to mitigate the significant consequences of high-intensity or long-term stress, which impacts safety and disrupts normal routines. We propose a stress detection system developed based on the convolutional neural network (CNN) method to address this. The study involves university students aged 20–22, focusing on mental stress. The dataset encompasses parameters such as heart rate, footsteps, and resting heart rate recorded through a smartwatch with 149,797-row data. Our results indicate that the CNN model achieves an 84.5% accuracy, 80.9% precision, 79.8% recall, and an 80.4% F1-score, confirming its efficacy in stress classification. The confusion matrix further validates the model’s accuracy, particularly for classes 1 and 2. This research contributes significantly to the development of an effective and practical stress detection method, holding promise for enhancing well-being and preventing stress-related health issues.
Co-Authors Abdul Khaliq Al Bari Agus Harjoko Al’Fattah, Bimayudha Bedy Purnama Deny Haryadi Ema Rachmawati Ema Rachmawati Ema Rachmawati Febryanti Sthevanie Gibran, Hilal Ilham Imaduddin, Ilham Kamil, Muhammad Najmi Ketut Sudyatmika Putra Kurniawan Nur Ramadhani Latifah Noor, Latifah Mahmud Dwi Sulistiyo Muhammad Mukhlis Nugraha, Muhammad Mukhlis Muhammad Rayhan Subhi Naufal Harsa Pratama Purnama , Bedy Rahman, Rahadian Yusuf Abdul Rifki Wijaya Safarin, Arva Adwitya Setyawan, Hendy Sri Hartati Tito Waluyo Ubaidillah, Rifky Fahrizal Ulfah Fitriyani, Ulfah Wirasakananda, Dewa Made Aditya