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All Journal Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Husneni Mukhtar
Department of Electrical Engineering, School of Electrical Engineering, Telkom University, Bandung, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology

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Design and Evaluation of a Pre-Impact Fall Detection System with Wearable Airbag Vest for Elderly Injury Mitigation Willy Anugrah Cahyadi; Husneni Mukhtar; Suto Setiyadi; Dita Puspitasari; Nigel Bryan Tang; Mohamad Ramdhani
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 3 (2026): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i3.344

Abstract

The increase in the elderly population in Indonesia raises concerns about fall accidents. Many mitigation strategies are available to reduce fall accidents among the elderly. Two main strategies have been widely explored, i.e., fall detection and impact reduction. Despite its disadvantages, fall detection devices remain useful and important for future research. In addition, it is also urgent to address the impact reduction method by providing cushioning to minimize the physical impact on the subject of the fall. This study proposes a wearable safety vest equipped with a pre-impact fall-detection system and an automatically inflating airbag to protect the wearer during a fall. The main contributions of this study are the design and implementation of a lightweight wearable airbag vest for elderly protection, the development of a machine-learning-based pre-impact fall detection, and a quantitative evaluation of impact reduction using accelerometer-based measurements. The proposed safety vest with airbag has been trained, validated, and tested, with the subject achieving fall detection accuracy beyond 90% and the airbag deployment delay of less than 1 second after detection. The experiment results also show that the use of the airbag could reduce the impact shock down to 40-50% based on the accelerometer measurements.
Co-Authors Dita Puspitasari Mohamad Ramdhani Nigel Bryan Tang Suto Setiyadi Willy Anugrah Cahyadi