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http://dx.doi.org/10.14346/JKOSOS.2020.35.2.94

A Study on a Wearable Smart Airbag Using Machine Learning Algorithm  

Kim, Hyun Sik (Department of Mechanical Design Engineering, Graduate School of Pukyong National University)
Baek, Won Cheol (Department of Weapon Systems Engineering, Graduate School of Pukyong National University)
Baek, Woon Kyung (Department of Mechanical Design Engineering, Pukyong National University)
Publication Information
Journal of the Korean Society of Safety / v.35, no.2, 2020 , pp. 94-99 More about this Journal
Abstract
Bikers can be subjected to injuries from unexpected accidents even if they wear basic helmets. A properly designed airbag can efficiently protect the critical areas of the human body. This study introduces a wearable smart airbag system using machine learning techniques to protect human neck and shoulders. When a bicycle accident happens, a microprocessor analyzes the biker's motion data to recognize if it is a critical accident by comparing with accident classification models. These models are trained by a variety of possible accidents through machine learning techniques, like k-means and SVM methods. When the microprocessor decides it is a critical accident, it issues an actuation signal for the gas inflater to inflate the airbag. A protype of the wearable smart airbag with the machine learning techniques is developed and its performance is tested using a human dummy mounted on a moving cart.
Keywords
wearable smart airbag; accident recognition algorithm; machine learning; k-means; SVM;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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