• Journal of Multimedia Information System
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• v.3 no.3
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• pp.69-76
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• 2016

Currently, research on more convenient and safer cars for drivers and passengers, the intelligent cars, are being actively conducted. The researches involve designing systems that intelligently minimize physical damages caused by car accidents, not just pursuing passive safety measures like airbags or safety belts. However, there are many cases around the world where airbags cannot be activated in times of accidents and it is difficult for consumers to check if their airbags will be activated, especially for the old-style cars. Thus, in this paper, a base technology for an application that can determine whether the airbags will actually be actuated at a critical moment based on the mechanical calculations is being introduced.

• Journal of the Korean Society of Safety
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• v.35 no.2
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• pp.94-99
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• 2020

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.