• 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.

• Journal of Korea Multimedia Society
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• v.13 no.1
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• pp.66-75
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• 2010

We proposed an occupant sensing system using single video camera and ultrasonic sensor for the advanced airbag. To detect the occupant form and the face position in real-time, we used the skin color and motion information. We made the candidate face block image using the threshold value of the color difference signal corresponding to skin color and difference value of current image and previous image of luminance signal to gel motion information. And then it detects the face by the morphology and the labeling. In case of night without color and luminance information, it detects the face by using the threshold value of the luminance signal get by infra-red LED instead of the color difference signal. To evaluate the performance of the proposed occupant detection system, it performed various experiments through the setting of the IEEE camera, ultrasonic sensor, and infra-red LED in vehicle jig.

• International Journal of Automotive Technology
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• v.3 no.2
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• pp.53-56
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• 2002

A new generation of the BMW child seat and occupant detection system SBE2 far a smart airbag system is described. The SBE2 system consists of two subsystems: OC (Occupant Classification) and FDS (Field Detection System). The OC system is a force sensitive sensor array that measures a pressure profile. The FDS system detects child seat and occupant according to the change of electrical field generated by four capacitive plates. Combining the signals from both subsystems, the BMW SBE2 system can distinguish fully automatically between a child seat and a person.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.22 no.3
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• pp.222-229
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• 2009

A noble infrared sensor was studied for passenger conditional detection in vehicle, This research relates to uncooled infrared sensors for detecting the presence, type and temperature of occupants in vehicle. It sense that the occupants purpose to control the smart airbag for safety in the case of adult or child and to control the automatic air conditioning for convenience. This paper described the design and the fabrication of microbolometers which were composed of 2 by 8 elements using the surface micromachining technology. The characteristics of the array were investigated in the spectral region of $8{\sim}12{\mu}m$. The fabricated detectors exhibited the thermal mass of $7.05{\times}10^{-9}\;J/K$, the thermal conductance of $1.03{\times}10^{-6}\;W/K$, the thermal time constant of 6.8 ms, the responsivity of $2.96{\times}10^4\;V/W$ and the detectivity of $1.01{\times}10^9\;cmHz^{1/2}/W$, at the chopper frequency of 10 Hz and the bias current of $4.4{\mu}A$. We could successfully detect the human body condition in the divided zone. As a results, we concluded that microbolometer optimized in this research could be useful for the application of passenger conditional detection in vehicle.