• Title/Summary/Keyword: Multi-person boarding detection

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Multiuser Detection of Electric Scooter Using Tilt and Pressure Sensors (기울기 센서와 압력 센서를 이용한 전동 킥보드용 다인승 감지 방안)

  • Moonjeong Ahn;Jia Kim;Jihoon Lee
    • Journal of the Semiconductor & Display Technology
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    • v.23 no.2
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    • pp.28-32
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    • 2024
  • The personal mobility Sharing service is currently active. Especially, electric scooters are widely utilized because they can move comfortably at a high speed over a short distance with a simple driving method. Its driving method is easy, but there is no protection device to protect the bare body. So, there is a greater accident than other means of transportation, and if two people are on board, there is higher accident probability. However, since there is no specific ways to prevent multi-person boarding yet, we propose a multi-person boarding detection model using tilt and pressure sensor. The proposed method measures the tilt degree and direction by using a tilt sensor installed in the center of the board plate and detects multi-people riding.

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CDS-based Efficient Multiuser Boarding Detection Scheme for Electric Scooters (조도 센서를 이용한 효율적인 학습모델 기반의 전동 킥보드용 다인 탑승 감지 방안)

  • Harin Kwon;Munjeong Ahn;Jihoon Lee
    • Journal of the Semiconductor & Display Technology
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    • v.23 no.3
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    • pp.148-151
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    • 2024
  • Electric scooters are used by many people as a means of transportation because they are easy to operate and get around quickly. Although electric scooters can move at high speeds, they lack safety measures such as seat belts and blocked car body, which can lead to serious injury in the event of an accident. If there are two people on board, the braking speed will be slower and the weight of the vehicle will increase its braking distance during sudden stops, leading to even more serious damage. Therefore, this paper proposes the illuminance sensors based multiuser boarding detection scheme for electric scooter, which is expected to decrease the risk of accidents and to lessen the likelihood of injury. From the performance evaluation results, it has been shown that the proposed scheme has higher detection ratio than existing schemes and has the detection accuracy of about 83% by means of the machine learning based foot position estimation for the sensed data.

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