Monitoring Bicycle Riding Motion with Multiple Inertial Sensors

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This paper presents a novel sensory system for monitoring situations of riding bicycle. The proposed system can be used to measure and save in real-time not only the motion of bicycle rider but also the situation near the vehicle. Multiple inertial sensors being attached to human body are employed to measure the motion of the rider. Two laser scanners installed in the front of the bicycle and two cameras of wide view angle were used to detect the environmental change including pedestrians and static/dynamic objects. The system configuration was designed for the synchronization of multiple sensors according to the position information of the vehicle. Particularly, the human motion of riding bicycle is captured with the system and analyzed with the measurement data in this paper.

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1398-1402

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March 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] N. Apostoloff and A. Zelinsky: Vision In and Out of Vehicles: Integrated Driver and Road Scene Monitoring, International Journal of Robotics Research, Vol. 23, No. 4-5, pp.513-538 (2004).

DOI: 10.1177/0278364904042206

Google Scholar

[2] B. Cayir and T. Acarman: Low Cost Driver Monitoring and Warning System Development, Proceedings of IEEE Intelligent Vehicles Symposium, pp.94-98 (2009).

DOI: 10.1109/ivs.2009.5164259

Google Scholar

[3] T. Hirayama, K. Mase and K Takeda: Analysis of Temporal Relationships between Eye Gaze and Peripheral Vehicle Behavior for Detecting Driver Distraction, International Journal of Vehicular Technology, Vol. 2013 (2013).

DOI: 10.1155/2013/285927

Google Scholar

[4] S. Kudo, Vehicle Drive Assist System, U.S. Patent 7, 925, 415 B2. (2011).

Google Scholar

[5] W. Walker, A.L.P. Aroul and D. Bhatia: Mobile Health Monitoring Systems, Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.5199-5202 (2009).

DOI: 10.1109/iembs.2009.5334583

Google Scholar

[6] J.H. Lee, T. Yano, T. Yamashita, and S. Okamoto, Sensory System for Monitoring Status of Driving Bicycle, Proceedings of the 19th Int. Symposium on Artificial Life and Robotics (2014).

Google Scholar

[7] S.O.H. Madgwick, A.J.L. Harrison and R. Vaidyanathan: Estimation of IMU and MARG Orientation Using a Gradient Descent Algorithm, Proceedings of IEEE International Conference on Rehabilitation Robotics (2011), pp.1-7.

DOI: 10.1109/icorr.2011.5975346

Google Scholar