Bicycle behavior recognition using sensors equipped with smartphone

Yuri Usami; Kazuaki Ishikawa; Toshinori Takayama; Masao Yanagisawa; Nozomu Togawa

doi:10.1109/ICCE-Berlin.2018.8576254

Bicycle behavior recognition using sensors equipped with smartphone

Yuri Usami, Kazuaki Ishikawa, Toshinori Takayama, Masao Yanagisawa, Nozomu Togawa

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

It becomes possible to prevent accidents beforehand by predicting dangerous riding behavior based on recognition of bicycle behaviors. In this paper, we propose a bicycle behavior recognition method using a three-axis acceleration sensor and three-axis gyro sensor equipped with a smartphone. We focus on the periodic handlebar motions for balancing while running a bicycle and reduce the sensor noises caused by them. After that, we use machine learning for recognizing the bicycle behaviors, effectively utilizing the motion features in bicycle behavior recognition. The experimental results demonstrate that the proposed method accurately recognizes the four bicycle behaviors of stop, run straight, turn right, and turn left and its F-measure becomes around 0.9 while the F-measure of the existing method just reaches 0.6-0.8.

Original language	English
Title of host publication	2018 IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018
Publisher	IEEE Computer Society
Volume	2018-September
ISBN (Electronic)	9781538660959
DOIs	https://doi.org/10.1109/ICCE-Berlin.2018.8576254
Publication status	Published - 2018 Dec 13
Event	8th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018 - Berlin, Germany Duration: 2018 Sep 2 → 2018 Sep 5

Other

Other	8th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018
Country	Germany
City	Berlin
Period	18/9/2 → 18/9/5

Keywords

acceleration sensor
behavior recognition
bicycle
gyro sensor
smartphone

ASJC Scopus subject areas

Electrical and Electronic Engineering
Industrial and Manufacturing Engineering
Media Technology

Access to Document

10.1109/ICCE-Berlin.2018.8576254

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Usami, Y., Ishikawa, K., Takayama, T., Yanagisawa, M., & Togawa, N. (2018). Bicycle behavior recognition using sensors equipped with smartphone. In 2018 IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018 (Vol. 2018-September). [8576254] IEEE Computer Society. https://doi.org/10.1109/ICCE-Berlin.2018.8576254

Bicycle behavior recognition using sensors equipped with smartphone. / Usami, Yuri; Ishikawa, Kazuaki; Takayama, Toshinori; Yanagisawa, Masao ; Togawa, Nozomu.

2018 IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018. Vol. 2018-September IEEE Computer Society, 2018. 8576254.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Usami, Y, Ishikawa, K, Takayama, T, Yanagisawa, M & Togawa, N 2018, Bicycle behavior recognition using sensors equipped with smartphone. in 2018 IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018. vol. 2018-September, 8576254, IEEE Computer Society, 8th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018, Berlin, Germany, 18/9/2. https://doi.org/10.1109/ICCE-Berlin.2018.8576254

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abstract = "It becomes possible to prevent accidents beforehand by predicting dangerous riding behavior based on recognition of bicycle behaviors. In this paper, we propose a bicycle behavior recognition method using a three-axis acceleration sensor and three-axis gyro sensor equipped with a smartphone. We focus on the periodic handlebar motions for balancing while running a bicycle and reduce the sensor noises caused by them. After that, we use machine learning for recognizing the bicycle behaviors, effectively utilizing the motion features in bicycle behavior recognition. The experimental results demonstrate that the proposed method accurately recognizes the four bicycle behaviors of stop, run straight, turn right, and turn left and its F-measure becomes around 0.9 while the F-measure of the existing method just reaches 0.6-0.8.",

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AB - It becomes possible to prevent accidents beforehand by predicting dangerous riding behavior based on recognition of bicycle behaviors. In this paper, we propose a bicycle behavior recognition method using a three-axis acceleration sensor and three-axis gyro sensor equipped with a smartphone. We focus on the periodic handlebar motions for balancing while running a bicycle and reduce the sensor noises caused by them. After that, we use machine learning for recognizing the bicycle behaviors, effectively utilizing the motion features in bicycle behavior recognition. The experimental results demonstrate that the proposed method accurately recognizes the four bicycle behaviors of stop, run straight, turn right, and turn left and its F-measure becomes around 0.9 while the F-measure of the existing method just reaches 0.6-0.8.

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