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

School of Fundamental Science and Engineering

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

9 Citations (Scopus)

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
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

Publication series

Name	IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin
Volume	2018-September
ISSN (Print)	2166-6814
ISSN (Electronic)	2166-6822

Other

Other	8th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018
Country/Territory	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

Cite this

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 [8576254] (IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin; Vol. 2018-September). 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 et al.

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

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., 8576254, IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin, vol. 2018-September, 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

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

@inproceedings{9a616ca92aa94a71b5e9bde1e97513db,

title = "Bicycle behavior recognition using sensors equipped with smartphone",

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.",

keywords = "acceleration sensor, behavior recognition, bicycle, gyro sensor, smartphone",

author = "Yuri Usami and Kazuaki Ishikawa and Toshinori Takayama and Masao Yanagisawa and Nozomu Togawa",

note = "Funding Information: ACKNOWLEDGMENTS This research is supported in part by Waseda University Grant for Special Research Projects (Nos. 2018K-189 and 2018B-088). Publisher Copyright: {\textcopyright} 2018 IEEE.; 8th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018 ; Conference date: 02-09-2018 Through 05-09-2018",

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doi = "10.1109/ICCE-Berlin.2018.8576254",

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AU - Ishikawa, Kazuaki

AU - Takayama, Toshinori

AU - Yanagisawa, Masao

AU - Togawa, Nozomu

PY - 2018/12/13

Y1 - 2018/12/13

N2 - 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.

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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Bicycle behavior recognition using sensors equipped with smartphone

Abstract

Publication series

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Keywords

ASJC Scopus subject areas

Access to Document

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