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 | |
| 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 |
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Keywords
- acceleration sensor
- behavior recognition
- bicycle
- gyro sensor
- smartphone
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Industrial and Manufacturing Engineering
- Media Technology
Cite this
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
}
TY - GEN
T1 - Bicycle behavior recognition using sensors equipped with smartphone
AU - Usami, Yuri
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.
KW - acceleration sensor
KW - behavior recognition
KW - bicycle
KW - gyro sensor
KW - smartphone
UR - http://www.scopus.com/inward/record.url?scp=85060306292&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060306292&partnerID=8YFLogxK
U2 - 10.1109/ICCE-Berlin.2018.8576254
DO - 10.1109/ICCE-Berlin.2018.8576254
M3 - Conference contribution
AN - SCOPUS:85060306292
VL - 2018-September
BT - 2018 IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018
PB - IEEE Computer Society
ER -