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 when it is installed on a bicycle handlebar. 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. The results indicate that, even if the smartphone is installed on the noisy bicycle handlebar, our proposed method can recognize the bicycle behaviors with almost the same accuracy as the one when a smartphone is installed on a rear axle of a bicycle on which the handlebar motion noises can be much reduced.
| Original language | English |
|---|---|
| Pages (from-to) | 953-965 |
| Number of pages | 13 |
| Journal | IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences |
| Volume | E102A |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 2019 Jan 1 |
Fingerprint
Keywords
- Acceleration sensor
- Behavior recognition
- Bicycle
- Gyro sensor
- Smartphone
ASJC Scopus subject areas
- Signal Processing
- Computer Graphics and Computer-Aided Design
- Electrical and Electronic Engineering
- Applied Mathematics
Cite this
Bicycle behavior recognition using 3-axis acceleration sensor and 3-axis gyro sensor equipped with smartphone. / Usami, Yuri; Ishikawa, Kazuaki; Takayama, Toshinori; Yanagisawa, Masao; Togawa, Nozomu.
In: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Vol. E102A, No. 8, 01.01.2019, p. 953-965.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Bicycle behavior recognition using 3-axis acceleration sensor and 3-axis gyro sensor equipped with smartphone
AU - Usami, Yuri
AU - Ishikawa, Kazuaki
AU - Takayama, Toshinori
AU - Yanagisawa, Masao
AU - Togawa, Nozomu
PY - 2019/1/1
Y1 - 2019/1/1
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 when it is installed on a bicycle handlebar. 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. The results indicate that, even if the smartphone is installed on the noisy bicycle handlebar, our proposed method can recognize the bicycle behaviors with almost the same accuracy as the one when a smartphone is installed on a rear axle of a bicycle on which the handlebar motion noises can be much reduced.
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 when it is installed on a bicycle handlebar. 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. The results indicate that, even if the smartphone is installed on the noisy bicycle handlebar, our proposed method can recognize the bicycle behaviors with almost the same accuracy as the one when a smartphone is installed on a rear axle of a bicycle on which the handlebar motion noises can be much reduced.
KW - Acceleration sensor
KW - Behavior recognition
KW - Bicycle
KW - Gyro sensor
KW - Smartphone
UR - http://www.scopus.com/inward/record.url?scp=85072672225&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072672225&partnerID=8YFLogxK
U2 - 10.1587/transfun.E102.A.953
DO - 10.1587/transfun.E102.A.953
M3 - Article
AN - SCOPUS:85072672225
VL - E102A
SP - 953
EP - 965
JO - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
JF - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
SN - 0916-8508
IS - 8
ER -