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
N1 - 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:
© 2018 IEEE.
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
T3 - IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin
BT - 2018 IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018
PB - IEEE Computer Society
T2 - 8th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018
Y2 - 2 September 2018 through 5 September 2018
ER -