The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Ia menjadi mungkin untuk mencegah kemalangan terlebih dahulu dengan meramalkan tingkah laku menunggang berbahaya berdasarkan pengiktirafan tingkah laku basikal. Dalam kertas kerja ini, kami mencadangkan kaedah pengecaman tingkah laku basikal menggunakan penderia pecutan tiga paksi dan penderia giro tiga paksi yang dilengkapi dengan telefon pintar apabila ia dipasang pada bar hendal basikal. Kami menumpukan pada gerakan hendal berkala untuk mengimbangi semasa menjalankan basikal dan mengurangkan bunyi sensor yang disebabkan olehnya. Selepas itu, kami menggunakan pembelajaran mesin untuk mengenali gelagat basikal, menggunakan ciri gerakan secara berkesan dalam pengecaman gelagat basikal. Keputusan eksperimen menunjukkan bahawa kaedah yang dicadangkan mengenali dengan tepat empat tingkah laku basikal iaitu berhenti, berlari lurus, belok kanan dan belok kiri dan ukuran Fnya menjadi sekitar 0.9. Keputusan menunjukkan bahawa, walaupun telefon pintar dipasang pada bar hendal basikal yang bising, kaedah yang dicadangkan kami boleh mengecam tingkah laku basikal dengan ketepatan yang hampir sama seperti yang berlaku apabila telefon pintar dipasang pada gandar belakang basikal di mana bar hendal bergerak. bunyi boleh dikurangkan.
Yuri USAMI
Waseda University
Kazuaki ISHIKAWA
Zenrin DataCom Co., LTD.
Toshinori TAKAYAMA
Zenrin DataCom Co., LTD.
Masao YANAGISAWA
Waseda University
Nozomu TOGAWA
Waseda University
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Salinan
Yuri USAMI, Kazuaki ISHIKAWA, Toshinori TAKAYAMA, Masao YANAGISAWA, Nozomu TOGAWA, "Bicycle Behavior Recognition Using 3-Axis Acceleration Sensor and 3-Axis Gyro Sensor Equipped with Smartphone" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 8, pp. 953-965, August 2019, doi: 10.1587/transfun.E102.A.953.
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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.953/_p
Salinan
@ARTICLE{e102-a_8_953,
author={Yuri USAMI, Kazuaki ISHIKAWA, Toshinori TAKAYAMA, Masao YANAGISAWA, Nozomu TOGAWA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Bicycle Behavior Recognition Using 3-Axis Acceleration Sensor and 3-Axis Gyro Sensor Equipped with Smartphone},
year={2019},
volume={E102-A},
number={8},
pages={953-965},
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.},
keywords={},
doi={10.1587/transfun.E102.A.953},
ISSN={1745-1337},
month={August},}
Salinan
TY - JOUR
TI - Bicycle Behavior Recognition Using 3-Axis Acceleration Sensor and 3-Axis Gyro Sensor Equipped with Smartphone
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 953
EP - 965
AU - Yuri USAMI
AU - Kazuaki ISHIKAWA
AU - Toshinori TAKAYAMA
AU - Masao YANAGISAWA
AU - Nozomu TOGAWA
PY - 2019
DO - 10.1587/transfun.E102.A.953
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E102-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2019
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.
ER -