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Design of Wearable Prototype Smart Wristband for Remote Health Monitoring Using Internet of Things Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Driving Activity Recognition of Motorcyclists Using Smartphone Sensor

Authors : Aasim Raheel, Muhammad Ehatisham-ul-Haq, Anees Iqbal, Hanan Ali, Muhammad Majid

Published in: Intelligent Technologies and Applications

Publisher: Springer Singapore

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Abstract

Smartphone sensors ubiquitously provide an unobtrusive opportunity to develop solutions for road anomaly detection, driving behavior analysis, and activity recognition. Driver’s activity recognition is important for monitoring streets and narrow lanes where employed vehicles cannot get along. In this paper, smartphone sensor is used to monitor driving activity of motorcyclists. Motorcyclists are asked to follow a predefined path and gyroscope data is recorded from the phone, which is placed in motorcyclist pocket. Features are selected from twelve extracted statistical features from the recorded gyroscope data to classify four driving activities i.e., left turn, right turn, U-turn, and a straight path. Three different classifiers i.e., Bayes Net, random forest, and support vector machine are used to classify four motorcyclists driving activities. It is evident that the random forest classifies four motorcyclist driving activities with the highest accuracy of 86.51%.

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Metadata
Title
Driving Activity Recognition of Motorcyclists Using Smartphone Sensor
Authors
Aasim Raheel
Muhammad Ehatisham-ul-Haq
Anees Iqbal
Hanan Ali
Muhammad Majid
Copyright Year
2020
Publisher
Springer Singapore
Book
Intelligent Technologies and Applications

Print ISBN: 978-981-15-5231-1

Electronic ISBN: 978-981-15-5232-8

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-15-5232-8_59

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