Paper Info
Title
Head Motion Recognition Using a Smart Helmet for Motorcycle Riders
Abstract
This paper presents a head motion detection and recognition study using a smart helmet for motorcycle rider which can potential be used for the analysis of behavior of motorcycle riders. The smart helmet is a full face motorcycle helmet integrated with an intelligent system embedded an Inertial Measurement Unit (IMU) sensor. In the analysis, the motions and the corresponding signals are assessed with the video footage with a data acquisition and visualization platform. We introduce a feature extraction methodology to extract the most discriminant features from the signal data, and the head motion recognition problem is formulated as a machine-learning based classification model. Experiment results show that gyroscope sensor data is more useful than accelerometer sensor data for head motion recognition and the classification accuracy for different head motions ranges from 95.9% to 99.1%.
Year
DOI
Venue
2019
10.1109/ICMLC48188.2019.8949319
2019 International Conference on Machine Learning and Cybernetics (ICMLC)
Keywords
Field
DocType
Tracking,Activity classification,Head motion,IMUs,Motorcycle,Smart helmet,Wearable sensors
Gyroscope,Pattern recognition,Motion detection,Motion recognition,Visualization,Computer science,Accelerometer,Data acquisition,Feature extraction,Inertial measurement unit,Artificial intelligence
Conference
ISSN
ISBN
Citations 
2160-133X
978-1-7281-2817-7
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
K. I. Wong100.34
Yi-Chung Chen26913.39
tzuchang lee310.69
Sheng-Min Wang400.34