Transportation mode detection using kinetic energy harvesting wearables

Lan, Guohao, Xu, Weitao, Khalifa, Sara, Hassan, Muhbub and Hu, Wen (2016). Transportation mode detection using kinetic energy harvesting wearables. In: 2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016. 13th IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016, Sydney, NSW, (). 14-18 March 2016. doi:10.1109/PERCOMW.2016.7457048


Author Lan, Guohao
Xu, Weitao
Khalifa, Sara
Hassan, Muhbub
Hu, Wen
Title of paper Transportation mode detection using kinetic energy harvesting wearables
Conference name 13th IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016
Conference location Sydney, NSW
Conference dates 14-18 March 2016
Convener IEEE
Proceedings title 2016 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016
Journal name 2016 Ieee International Conference On Pervasive Computing and Communication Workshops (Percom Workshops)
Publisher Institute of Electrical and Electronics Engineers
Publication Year 2016
Year available 2016
Sub-type Fully published paper
DOI 10.1109/PERCOMW.2016.7457048
Open Access Status Not Open Access
ISBN 9781509019410
Total pages 4
Collection year 2017
Language eng
Abstract/Summary Detecting the transportation mode of an individual's everyday travel provides useful information in urban design, real-time journey planning, and activity monitoring. In existing systems, accelerometer and GPS are the dominantly used signal sources which quickly drain the limited battery life of the wearable devices. In this paper, we investigate the feasibility of using the output voltage from the kinetic energy harvesting device as the signal source to achieve transportation mode detection. The proposed idea is based on the intuition that the vibrations experienced by the passenger during motoring of different transportation modes are different. Thus, voltage generated by the energy harvesting devices should contain distinctive features to distinguish different transportation modes. Using the dataset collected from a real energy harvesting device, we present the initial demonstration of the proposed method. We can achieve 98.84% of accuracy in determining whether the user is traveling by pedestrian or motorized modes, and in a fine-grained classification of three different motorized modes (car, bus, and train), an overall accuracy over 85% is achieved
Keyword Australia
Data collection
Kinetic energy
Motion segmentation
Vehicles
Vibrations
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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