Advancing Android activity recognition service with Markov smoother

Zhong, Mingyang, Wen, Jiahui, Hu, Peishao and Indulska, Jadwiga (2015). Advancing Android activity recognition service with Markov smoother. In: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2015. IEEE International Conference on Pervasive Computing and Communication, St. Louis, MO, United States, (38-43). 23-27 March, 2015. doi:10.1109/PERCOMW.2015.7133990


Author Zhong, Mingyang
Wen, Jiahui
Hu, Peishao
Indulska, Jadwiga
Title of paper Advancing Android activity recognition service with Markov smoother
Conference name IEEE International Conference on Pervasive Computing and Communication
Conference location St. Louis, MO, United States
Conference dates 23-27 March, 2015
Convener IEEE
Proceedings title 2015 IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2015
Journal name 2015 Ieee International Conference On Pervasive Computing and Communication Workshops (Percom Workshops)
Place of Publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Publication Year 2015
Sub-type Fully published paper
DOI 10.1109/PERCOMW.2015.7133990
Open Access Status Not Open Access
ISBN 9781479984251
Start page 38
End page 43
Total pages 6
Language eng
Abstract/Summary The rapid market shift to multi-functional mobile devices has created an opportunity to support activity recognition using the on-board sensors of these devices. Over the last decade, many activity recognition approaches have been proposed for various activities in different settings. Wearable sensors and augmented environments potentially have better accuracy, however performing activity recognition on user mobile devices has also attracted significant attention. This is because of less requirements on the environments and easier application deployment. Many solutions have been proposed by academia, but practical use is limited to testbed experiments. In 2013, Google released an activity recognition service on Android, putting this technology to the test. With its enormous market share, the impact is significant. In this paper, we present a systematic evaluation of this activity recognition service and share the lesson learnt. Through our experiments, we found scenarios in which the recognition accuracy was barely acceptable. To improve its accuracy, we developed ARshell in which we apply a Markov smoother to post-process the results generated by the recognition service. Our evaluation experiments show significant improvement in accuracy when compared to the original results. As a contribution to the community, we open-sourced ARshell on GitHub for application developers who are interested in this activity recognition service.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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