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A novel orientation- and location-independent activity recognition method

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Abstract

The orientation and location of a mobile phone pose fundamental challenges to activity recognition (AR) in a device. Given that AR significantly affects recognition accuracy, in this study, we focus on eliminating the influence of orientation and location changes on AR. First, we propose an activity recognition framework, which is independent of orientation and location changes, to uniformly deal with the problem of orientation and location changes on AR. Second, a dynamic coordinate transformation approach on inertial sensor data is proposed. In this method, the data collected in different orientations are dynamically mapped to the reference coordinate system of a mobile phone. The classification on the mapped data can reach significantly higher accuracy than that on the original data. We design four sets of comparative experiments to verify the validity of the proposed method, and the results demonstrate its effectiveness. Third, the influence of the location changes of mobile phones on AR is eliminated through the location-specific AR method. The effectiveness of the proposed method is verified by two groups of contrast tests. Finally, a real-time AR system is implemented on an Android platform. Results demonstrate that the proposed method obtains valid recognition results despite various orientation and location changes.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (No. 91118008). We want to thank all the students who participated in our experiments in the National University of Defense Technology. They provided us many valuable comments and suggestions.

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Authors and Affiliations

  1. National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, China

    Dianxi Shi, Ran Wang, Yuan Wu, Xiaoyun Mo & Jing Wei

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  1. Dianxi Shi
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  2. Ran Wang
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  3. Yuan Wu
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  4. Xiaoyun Mo
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  5. Jing Wei
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Correspondence to Dianxi Shi.

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Shi, D., Wang, R., Wu, Y. et al. A novel orientation- and location-independent activity recognition method. Pers Ubiquit Comput 21, 427–441 (2017). https://doi.org/10.1007/s00779-017-1007-3

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