Abstract
In recent years, mobile devices (e.g., smartphones, tablets and etc.) equipped with various inertial sensors have been increasingly popular in daily life, and a large number of mobile applications have been developed based on such built-in inertial sensors. In particular, many of these applications, such as healthcare, navigation, and etc., rely on the knowledge of whether a user is walking or not, so that walking detection thus has attained much attention. This paper deals with walking detection by using the gyroscope of any commercial off-the-shelf (COTS) smartphone, which can be placed at different positions of the user. Inspired by the fact that the walking activity often results in notable features in the frequency domain, we propose a novel algorithm based on fast Fourier transformation (FFT) to identify the walking activity of a user who may perform various activities and may hold the smartphone in different manners. A thorough experiment involving three testers and multiple activities is carried out and confirms that the proposed algorithm is superior to the existing well-known counterparts.
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References
Bahl, P., Padmanabhan, V.N.: RADAR: an in-building RF-based user location and tracking system. In: Proceedings of Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2000, pp. 775–784. IEEE (2000)
Das, S., Green, L.T., Perez, B.: Detecting user activities using the accelerometer on Android smartphones, the team for research in ubiquitous secure technology, TRUSTREU Carnefie Mellon University, pp. 1–10 (2010)
Zou, H., Huang, B., Lu, X., Jiang, H., Xie, L.: Standardizing location fingerprints across heterogeneous mobile devices for indoor localization. In: IEEE Wireless Communications and Networking Conference (WCNC), Doha, Qatar, pp. 503–508 (2016)
Zhao, H., Huang, B., Jia, B.: Applying kriging interpolation for WiFi fingerprinting based indoor positioning systems. In: IEEE Wireless Communications and Networking Conference (WCNC), Doha, Qatar, pp. 1822–1827 (2016)
Zou, H., Huang, B., Lu, X., Jiang, H., Xie, L.: A robust indoor positioning system based on the procrustes analysis and weighted extreme learning machine. In: IEEE Transactions on Wireless Communications, vol. 15, no. 2, pp. 1252–1266 (2016)
Foxlin, E.: Pedestrian tracking with shoe-mounted inertial sensors. Comput. Graph. Appl. 25(6), 38–46 (2005). IEEE
Huang, B., Qi, G., Yang, X., Zhao, L., Zou, H.: Exploiting cyclic features of walking for pedestrian dead reckoning with unconstrained smartphones. In: 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016, Heidelberg, Germany, pp. 374–385 (2016)
Yang, X., Huang, B., Miao, Q.: A step-wise algorithm for heading estimation via a smartphone. In: 28th Chinese Control and Decision Conference (CCDC), China, pp. 4711–4715 (2016)
Yang, X., Huang, B.: An accurate step detection algorithm using unconstrained smartphones. In: 27th Chinese Control and Decision Conference (CCDC), Qingdao, China, pp. 5702–5707 (2015)
Brajdic, A., Harle, R.: Walk detection and step counting on unconstrained smartphones. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 225–234. ACM (2013)
Goyal, P., Ribeiro, V.J., Saran, H.: Strap-down pedestrian dead-reckoning system. In: Indoor Positioning and Indoor Navigation (IPIN), pp. 1–7. IEEE (2011)
Rai, A., Chintalapudi, K.K., Padmanabhan, V.N.: Zee: zero-effort crowdsourcing for indoor localization. In: Proceedings of the 18th Annual International Conference on Mobile Computing and Networking. ACM (2012)
Barralon, P., Vuillerme, N., Noury, N.: Walk detection with a kinematic sensor: frequency and wavelet comparison. In: 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2006, pp. 1711–1714. IEEE (2006)
Nickel, C., Brandt, H., Busch, C.: Benchmarking the performance of SVMs and HMMs for accelerometer-based biometric gait recognition. In: 2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 281–286. IEEE (2011)
Choe, B.W., Min, J.-K., Cho, S.-B.: Online gesture recognition for user interface on accelerometer built-in mobile phones. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds.) ICONIP 2010. LNCS, vol. 6444, pp. 650–657. Springer, Heidelberg (2010). doi:https://doi.org/10.1007/978-3-642-17534-3_80
Dargie, W.: Analysis of time and frequency domain features of accelerometer measurements. In: Proceedings of 18th Internatonal Conference on Computer Communications and Networks, ICCCN 2009, pp. 1–6. IEEE (2009)
Henriksen, M., Lund, H., Moe-Nilssen, R.: Test-retest reliability of trunk accelerometric gait analysis. Gait posture 19(3), 288–297 (2004)
Acknowledgments
This work is supported by the National Natural Science Foundation of China under Grants 61461037 and 41401519, the Natural Science Foundation of Inner Mongolia Autonomous Region of China under Grant 2014MS0604, and the “Grassland Elite” Project of the Inner Mongolia Autonomous Region under Grant CYYC5016.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Qi, G., Huang, B. (2018). Walking Detection Using the Gyroscope of an Unconstrained Smartphone. In: Chen, Q., Meng, W., Zhao, L. (eds) Communications and Networking. ChinaCom 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 210. Springer, Cham. https://doi.org/10.1007/978-3-319-66628-0_51
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DOI: https://doi.org/10.1007/978-3-319-66628-0_51
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