Abstract
As smartphone users have been increased, studies using mobile sensors on smartphone have been investigated in recent years. Activity recognition is one of the active research topics, which can be used for providing users the adaptive services with mobile devices. In this paper, an activity recognition system on a smartphone is proposed where the uncertain time-series acceleration signal is analyzed by using hierarchical hidden Markov models. In order to address the limitations on the memory storage and computational power of the mobile devices, the recognition models are designed hierarchy as actions and activities. We implemented the real-time activity recognition application on a smartphone with the Google android platform, and conducted experiments as well. Experimental results showed the feasibility of the proposed method.
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References
Corchado, E., Abraham, A., Carvalho, F.L.P.C.A.: Hybrid intelligent algorithms and applications. Information Science 180(14), 2633–2634 (2010)
Abraham, A., Corchado, E., Corchado, M.J.: Hybrid learning machines. Neurocomputing 72(13-15), 2729–2730 (2009)
Böhmer, M., Bauer, G., Krüger, A.: Exploring the design space of context-aware recommender systems that suggest mobile applications. In: 2nd Workshop on Context-Aware Recommender Systems (2010)
Bellotti, V., Begole, B., Chi, H.E., Ducheneaut, N., Fang, J., Isaacs, E., King, T., Newman, W.M., Partridge, K., Price, B., Rasmussen, P., Roberts, M., Schiano, J.D., Walendowski, A.: Activity-based serendipitous recommendations with the Magitti mobile leisure guide. In: CHI 2008 Proceedings, pp. 1157–1166 (2008)
Kwapisz, R.J., Weiss, M.G., Moore, A.S.: Activity recognition using cell phone accelerometers. In: 4th ACM SIGKDD International Workshop on Knowledge Discovery from Sensor Data (2010)
Longstaff, B., Reddy, S., Estrin, D.: Improving activity classification for health applications on mobile devices using active and semi-supervised learning. Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp. 1–7 (2010)
Maguire, D., Frisby, R.: Comparison of feature classification algorithm for activity recognition based on accelerometer and heart rate data. In: 9th. IT & T Conference (2009)
Gÿorbíró, N., Fábián, Á., Homány, G.: An activity recognition system for mobile phone. Mobile Netw. Appl. 14, 82–91 (2008)
Song, K.S., Jang, J., Park, S.: A phone for human activity recognition using triaxial acceleration sensor. In: Digest of Technical Papers - IEEE International Conference on Consumer Electronics (2008)
Zappi, P., Lombriser, C., Stiefmeier, T., Farella, E., Roggen, D., Benini, L., Tröster, G.: Activity recognition from on-body sensors: Accuracy-power trade-off by dynamic sensor selection. In: Verdone, R. (ed.) EWSN 2008. LNCS, vol. 4913, pp. 17–43. Springer, Heidelberg (2008)
Yang, J.-Y., Chen, Y.-P., Lee, G.-Y., Liou, S.-N., Wang, J.-S.: Activity recognition using one triaxial accelerometer: A neuro-fuzzy classifier with feature reduction. In: Ma, L., Rauterberg, M., Nakatsu, R. (eds.) ICEC 2007. LNCS, vol. 4740, pp. 395–400. Springer, Heidelberg (2007)
Ganti, K.R., Jayachandran, P., Abdelzaher, F.T., Stankovic, A.J.: Satire: a software architecture for smart attire. In: Proceedings of the 4th International Conference on Mobile Systems, Applications and Services, pp. 110–123 (2006)
Korb, B.K.: A Bayesian Artificial Intelligence. Chapman & Hall/CRC Computer Science & Data Analysi, Boca Raton (2004)
Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Yang, S.-I., Hong, J.-H., Cho, S.-B.: Activity recognition based on multi-modal sensors using dynamic Bayesian networks. J. KIISE: Computing Practices and Letters 15(1), 72–76 (2009)
Park, S., Aggarwal, K.J.: A hierarchical Bayesian network for event recognition of human actions and interactions. Multimedia Systems 10(9), 164–179 (2004)
Mengistu, T.K., Hannemann, M., Baum, T., Wendemuth, A.: Hierarchical HMM-based semantic concept labeling model, pp. 57–60. IEEE Computer Society Press, Los Alamitos (2008)
Wang, A.W.-H., Tung, C.-L.: Dynamic gesture recognition based on dynamic Bayesian networks. WSEAS Transactions on Business and Economics 4(11), 168–173 (2008)
Du, Y., Chen, F., Xu, W., Zhang, W.: Interacting activity recognition using hierarchical durational-state dynamic Bayesian network. In: Zhuang, Y.-t., Yang, S.-Q., Rui, Y., He, Q. (eds.) PCM 2006. LNCS, vol. 4261, pp. 185–192. Springer, Heidelberg (2006)
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Lee, YS., Cho, SB. (2011). Activity Recognition Using Hierarchical Hidden Markov Models on a Smartphone with 3D Accelerometer. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21219-2_58
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DOI: https://doi.org/10.1007/978-3-642-21219-2_58
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