Abstract
Sensor based activity recognition (AR) has gained extensive attention in recent years due to the ubiquitous presence of smart devices, such as smartphones and smartwatches. One of the major challenges posed by AR is to reliably recognize the current activity, when a given window of time series data contains several activities. Most of the traditional AR methods assume the entire window corresponds to a single activity, which may cause high error rate in activity recognition. To overcome this challenge, we propose a Weighted Min-max Activity Recognition Model (WMARM), which reliably predicts the current activity by finding an optimal partition of the time series matching the occurred activities. WMARM can handle the time series containing an arbitrary number of activities, without having any prior knowledge about the number of activities. We devise an efficient dynamic programming algorithm that solves WMARM in \(\mathcal {O}(n^2)\) time complexity, where n is the length of the window. Extensive experiments conducted on 5 real datasets demonstrate about 10%–30% improvement on accuracy of WMARM compared to the state-of-the-art methods.
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References
Bellman, R.: On the approximation of curves by line segments using dynamic programming. Commun. ACM 4(6), 284 (1961)
Hemminki, S., Nurmi, P., Tarkoma, S.: Accelerometer-based transportation mode detection on smartphones. In: Proceedings of Conference on Embedded Networked Sensor Systems, p. 13 (2013)
Himberg, J., Korpiaho, K., Mannila, H., Tikanmaki, J., Toivonen, H.T.: Time series segmentation for context recognition in mobile devices. In: Proceedings of International Conference on Data Mining, pp. 203–210 (2001)
Jackson, B., Scargle, J.D., Barnes, D., Arabhi, S., Alt, A., Gioumousis, P., Gwin, E., Sangtrakulcharoen, P., Tan, L., Tsai, T.T.: An algorithm for optimal partitioning of data on an interval. Signal Proces. Lett. 12(2), 105–108 (2005)
Kawaguchi, N., Ogawa, N., Iwasaki, Y., Kaji, K., Terada, T., Murao, K., Inoue, S., Kawahara, Y., Sumi, Y., Nishio, N.: HASC challenge: gathering large scale human activity corpus for the real-world activity understandings. In: Proceedings of Augmented Human International Conference, p. 27 (2011)
Keogh, E., Chu, S., Hart, D., Pazzani, M.: Segmenting time series: a survey and novel approach. Data Min. Time Ser. Databases 57, 1–22 (2004)
Khan, A.M., Lee, Y.K., Lee, S.Y., Kim, T.S.: A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer. Trans. Inf. Technol. Biomed. 14(5), 1166–1172 (2010)
Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. SigKDD Explor. Newsl. 12(2), 74–82 (2011)
Levy-leduc, C., Harchaoui, Z.: Catching change-points with lasso. In: Proceedings of Advances in Neural Information Processing Systems, pp. 617–624 (2008)
Lockhart, J.W., Pulickal, T., Weiss, G.M.: Applications of mobile activity recognition. In: Proceedings of Conference on Ubiquitous Computing, pp. 1054–1058 (2012)
Nguyen, T., Gupta, S.K., Venkatesh, S., Phung, D.Q.: A bayesian nonparametric framework for activity recognition using accelerometer data. In: Proceedings of International Conference on Pattern Recognition, pp. 2017–2022 (2014)
Reddy, S., Mun, M., Burke, J., Estrin, D., Hansen, M., Srivastava, M.: Using mobile phones to determine transportation modes. Trans. Sens. Netw. (TOSN) 6(2), 13 (2010)
Rednic, R., Gaura, E., Kemp, J., Brusey, J.: Fielded autonomous posture classification systems: design and realistic evaluation. In: Proceedings of ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, pp. 635–640 (2013)
Reyes-Ortiz, J.L., Oneto, L., Samà, A., Parra, X., Anguita, D.: Transition-aware human activity recognition using smartphones. Neurocomputing 171, 754–767 (2016)
Rosman, G., Volkov, M., Feldman, D., Fisher III, J.W., Rus, D.: Coresets for k-segmentation of streaming data. In: Proceedings of Advances in Neural Information Processing Systems, pp. 559–567 (2014)
Sak, H., Senior, A.W., Beaufays, F.: Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: Proceedings of Interspeech, pp. 338–342 (2014)
Tseng, V.S., Chen, C.H., Huang, P.C., Hong, T.P.: Cluster-based genetic segmentation of time series with dwt. Pattern Recogn. Lett. 30(13), 1190–1197 (2009)
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Cheng, W., Erfani, S., Zhang, R., Kotagiri, R. (2017). Accurate Recognition of the Current Activity in the Presence of Multiple Activities. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10235. Springer, Cham. https://doi.org/10.1007/978-3-319-57529-2_4
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DOI: https://doi.org/10.1007/978-3-319-57529-2_4
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