Abstract
Traditionally, the automatic recognition of human activities is performed with supervised learning algorithms on limited sets of specific activities. This work proposes to recognize recurrent activity patterns, called routines, instead of precisely defined activities. The modeling of routines is defined as a metric learning problem, and an architecture, called SS2S, based on sequence-to-sequence models is proposed to learn a distance between time series. This approach only relies on inertial data and is thus non intrusive and preserves privacy. Experimental results show that a clustering algorithm provided with the learned distance is able to recover daily routines.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abid, A., Zou, J.: Autowarp: learning a warping distance from unlabeled time series using sequence autoencoders. arXiv preprint arXiv:1810.10107 (2018)
Aghabozorgi, S., Shirkhorshidi, A.S., Wah, T.Y.: Time-series clustering-a decade review. Inf. Syst. 53, 16–38 (2015)
Avci, A., Bosch, S., Marin-Perianu, M., Marin-Perianu, R., Havinga, P.: Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: a survey. In: ARCS, pp. 1–10. VDE (2010)
Berlemont, S., Lefebvre, G., Duffner, S., Garcia, C.: Class-balanced siamese neural networks. Neurocomputing 273, 47–56 (2018)
Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.S.: Fully-convolutional siamese networks for object tracking. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 850–865. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_56
Bohr, H.: Zur theorie der fastperiodischen funktionen. Acta Mathematica 46(1–2), 101–214 (1925)
Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., Shah, R.: Signature verification using a “siamese” time delay neural network. In: NIPS, pp. 737–744 (1994)
Chatzaki, C., Pediaditis, M., Vavoulas, G., Tsiknakis, M.: Human Daily Activity and Fall Recognition Using a Smartphone’s Acceleration Sensor. In: Röcker, C., O’Donoghue, J., Ziefle, M., Helfert, M., Molloy, W. (eds.) ICT4AWE 2016. CCIS, vol. 736, pp. 100–118. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62704-5_7
Cho, K., et al.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Cumin, J., Lefebvre, G., Ramparany, F., Crowley, J.L.: Human activity recognition using place-based decision fusion in smart homes. In: Brézillon, P., Turner, R., Penco, C. (eds.) CONTEXT 2017. LNCS (LNAI), vol. 10257, pp. 137–150. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57837-8_11
Esling, P., Agon, C.: Time-series data mining. ACM CSUR 45(1), 12 (2012)
Faraki, M., Harandi, M.T., Porikli, F.: Large-scale metric learning: a voyage from shallow to deep. IEEE TNNLS 29(9), 4339–4346 (2018)
Gehring, J., Auli, M., Grangier, D., Yarats, D., Dauphin, Y.N.: Convolutional sequence to sequence learning. In: ICML, pp. 1243–1252 (2017)
Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 453(7196), 779 (2008)
Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: CVPR, vol. 2, pp. 1735–1742. IEEE (2006)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural comput. 9(8), 1735–1780 (1997)
Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks-with an erratum note. Bonn, Germany: German National Research Center for Information Technology GMD Technical report 148(34), 13 (2001)
Kalpakis, K., Gada, D., Puttagunta, V.: Distance measures for effective clustering of arima time-series. In: IEEE ICDM, pp. 273–280. IEEE (2001)
Koestinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: CVPR, pp. 2288–2295. IEEE (2012)
Lally, P., Van Jaarsveld, C.H., Potts, H.W., Wardle, J.: How are habits formed: modelling habit formation in the real world. Eur. J. Soc. Psychol. 40(6), 998–1009 (2010)
LeCun, Y., Chopra, S., Hadsell, R., Ranzato, M., Huang, F.: A tutorial on energy-based learning. Predicting structured data 1, (2006)
Lin, J., Li, Y.: Finding structural similarity in time series data using bag-of-patterns representation. In: Winslett, M. (ed.) SSDBM 2009. LNCS, vol. 5566, pp. 461–477. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02279-1_33
Martin, R.J.: A metric for ARMA processes. IEEE Trans. Signal Process. 48(4), 1164–1170 (2000)
Müller, J., Thyagarajan, A.: Siamese recurrent architectures for learning sentence similarity. In: AAAI, pp. 2786–2792 (2016)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26(1), 43–49 (1978)
Salvador, S., Chan, P.: Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11(5), 561–580 (2007)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: NIPS, pp. 3104–3112 (2014)
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on Machine learning, pp. 1096–1103. ACM (2008)
Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. JMLR 10(Feb), 207–244 (2009)
Weiss, A., et al.: Does the evaluation of gait quality during daily life provide insight into fall risk? A novel approach using 3-day accelerometer recordings. Neurorehabilitation Neural Repair 27(8), 742–752 (2013)
Xi, X., Keogh, E., Shelton, C., Wei, L., Ratanamahatana, C.A.: Fast time series classification using numerosity reduction. In: Proceedings of the 23rd international conference on Machine learning, pp. 1033–1040. ACM (2006)
Yi, D., Lei, Z., Liao, S., Li, S.Z.: Deep metric learning for person re-identification. In: ICPR, pp. 34–39. IEEE (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Compagnon, P., Lefebvre, G., Duffner, S., Garcia, C. (2019). Routine Modeling with Time Series Metric Learning. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning. ICANN 2019. Lecture Notes in Computer Science(), vol 11728. Springer, Cham. https://doi.org/10.1007/978-3-030-30484-3_47
Download citation
DOI: https://doi.org/10.1007/978-3-030-30484-3_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30483-6
Online ISBN: 978-3-030-30484-3
eBook Packages: Computer ScienceComputer Science (R0)