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On Distance Mapping from non-Euclidean Spaces to Euclidean Spaces Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Human Activity Recognition Using Recurrent Neural Networks

Authors : Deepika Singh, Erinc Merdivan, Ismini Psychoula, Johannes Kropf, Sten Hanke, Matthieu Geist, Andreas Holzinger

Published in: Machine Learning and Knowledge Extraction

Publisher: Springer International Publishing

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Abstract

Human activity recognition using smart home sensors is one of the bases of ubiquitous computing in smart environments and a topic undergoing intense research in the field of ambient assisted living. The increasingly large amount of data sets calls for machine learning methods. In this paper, we introduce a deep learning model that learns to classify human activities without using any prior knowledge. For this purpose, a Long Short Term Memory (LSTM) Recurrent Neural Network was applied to three real world smart home datasets. The results of these experiments show that the proposed approach outperforms the existing ones in terms of accuracy and performance.

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Literature
1.
Roecker, C., Ziefle, M., Holzinger, A.: Social inclusion in ambient assisted living environments: home automation and convenience services for elderly users. In: Proceedings of the International Conference on Artificial Intelligence (ICAI 2011), pp. 55–59. CSERA Press, New York (2011)
2.
Chen, L., Hoey, J., Nugent, C.D., Cook, D.J., Yu, Z.: Sensor-based activity recognition. IEEE Trans. Syst. Man Cybern., Part C (Applications and Reviews) 42, 790–808 (2012)
3.
Holzinger, A.: Introduction to machine learning and knowledge extraction (MAKE). Mach. Learn. Knowl. Extr. 1, 1–20 (2017)CrossRef
4.
Roggen, D., Cuspinera, L.P., Pombo, G., Ali, F., Nguyen-Dinh, L.-V.: Limited-Memory Warping LCSS for real-time low-power pattern recognition in wireless nodes. In: Abdelzaher, T., Pereira, N., Tovar, E. (eds.) EWSN 2015. LNCS, vol. 8965, pp. 151–167. Springer, Cham (2015). doi:10.​1007/​978-3-319-15582-1_​10
5.
Duong, T.V., Bui, H.H., Phung, D.Q., Venkatesh, S.: Activity recognition and abnormality detection with the switching hidden semi-markov model. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 838–845. IEEE (2005)
6.
Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 609–616. ACM (2009)
7.
Lee, H., Pham, P., Largman, Y., Ng, A.Y.: Unsupervised feature learning for audio classification using convolutional deep belief networks. In: Advances in Neural Information Processing Systems, pp. 1096–1104 (2009)
8.
Hinton, G., Deng, L., Yu, D., Dahl, G.E., Mohamed, A.R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T.N., et al.: Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Sig. Process. Mag. 29, 82–97 (2012)CrossRef
9.
Kasteren, T.L., Englebienne, G., Kröse, B.J.: Human activity recognition from wireless sensor network data: Benchmark and software. In: Chen, L. (ed.) Activity Recognition in Pervasive Intelligent Environments, pp. 165–186. Atlantis Press, Amsterdam (2011)CrossRef
10.
11.
Ye, J., Stevenson, G., Dobson, S.: Kcar: a knowledge-driven approach for concurrent activity recognition. Pervasive Mob. Comput. 19, 47–70 (2015)CrossRef
12.
Tapia, E.M., Intille, S.S., Larson, K.: Activity recognition in the home using simple and ubiquitous sensors. In: Ferscha, A., Mattern, F. (eds.) Pervasive 2004. LNCS, vol. 3001, pp. 158–175. Springer, Heidelberg (2004). doi:10.​1007/​978-3-540-24646-6_​10 CrossRef
13.
14.
Van Kasteren, T., Noulas, A., Englebienne, G., Kröse, B.: Accurate activity recognition in a home setting. In: Proceedings of the 10th International Conference on Ubiquitous Computing, pp. 1–9. ACM (2008)
15.
Wu, W., Dasgupta, S., Ramirez, E.E., Peterson, C., Norman, G.J.: Classification accuracies of physical activities using smartphone motion sensors. J. Med. Internet Res. 14, e130 (2012)CrossRef
16.
Zhu, Y., Nayak, N.M., Roy-Chowdhury, A.K.: Context-aware activity recognition and anomaly detection in video. IEEE J. Sel. Top. Sig. Proces. 7, 91–101 (2013)CrossRef
17.
Ribbe, M.W., Ljunggren, G., Steel, K., Topinkova, E., Hawes, C., Ikegami, N., Henrard, J.C., JÓNnson, P.V.: Nursing homes in 10 nations: a comparison between countries and settings. Age Ageing 26, 3–12 (1997)CrossRef
18.
Sundermeyer, M., Schlüter, R., Ney, H.: Lstm neural networks for language modeling. In: Interspeech, pp. 194–197 (2012)
19.
Sak, H., Senior, A., Beaufays, F.: Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: Fifteenth Annual Conference of the International Speech Communication Association (2014)
20.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)CrossRef
21.
Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with lstm. Neural Comput. 12, 2451–2471 (2000)CrossRef
22.
Gers, F.A., Schraudolph, N.N., Schmidhuber, J.: Learning precise timing with lstm recurrent networks. J. Mach. Learn. Res. 3, 115–143 (2002)MathSciNetMATH
23.
Zhang, S., Zheng, D., Hu, X., Yang, M.: Bidirectional long short-term memory networks for relation classification. In: PACLIC (2015)
24.
25.
Gal, Y., Ghahramani, Z.: Dropout as a bayesian approximation: representing model uncertainty in deep learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning (ICML), vol. 48, pp. 1050–1059. PMLR (2016)
Metadata
Title
Human Activity Recognition Using Recurrent Neural Networks
Authors
Deepika Singh
Erinc Merdivan
Ismini Psychoula
Johannes Kropf
Sten Hanke
Matthieu Geist
Andreas Holzinger
Copyright Year
2017
Book
Machine Learning and Knowledge Extraction

Print ISBN: 978-3-319-66807-9

Electronic ISBN: 978-3-319-66808-6

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-66808-6_18

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