Human behavior prediction for smart homes using deep learning

Proceedings - IEEE International Workshop on Robot and Human Interactive Communication

Volumn , Issue , 2013, Pages 173-179

  Choi, Sungjoon ^a   Kim, Eunwoo ^a   Oh, Songhwai ^a  

^a Seoul National University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

CONTRASTIVE DIVERGENCE; HUMAN BEHAVIORS; LEARNING METHODS; ONLINE LEARNING ALGORITHMS; PREDICTION ACCURACY; PREDICTION ALGORITHMS; RESTRICTED BOLTZMANN MACHINE; SMART HOME SERVICES;

ALGORITHMS; AUTOMATION; COMMUNICATION; INTELLIGENT BUILDINGS; ROBOTS; SENSORS;

FORECASTING;

EID: 84889567212     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ROMAN.2013.6628440     Document Type: Conference Paper

References (28)

1. P.Uhlenberg, International handbook of population aging.Springer, 2009, vol.1.
2. E.M.Tapia, S.S.Intille, and K.Larson, "Activity recognition in the, home using simple and ubiquitous sensors," Pervasive Computing, vol., 3001, pp.158-175, 2004.
3. E.D.Mynatt, I.Essa, and W.Rogers, "Increasing the opportunities, for aging in place," in Proc.of the ACM Conference on Universal, Usability.
4. N.Le Roux and Y.Bengio, "Deep belief networks are compact, universal approximators," Neural Computation, vol.22, no.8, pp., 2192-2207, 2010.
5. R.Aipperspach, E.Cohen, and J.Canny, "Modeling human behavior, from simple sensors in the home," Pervasive Computing, pp.337-348, 2006. (Pubitemid 44044767)
6. M.Mozer, "Lessons from an adaptive house," Ph.D.dissertation, University of Colorado, 2004.
7. Y.-J.Lin, H.A.Latchman, M.Lee, and S.Katar, "A power line, communication network infrastructure for the smart home," Wireless, Communications, vol.9, no.6, pp.104-111, 2002.
8. T.Shon and Y.Park, "Implementation of RF4CE-based wireless auto, configuration architecture for ubiquitous smart home," in Proc.of the, IEEE International Conference on Complex, Intelligent and Software, Intensive Systems, 2010, pp.779-783.
9. K.Park, E.Becker, J.K.Vinjumur, Z.Le, and F.Makedon, "Human, behavioral detection and data cleaning in assisted living environment, using wireless sensor networks," in Proc.of the ACM 2nd International, Conference on Pervasive Technologies Related to Assistive Environments, 2009, pp.7:1-7:8.
10. N.Kubota and K.Nishida, "Prediction of human behavior patterns, based on spiking neurons for a partner robot," in Proc.of the 15th, IEEE International Symposium on Robot and Human Interactive, Communication, 2006, pp.692-697.
11. S.-J.Blakemore and J.Decety, "From the perception of action to, the understanding of intention," Nature Reviews Neuroscience, vol.2, no.8, pp.561-567, 2001.
12. Y.Bengio, "Learning deep architectures for AI," Foundations and, TrendsR in Machine Learning, vol.2, no.1, pp.1-127, 2009.
13. H.Larochelle and Y.Bengio, "Classification using discriminative, restricted Boltzmann machines," in Proc.of the 25th International, Conference on Machine Learning, 2008, pp.536-543.
14. G.E.Hinton and R.R.Salakhutdinov, "Reducing the dimensionality, of data with neural networks," Science, vol.313, no.5786, pp.504-507, 2006. (Pubitemid 44148451)
15. Y.MarcAurelio Ranzato, L.Boureau, and Y.LeCun, "Sparse feature, learning for deep belief networks," in Advances in Neural Information, Processing Systems, vol.20, 2007, pp.1185-1192.
16. G.E.Hinton, "Training products of experts by minimizing contrastive, divergence," Neural Computation, vol.14, pp.1771-1800, 2002.
17. K.Cho, T.Raiko, and A.Ilin, "Parallel tempering is efficient for, learning restricted Boltzmann machines," in Proc.of the International, Joint Conference on Neural Networks, 2010, pp.3246-3253.
18. R.Salakhutdinov, "Learning deep Boltzmann machines using adaptive, mcmc," in Proc.of the International Conference on Machine Learning, vol.27, 2010.
19. Y.Bengio, O.Delalleau, and N.Le Roux, "The curse of highly, variable functions for local kernel machines," in Advances in Neural, Information Processing Systems, vol.18.MIT; 1998, 2006, pp.107-114.
20. A.Frank and A.Asuncion, "UCI machine learning repository." [Online].Available: http:-archive.ics.uci.edu-ml
21. L.Yann and C.Corinna.MNIST database of hand written digits.[Online]. Available: http:-yann.lecun.com-exdb-mnist-
22. M.A.Carreira-Perpinan and G.E.Hinton, "On contrastive divergence, learning," in Proc.of the Artificial Intelligence and Statistics, 2005, pp.33-40.
23. P.Hamel and D.Eck, "Learning features from music audio with deep, belief networks," in 11th International Society for Music Information, Retrieval Conference, 2010, pp.339-344.
24. M.F.Møller, "A scaled conjugate gradient algorithm for fast supervised, learning," Neural Networks, vol.6, no.4, pp.525-533, 1993.
25. D.R.Wilson and T.R.Martinesz, "The general inefficiency of batch, training for gradient descent learning," Neural Networks, vol.16, pp., 1429-1451, 2003. (Pubitemid 37386361)
26. C.-C.Chang and C.-J.Lin, "LIBSVM: A library for support vector, machines," ACM Transactions on Intelligent Systems and Technology, vol.2, pp.27:1-27:27, 2011.
27. Y.Bengio, P.Lamblin, D.Popovici, and H.Larochelle, "Greedy layerwise, training of deep networks," in Advances in Neural Information, Processing Systems, vol.19.MIT; 1998, 2007, pp.153-160.
28. K.Cho, T.Raiko, and A.Ilin, "Gaussian-bernoulli deep boltzmann, machine," in Advances in Neural Information Processing Systems, 2011.