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Evaluating Machine Learning Techniques for Activity Classification in Smart Home Environments
Abstract:With the widespread adoption of the Internet-connected devices, and with the prevalence of the Internet of Things (IoT) applications, there is an increased interest in machine learning techniques that can provide useful and interesting services in the smart home domain. The areas that machine learning techniques can help advance are varied and ever-evolving. Classifying smart home inhabitants’ Activities of Daily Living (ADLs), is one prominent example. The ability of machine learning technique to find meaningful spatio-temporal relations of high-dimensional data is an important requirement as well. This paper presents a comparative evaluation of state-of-the-art machine learning techniques to classify ADLs in the smart home domain. Forty-two synthetic datasets and two real-world datasets with multiple inhabitants are used to evaluate and compare the performance of the identified machine learning techniques. Our results show significant performance differences between the evaluated techniques. Such as AdaBoost, Cortical Learning Algorithm (CLA), Decision Trees, Hidden Markov Model (HMM), Multi-layer Perceptron (MLP), Structured Perceptron and Support Vector Machines (SVM). Overall, neural network based techniques have shown superiority over the other tested techniques.
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 L. Bartram, J. Rodgers, and R. Woodbury, “Smart homes or smart occupants? supporting aware living in the home,” Human-Computer Interaction–INTERACT 2011, pp. 52–64, 2011.
 B. Minor, J. R. Doppa, and D. J. Cook, “Data-driven activity prediction: Algorithms, evaluation methodology, and applications,” in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015, pp. 805–814.
 I. Fatima, M. Fahim, Y.-K. Lee, and S. Lee, “Effects of smart home dataset characteristics on classifiers performance for human activity recognition,” in Computer Science and its Applications. Springer, 2012, pp. 271–281.
 E. M. Tapia, S. S. Intille, and K. Larson, “Activity recognition in the home using simple and ubiquitous sensors,” in Pervasive, vol. 4. Springer, 2004, pp. 158–175.
 E. Thomaz, T. Pl¨otz, I. Essa, and G. D. Abowd, “Interactive techniques for labeling activities of daily living to assist machine learning,” in Proceedings of Workshop on Interactive Systems in Healthcare, 2011.
 S. T. M. Bourobou and Y. Yoo, “User activity recognition in smart homes using pattern clustering applied to temporal ann algorithm,” Sensors, vol. 15, no. 5, pp. 11 953–11 971, 2015.
 D. J. Cook, M. Youngblood, E. O. Heierman, K. Gopalratnam, S. Rao, A. Litvin, and F. Khawaja, “Mavhome: An agent-based smart home,” in Pervasive Computing and Communications, 2003.(PerCom 2003). Proceedings of the First IEEE International Conference on. IEEE, 2003, pp. 521–524.
 C. Cortes and V. Vapnik, “Support-vector networks,” Machine learning, vol. 20, no. 3, pp. 273–297, 1995.
 F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
 A. Fleury, M. Vacher, and N. Noury, “Svm-based multimodal classification of activities of daily living in health smart homes: sensors, algorithms, and first experimental results,” IEEE transactions on information technology in biomedicine, vol. 14, no. 2, pp. 274–283, 2010.
 L. E. Baum and T. Petrie, “Statistical inference for probabilistic functions of finite state markov chains,” The annals of mathematical statistics, vol. 37, no. 6, pp. 1554–1563, 1966.
 H. Alemdar, H. Ertan, O. D. Incel, and C. Ersoy, “Aras human activity datasets in multiple homes with multiple residents,” in Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 2013, pp. 232–235.
 M. Prossegger and A. Bouchachia, “Multi-resident activity recognition using incremental decision trees,” in Adaptive and Intelligent Systems. Springer, 2014, pp. 182–191.
 G. Manogaran and D. Lopez, “Health data analytics using scalable logistic regression with stochastic gradient descent,” International Journal of Advanced Intelligence Paradigms, vol. 8, no. 2, 2017.
 Y. Freund and R. E. Schapire, “A desicion-theoretic generalization of on-line learning and an application to boosting,” in European conference on computational learning theory. Springer, 1995, pp. 23–37.
 B. Logan and J. Healey, “Sensors to detect the activities of daily living,” in Engineering in Medicine and Biology Society, 2006. EMBS’06. 28th Annual International Conference of the IEEE. IEEE, 2006, pp. 5362–5365.
 J. Hawkins and S. Ahmad, “Why neurons have thousands of synapses, a theory of sequence memory in neocortex,” Frontiers in Neural Circuits, vol. 10, p. 23, 2016.
[Online]. Available: https://www.frontiersin.org/article/10.3389/fncir.2016.00023.
 J. Hawkins, S. Ahmad, S. Purdy, and A. Lavin, “Biological and machine intelligence (bami),” 2016, initial online release 0.4. (Online). Available: http://numenta.com/biological-and-machine-intelligence/.
 R. ˇSkoviera and I. Bajla, “Image classification based on hierarchical temporal memory and color features,” Slovak Academy of Sciences, MEASUREMENT, 2013.
 I. Arel, D. C. Rose, and T. P. Karnowski, “Deep machine learning-a new frontier in artificial intelligence research (research frontier),” IEEE computational intelligence magazine, vol. 5, no. 4, pp. 13–18, 2010.
 F. D. S. Webber, “Semantic folding theory and its application in semantic fingerprinting,” arXiv preprint arXiv:1511.08855, 2015.
 M. Otahal and O. Stepankova, “Anomaly detection with cortical learning algorithm for smart homes,” SMART HOMES, vol. 20144, p. 24.
 F. Rosenblatt, “Principles of neurodynamics. perceptrons and the theory of brain mechanisms,” Cornell Aeronautical Lab Inc Buffalo NY, Tech. Rep., 1961.
 M. Collins, “Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms,” in Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10. Association for Computational Linguistics, 2002, pp. 1–8.
 L. Zhu, Y. Chen, X. Ye, and A. Yuille, “Structure-perceptron learning of a hierarchical log-linear model,” in Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on. IEEE, 2008, pp. 1–8.
 H. Fang and L. He, “Bp neural network for human activity recognition in smart home,” in Computer Science & Service System (CSSS), 2012 International Conference on. IEEE, 2012, pp. 1034–1037.
 Y. Bengio, P. Simard, and P. Frasconi, “Learning long-term dependencies with gradient descent is difficult,” IEEE transactions on neural networks, vol. 5, no. 2, pp. 157–166, 1994.
 S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997.
 F. J. Ord´o˜nez and D. Roggen, “Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition,” Sensors, vol. 16, no. 1, p. 115, 2016.
 D. Roggen, A. Calatroni, M. Rossi, T. Holleczek, K. F¨orster, G. Tr¨oster, P. Lukowicz, D. Bannach, G. Pirkl, A. Ferscha et al., “Collecting complex activity datasets in highly rich networked sensor environments,” in Networked Sensing Systems (INSS), 2010 Seventh International Conference on. IEEE, 2010, pp. 233–240.
 P. Zappi, C. Lombriser, T. Stiefmeier, E. Farella, D. Roggen, L. Benini, and G. Troster, “Activity recognition from on-body sensors: accuracy-power trade-off by dynamic sensor selection,” Lecture Notes in Computer Science, vol. 4913, p. 17, 2008.
 Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.
 M. Zeng, L. T. Nguyen, B. Yu, O. J. Mengshoel, J. Zhu, P. Wu, and J. Zhang, “Convolutional neural networks for human activity recognition using mobile sensors,” in Mobile Computing, Applications and Services (MobiCASE), 2014 6th International Conference on. IEEE, 2014, pp. 197–205.
 N. Alshammari, T. Alshammari, M. Sedky, J. Champion, and C. Bauer, “Openshs: Open smart home simulator,” Sensors, vol. 17, no. 5, 2017.
 D. Cook, M. Schmitter-Edgecombe, A. Crandall, C. Sanders, and B. Thomas, “Collecting and disseminating smart home sensor data in the casas project,” in Proceedings of the CHI Workshop on Developing Shared Home Behavior Datasets to Advance HCI and Ubiquitous Computing Research, 2009, pp. 1–7.
 K. Bouchard, A. Ajroud, B. Bouchard, and A. Bouzouane, “Simact: a 3d open source smart home simulator for activity recognition,” Advances in Computer Science and Information Technology, pp. 524–533, 2010.
 J. Synnott, C. Nugent, and P. Jeffers, “Simulation of smart home activity datasets,” Sensors, vol. 15, no. 6, pp. 14 162–14 179, 2015.
 F. Chollet et al., “Keras,” https://github.com/fchollet/keras, 2015.
 K. Gopalratnam and D. J. Cook, “Active lezi: An incremental parsing algorithm for sequential prediction,” International Journal on Artificial Intelligence Tools, vol. 13, no. 04, pp. 917–929, 2004.