Document Type : Original Article
Authors
Faculty of Computer Engineering and Information Technology, Shahrood University of Technology, Shahrood, Iran
Abstract
Smart homes enable many people, especially the elderly and patients, to live alone and maintain their independence and comfort. The realization of this goal depends on monitoring all activities in the house to report any observed anomaly immediately to their relatives or nurses. Anomaly detection in smart homes, just by existing data, is not an easy task. In this work, we train a recurrent network with raw outputs of binary sensors, including motion and door sensors, to predict which sensor will be switched on/off in the next event, and how long this on/off mode will last. Then, using Beam Search, we extend this event into k sequences of consecutive events to determine the possible range of upcoming activities. The error of this prediction i.e., the distance between these possible sequences and the real string of events is evaluated using several innovative methods for measuring the spatio-temporal similarity of the sequences. Modeling this error as a Gaussian distribution allows to assess the likelihood of anomaly scores. The input sequences that are ranked higher than a certain threshold will be considered as abnormal activities. The results of the experiments showed that this method enables the detection of abnormal activities with desirable accuracy.
Keywords
Main Subjects
- Withanage, C., R. Ashok, C. Yuen, and K. Otto, 2014. A comparison of the popular home automation technologies, In 2014 IEEE Innov. Smart Grid Technol. - Asia (ISGT ASIA), pp: 600–605.
- Zhou, B., W. Li, K.W. Chan, Y. Cao, Y. Kuang, X. Liu and X. Wang, 2016. Smart home energy management systems: Concept, configurations, and scheduling strategies. Renewable and Sustainable Energy Reviews, 61: 30–40.
- Majumder, S., E. Aghayi, M. Noferesti, H. Memarzadeh-Tehran, T. Mondal, Z. Pang and M.J. Deen, 2017. Smart Homes for Elderly Healthcare-Recent Advances and Research Challenges.
- Dahmen, J., B.L. Thomas, D.J. Cook and X. Wang, 2017. Activity learning as a foundation for security monitoring in smart homes. Sensors (Switzerland), 17(4): 1–17.
- Eisa, S. and A. Moreira, 2017. A behaviour monitoring system (BMS) for ambient assisted living. Sensors (Switzerland), 17(9):.
- Chandola, V., A. Banerjee, and V. Kumar, 2009. Anomaly detection. ACM Computing Surveys, 41(3): 1–58.
- Hayes, M.A. and M.A.M. Capretz, 2014. Contextual anomaly detection in big sensor data. Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014, 64–71.
- Han, J., M. Kamber, and J. Pei, 2011. Data Mining: Concepts and Techniques. 3rd ed. Morgan Kaufmann Publishers Inc.
- Grubbs, F.E., 1969. Procedures for Detecting Outlying Observations in Samples. Technometrics, 11(1): 1–21.
- Enderlein, G., 1987. Hawkins, D. M.: Identification of Outliers. Chapman and Hall, London – New York 1980, 188 S., £ 14, 50. Biometrical Journal, 29(2): 198.
- Ma, J. and S. Perkins, 2003. Time-series novelty detection using one-class support vector machines, In Proc. Int. Jt. Conf. Neural Networks, 2003., IEEE, pp: 1741–1745.
- Tavares Ferreira, E.W., G. Arantes Carrijo, R. de Oliveira and N. Virgilio de Souza Araujo, 2011. Intrusion Detection System with Wavelet and Neural Artifical Network Approach for Networks Computers. IEEE Latin America Transactions, 9(5): 832–837.
- Depren, O., M. Topallar, E. Anarim, and M.K. Ciliz, 2005. An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks.
- Pimentel, M.A.F., D.A. Clifton, L. Clifton, and L. Tarassenko, 2014. A review of novelty detection. Signal Processing, 99: 215–249.
- Ordonez, F.J., P. de Toledo and A. Sanchis, 2015. Sensor-based Bayesian detection of anomalous living patterns in a home setting. Personal and Ubiquitous Computing, 19(2): 259–270.
- Hela, S., B. Amel, and R. Badran, 2018. Early anomaly detection in smart home: A causal association rule-based approach. Artificial Intelligence in Medicine, (November 2017):
- Monekosso, D.N. and P. Remagnino, 2010. Behavior analysis for assisted living. IEEE Transactions on Automation Science and Engineering, 7(4): 879–886.
- Forkan, A.R.M., I. Khalil, Z. Tari, S. Foufou and A. Bouras, 2015. A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living. Pattern Recognition, 48(3): 628–641.
- Yuan, B. and J. Herbert, 2014. Context-aware Hybrid Reasoning Framework for Pervasive Healthcare. Personal Ubiquitous Comput., 18(4): 865–881.
- Novak, M., M. Binas and F. Jakab, 2012. Unobtrusive anomaly detection in presence of elderly in a smart-home environment. Proceedings of 9th International Conference, ELEKTRO 2012, (June 2016): 341–344.
- Moshtaghi, M., I. Zukerman, and R. Andrew Russell, 2015. Statistical models for unobtrusively detecting abnormal periods of inactivity in older adults.
- Paudel, R., W. Eberle, and L.B. Holder, 2018. Anomaly Detection of Elderly Patient Activities in Smart Homes using a Graph-Based Anomaly Detection of Elderly Patient Activities in Smart Homes using a Graph-Based Approach. (July):
- Jakkula, V. and D.J. Cook, 2011. Detecting Anomalous Sensor Events in Smart Home Data for Enhancing the Living Experience. AAAI Workshop on Artificial Intelligence and Smarter Living The Conquest of Complexity, (June 2015): 33–37.
- Lipton, Z.C., J. Berkowitz and C. Elkan, 2015. A Critical Review of Recurrent Neural Networks for Sequence Learning. 1–38.
- Hochreiter, S. and J. Urgen Schmidhuber, 1997. Long Short-Term Memory. Neural Computation, 9(8): 1735–1780.
- Gers, F.A. and J. Schmidhuber, 2000. Recurrent nets that time and count, In Proc. IEEE-INNS-ENNS Int. Jt. Conf. Neural Networks. IJCNN 2000. Neural Comput. New Challenges Perspect. New Millenn., IEEE, pp: 189–194 vol.3.
- Cho, K., B. van Merriënboer, C. Gulcehre, F. Bougares, H. Schwenk and Y. Bengio, 2014. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.
- Chung, J., C. Gulcehre, K. Cho and Y. Bengio, 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling, In NIPS 2014 Work. Deep Learn. December 2014,
- Caruana, R., 1997. Multitask Learning. Machine Learning, 28(1): 41–75.
- Collobert, R., R. Collobert and J. Weston, 2008. A unified architecture for natural language processing: Deep neural networks with multitask learning. 160--167.
- Kline, D., 2004. Methods for Multi-Step Time Series Forecasting with Neural Networks.
- Reddy, D.R.( D. of C.S., 1977. Speech Understanding Systems: A Summary of Results of the Five-Year Research Effort at CMU.
- Freitag, M., and Y. Al-Onaizan, 2017. Beam Search Strategies for Neural Machine Translation.
- Park, K., Y. Lin, V. Metsis, Z. Le and F. Makedon, 2010. Abnormal human behavioral pattern detection in assisted living environments. Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments - PETRA ’10.
- Malhotra, P., L. Vig, G. Shroff, and P. Agarwal, Long Short Term Memory Networks for Anomaly Detection in Time Series.
- Cook, D., 2010. Learning Setting-Generalized Activity Models for Smart Spaces.
- Ye, J., G. Stevenson and S. Dobson, 2016. Detecting abnormal events on binary sensors in smart home environments. Pervasive and Mobile Computing, 33: 32–49.
- Shin, J.H., B. Lee and K.S. Park, 2011. Detection of abnormal living patterns for elderly living alone using support vector data description. IEEE Transactions on Information Technology in Biomedicine : A Publication of the IEEE Engineering in Medicine and Biology Society, 15(3): 438–448.
- Fawcett, T., 2006. An introduction to ROC analysis. Pattern Recognition Letters, 27(8): 861–874.
- Goldstein, M., M. Goldstein, and S. Uchida, 2016. A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data. PLoS ONE, (April): 1–31.
- Emmott, A., S. Das, T. Dietterich, A. Fern and W.-K. Wong, 2015. A Meta-Analysis of the Anomaly Detection Problem.
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