Salisu Wada Yahaya
Ahmad Lotfi
ABSTRACT
Anomaly detection in Activities of Daily Living is a challenging task driven by the need to improve the quality of life and promote independent living of the increasing ageing population. There are many computational methodologies for detecting anomalies. They are mainly based on the concept of learning usual activities of daily living routines and detect abnormalities in it. However, they are limited by their inability to predict the actual cause of the anomaly. Understanding the cause of the anomalies can enable robust anomaly detection system to be built with a low rate of false alarms. This paper proposes a similarity measure approach for identifying the cause of anomalies in activities of daily living routine. The proposed approach is based on a pair-wise similarity measure of the features present in a dataset. Preliminary experiments conducted on both real and synthetic data achieve an excellent result with an overall accuracy of 96%.
- Damla Arifoglu and Abdelhamid Bouchachia. 2017. Activity Recognition and Abnormal Behaviour Detection with Recurrent Neural Networks. Procedia Computer Science 110 (2017), 86 -- 93.Google Scholar
Cross Ref
- D. J. Cook, A. S. Crandall, B. L. Thomas, and N. C. Krishnan. 2013. CASAS: A Smart Home in a Box. Computer 46, 7 (July 2013), 62--69. Google ScholarDigital Library
- Diane J. Cook and Narayanan Krishnan. 2014. Mining the home environment. Journal of Intelligent Information Systems 43, 3 (2014), 503--519. Google ScholarDigital Library
- L. G. Fahad and M. Rajarajan. 2015. Anomalies Detection in Smart-Home Activities. In The 14th International Conference on Machine Learning and Applications (ICMLA). IEEE, Miami, USA, 419--422.Google Scholar
- Abdur Rahim Mohammad Forkan, Ibrahim Khalil, Zahir Tari, Sebti Foufou, and Abdelaziz Bouras. 2015. A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living. Pattern Recognition 48, 3 (2015), 628 -- 641. Google ScholarDigital Library
- E. Hoque, R. F. Dickerson, S. M. Preum, M. Hanson, A. Barth, and J. A. Stankovic. 2015. Holmes: A Comprehensive Anomaly Detection System for Daily In-home Activities. In International Conference on Distributed Computing in Sensor Systems. IEEE, Fortaleza, Brazil, 40--51. Google ScholarDigital Library
- Vikramaditya Jakkula and Diane J. Cook. 2011. Detecting Anomalous Sensor Events in Smart Home Data for Enhancing the Living Experience. In Proceedings of the 7th AAAI Conference on Artificial Intelligence and Smarter Living: The Conquest of Complexity. AAAI Press, 33--37. Google ScholarDigital Library
- V. Jakkula, D. J. Cook, and A. S. Crandall. 2007. Temporal pattern discovery for anomaly detection in a smart home. In The 3rd IET International Conference on Intelligent Environments. IET, Ulm, Germany, 339--345.Google Scholar
- Daniel T. Larose. 2004. Discovering Knowledge in Data: An Introduction to Data Mining. Wiley-Interscience, New York, NY, USA. Google ScholarDigital Library
- Ahmad Lotfi, Caroline Langensiepen, Sawsan M. Mahmoud, and M. J. Akhlaghinia. 2012. Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behaviour. Journal of Ambient Intelligence and Humanized Computing 3, 3 (01 Sep 2012), 205--218.Google ScholarCross Ref
- M. Mahmud, M. S. Kaiser, A. Hussain, and S. Vassanelli. 2018. Applications of Deep Learning and Reinforcement Learning to Biological Data. IEEE Transactions on Neural Networks and Learning Systems 29, 6 (2018), 2063--2079.Google ScholarCross Ref
- Marek Novák, Frantisek Jakab, and Luis Lain. 2013. Anomaly Detection in User Daily Patterns in Smart-Home Environment. Journal of Selected Areas in Health Informatics (JSHI) 3, 6 (2013), 1--11.Google Scholar
- Marco A.F. Pimentel, David A. Clifton, Lei Clifton, and Lionel Tarassenko. 2014. A review of novelty detection. Signal Processing 99 (2014), 215 -- 249. Google ScholarDigital Library
- P. Rashidi and A. Mihailidis. 2013. A Survey on Ambient-Assisted Living Tools for Older Adults. IEEE Journal of Biomedical and Health Informatics 17, 3 (May 2013), 579--590.Google ScholarCross Ref
- D. J. Weller-Fahy, B. J. Borghetti, and A. A. Sodemann. 2015. A Survey of Distance and Similarity Measures Used Within Network Intrusion Anomaly Detection. IEEE Communications Surveys Tutorials 17, 1 (2015), 70--91.Google ScholarCross Ref
- Salisu Wada Yahaya, Caroline Langensiepen, and Ahmad Lotfi. 2019. Anomaly Detection in Activities of Daily Living Using One-Class Support Vector Machine. In Advances in Computational Intelligence Systems. Springer, 362--371.Google Scholar
- Salisu Wada Yahaya, Ahmad Lotfi, and Mufti Mahmud. 2019. A Consensus Novelty Detection Ensemble Approach for Anomaly Detection in Activities of Daily Living. Applied Soft Computing (2019).Google Scholar
Index Terms
- A similarity measure approach for identifying causes of anomaly in activities of daily living
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