Abstract
Population ageing is an issue that has encouraged the development of Ambient Intelligence systems to support elderly people to live autonomously at home longer. Some key aspects of these systems are the detection of behavior patterns and behavior profiles in their daily life. The information we can infer from these patterns could prove to be very valuable for monitoring the health status of a person, like to control deterioration of diseases or to provide personalized assistive services. In this paper we focus on the unsupervised learning techniques in health monitoring systems for elderly people, which has the advantage of not needing annotations. Collecting these is a tedious job and sometimes difficult to accomplish. We discuss the different existing approaches, identify some limitations and propose possible challenges and directions for future research.
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Bloom, D.E., Canning, D., Fink, G.: Implications of population ageing for economic growth. Oxford Review of Economic Policy 26(4), 583–612 (2010)
Aztiria, A., Izaguirre, A., Augusto, J.C.: Learning patterns in Ambient Intelligence environments: A Survey. Artificial Intelligence Review 34(1), 35–51 (2010)
Mahoney, F.I., Barthel, D.: Functional evaluation: the Barthel Index. Maryland State Medical Journal 14, 56–61 (1965)
Bikakis, A., Patkos, T., Antoniou, G., Plexousakis, D.: A Survey of Semantics-based Approaches for Context Reasoning in Ambient Intelligence. In: Mühlhäuser, M., Ferscha, A., Aitenbichler, E. (eds.) AmI 2007 Workshops. CCIS, vol. 11, pp. 14–23. Springer, Heidelberg (2008)
Stumpp, J., Anastasopoulou, P., Sghir, H., Hey, S.: Sensor Chest Strap Wirelessly Coupled with an e-Diary for Ambulatory Assessment of Psycho-Physiological Data. In: 2nd Biennial Conference of the Science of Ambulatory Assessment, Ann Arbor, Michigan (2011)
Tolstikov, A., Biswas, J., Tham, C.K., Yap, P.: Eating activity primitives detection - a step towards adl recognition. In: 10th IEEE International Conference on e–Health Networking, Applications and Service, HEALTHCOM 2008, pp. 35–41 (2008)
Hoey, J., Poupart, J., Boutilier, C., Mihailidis, A.: Semi-supervised learning of a POMDP model of Patient-Caregiver Interactions. In: International Joint Conference in Artificial Intelligence, Workshop on Modeling Others from Observations, pp. 101–110 (2005)
Zhang, D., Gatica-Perez, D., Bengio, S.: Semi-supervised adapted HMMs for unusual event detection. In: Conference Computer Vision and Pattern Recognition, CVPR 2005, pp. 611–618 (2005)
Sandhu, J.S., Agogino, A.M., Agogino, A.K.: Wireless Sensor Networks for Commercial Lighting Control: Decision Making with Multi-agent Systems. In: Association for Advancement of Artificial Intelligence Conference, Sensor Networks Workshop, San Jose, pp. 88–92 (2004)
Monekosso, D.N., Remagnino, P.: Anomalous behaviour detection: supporting independent living. In: Monekosso, D.N., Remagnino, P., Kuno, Y. (eds.) Ambient Intelligence Techniques and Applications, Advanced Information and Knowledge Processing, pp. 33–48. Springer, London (2009)
Barger, T.S., Brown, D.E., Alwan, M.: Health-status monitoring through analysis of behavioral patterns. IEEE Transactions on SMC-A 35, 22–27 (2005)
Cook, D.J., Youngblood, M., Heierman III, E.O., Gopalratnam, K., Rao, S., Litvin, A., Khawaja, F.: MavHome: An Agent-Based Smart Home. In: First IEEE International Conference on Pervasive Computing and Communications, PerCom 2003, pp. 521–524 (2003)
Rao, S., Cook, D.J.: Predicting Inhabitant Actions Using Action and Task Models with Application to Smart Homes. International Journals of Artificial Intel. Tools 13(1), 81–100 (2004)
Doctor, F., Hagras, H., Callaghan, V.: A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments. IEEE Transactions on Systems, Man, and Cybernetics, Part A 35(1), 55–65 (2005)
Wyatt, D., Philipose, M., Choudhury, T.: Unsupervised activity recognition using automatically mined common sense. In: 20th National Conference on Artificial Intelligence, Pittsburgh, Pennsylvania, pp. 21–27 (2005)
Robben, S., Krose, B.: Longitudinal Residential Ambient Monitoring: Correlating Sensor Data to Functional Health Status. In: 7th International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth, pp. 244–247 (2013)
Fisher, A.G.: Assessment of Motor and Process Skills, 6th edn. Development, Standardization, and Administration Manual, vol. 1. Three Star Press Inc., Fort Collins (2003)
Robben, S., Boot, M., Kanis, M., Kröse, B.: Identifying and Visualizing Relevant Deviations in Longitudinal Sensor Patterns for Care Professionals. In: 7th International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth, pp. 416–419 (2013)
Virone, G., Sixsmith, A.: Monitoring activity patterns and trends of older adults. In: 30th IEEE Engineering in Medicine and Biology Society, Microtechnologies in Medicine & Biology, pp. 2071–2074 (2008)
Yin, G., Bruckner, D.: Daily activity model for ambient assisted living. In: Camarinha-Matos, L.M. (ed.) DoCEIS 2011. IFIP AICT, vol. 349, pp. 197–204. Springer, Heidelberg (2011)
Mozer, M.C.: Lessons from an Adaptive Home. In: Cook, D.J., Das, S.K. (eds.) Smart Environments: Technologies, Protocols, and Applications. John Wiley & Sons, Inc., Hoboken (2005)
Rivera-Illingworth, F., Callaghan, V., Hagras, H.A.: Neural Network Agent Based Approach to Activity Detection in AmI Environments. In: IEE International Workshop on Intelligent Environments, pp. 92–99 (2005)
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Parada Otte, F.J., Rosales Saurer, B., Stork, W. (2013). Unsupervised Learning in Ambient Assisted Living for Pattern and Anomaly Detection: A Survey. In: O’Grady, M.J., et al. Evolving Ambient Intelligence. AmI 2013. Communications in Computer and Information Science, vol 413. Springer, Cham. https://doi.org/10.1007/978-3-319-04406-4_6
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DOI: https://doi.org/10.1007/978-3-319-04406-4_6
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