Skip to main content

Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behaviour


In this paper, we have described a solution for supporting independent living of the elderly by means of equipping their home with a simple sensor network to monitor their behaviour. Standard home automation sensors including movement sensors and door entry point sensors are used. By monitoring the sensor data, important information regarding any anomalous behaviour will be identified. Different ways of visualizing large sensor data sets and representing them in a format suitable for clustering the abnormalities are also investigated. In the latter part of this paper, recurrent neural networks are used to predict the future values of the activities for each sensor. The predicted values are used to inform the caregiver in case anomalous behaviour is predicted in the near future. Data collection, classification and prediction are investigated in real home environments with elderly occupants suffering from dementia.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20


  • Ali R, ElHelw M, Atallah L, Lo B, Yang GZ (2008) Pattern mining for routine behaviour discovery in pervasive healthcare environments. In: Proceedings of the international conference on Information Technology and Applications in Biomedicine (ITAB), pp 241–244

  • Akhlaghinia MJ, Lotfi A, Langensiepen C, Sherkat N (2008) A fuzzy predictor model for the occupancy prediction of an intelligent inhabited environment, in 2008 IEEE 16th International Conference on Fuzzy Systems (FUZZ-IEEE), London, pp 939–946

  • Akhlaghinia MJ, Lotfi A, Langensiepen C, Sherkat N (2008) Occupant behaviour prediction in ambient intelligence computing environment. Int J Uncertain Syst 2(2):85–100

    Google Scholar 

  • Akhlaghinia MJ, Lotfi A, Langensiepen C (2010) Localising agents in multiple-occupant intelligent environments, in Proc. WCCI 2010 IEEE World Congress on Computational Intelligence, Barcelona, July 18-23, pp 2202–2208

  • Augusto JC (2008) Ambient Intelligence: basic concepts and applications, software and data technologies. Commun Comput Inf Sci 10:16–26

  • Barger T, Brown DE, Alwan M (2005) Health-status monitoring through analysis of behavioral patterns. IEEE Transactions on Systems, Man, and Cybernetics; Part A 35(1):22–27

    Google Scholar 

  • Boissy P, Choquette S, Hamel M, Noury N (2007) User-based motion sensing and fuzzy logic for automated fall detection in older adults. Telemed J e-Health 13:683–693

    Article  Google Scholar 

  • Callaghan V, Clarke G, Colley M, Hagras H, Chin JSY, Doctor F (2004) Inhabited intelligent environments. BT Technol J 22:233–247

    Article  Google Scholar 

  • Cardinaux F, Brownsell1 S, Hawley M, Bradley D (2008) Modelling of behavioural patterns for abnormality detection in the context of lifestyle reassurance”, Proceeding CIARP, Havana

  • Cash M (2004) At Home with AT (assistive technology), Research report available from:

  • Chandola V (2009) Arindam Banerjee and Vipin Kumar. Anomaly detection: a survey. ACM Computing Surveys (CSUR) 41(3)

  • Cook DJ (2007) Making sense of sensor data. IEEE Pervasive Comput 6:105–108

    Article  Google Scholar 

  • Devert A, Bredeche N, Schoenauer M (2008) Unsupervised learning of echo state networks: a case study in artificial embryogeny. Artificial Evolution 4926:278–290

    Google Scholar 

  • European Comission (2010) Europa Public Health [online]. [Accessed 16 Aug 2010]

  • Gustavsson A, Jonsson L, McShane R, Boada M, Wimo A, Zbrozek AS (2009) Willingness-to-pay for reductions in care need: estimating the value of informal care in Alzheimer’s disease. Int J Geriatr Psychiatry 25(6):622–632

    Google Scholar 

  • Hagras H (2007) Embedding computational intelligence in pervasive spaces. IEEE Pervasive Comput 6:85–89

    Article  Google Scholar 

  • Hamid R, Maddi S, Johnson A, Bobick A, Essa I, Isbell C (2009) A novel sequence representation for unsupervised analysis of human activities. Elsevier Science, Artificial Intelligence, vol 173(14). pp 1221–1244

    Google Scholar 

  • Helal A, King J, Bose R (2009) Assistive Environments for Successful Aging, In: Kameas D, Callagan V, Hagras H, Weber M, Minker W (eds) Advanced Intelligent Environments, Achilles, Springer, US, pp 1–26

  • Hellbach S, Straussl S, Eggert JP, Korner E, Gross1 HM (2008) Echo State Networks for Online Prediction of Movement Data—Comparing Investigations

  • Illingworth R, Callaghan V, Hagras H (2005) A Neural Network Agent Based Approach to Activity Detection in AmI Environments. IEE International Workshop, Intelligent Environments (IE05), Colchester, pp 1–12

  • Illingworth R, Callagha V, Hagras H (2006) Towards the Detection of Temporal Behavioural Patterns in Intelligent Environments. 2nd IET International Conference on Intelligent Environments, pp 119–125

  • Jaeger H, Haas H (2004) Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304(5667):78–80

    Google Scholar 

  • Jakkula V, Cook DJ, Crandall AS (2007) Temporal pattern discovery for anomaly detection in a smart home, 3rd IET International Conference on Intelligent Environments, pp 339–345

  • JustChecking (2010) Supporting Independence People with Dementia [online]. [Accessed 16 Aug 2010]

  • Kautz H, Etziono O, Fox D, Weld D (2003) Foundations of assisted cognition systems. Technical report, University of Washington, Department of Computer Science and Engineering

  • Kenner A (2008) Securing the elderly body: dementia, surveillance, and the politics of aging in place. Surveill Soc 5(3):252–269

    Google Scholar 

  • Keogh E, Lin J, Vlachos M, Gunopulos D (2004) Iterative incremental clustering of time series, In EDBT, pp 106–122

  • Lee MD, Reilly RE, Butavicius MA (2003) An empirical evaluation of chernoff faces, star glyphs, and spatial visualizations for binary data, In CRPITS 24: Proceedings of the Australian symposium on Information visualization, pp 110

  • Li H, Zhang Q, Duan P (2008) A novel one-pass neural network approach for activities recognition in intelligent environments. Proceedings of the 7th World Congress on Intelligent Control and Automation, pp 50–54

  • Liao TW (2005) Clustering of time series data a survey. Pattern Recognit 38:1857–1874

    MATH  Article  Google Scholar 

  • Mahmoud SM, Lotfi A, Sherkat N, Langensiepen C, Osman T (2009) Echo state network for occupancy prediction and pattern mining in intelligent environment, in Proceedings of the 5th International Conference on Intelligent Environments, Barcelona, pp 474–481

  • McCullagh PJ, Carswell W, Augusto JC (2009) State of the art on night-Time care of people with dementia. IET Assisted Living Conference, 24–25 March, London

  • Medjahed H, Istrate D, Boudy J, Dorizzi B (2009) A fuzzy logic system for home elderly people monitoring (EMUTEM), in 10th WSEAS International Conference on Fuzzy Systems (FS’09), pp 69–75

  • Monekosso DN, Remagnino P (2009) Anomalous behaviour detection: supporting independent living. In: Monekosso D, Remagnino P, Kuno Y (eds) Ambient intelligence techniques and applications, advanced information and knowledge processing, Springer, London, pp 33–48

  • Monekosso DN, Remagnino P (2009) Anomalous behavior detection: supporting independent living, intelligent environments, Advanced Information and Knowledge Processing, Springer, Poland

  • Nugent C, Mulvenna M, Moelaert F, Bergvall-Kareborn B, Meiland F, Craig D, Davies R, Reinersmann A, Hettinga M, Andersson A, Droes R, Bengtsson JE (2007) Home based assistive technologies for people with mild dementia, in 5th International Conference on Smart Homes and Health Telematics, pp 63–69

  • Obst O, Wang XR, Prokopenko M (2008) Using Echo State Networks for Anomaly Detection in Underground Coal Mines, Proceedings of the 7th international conference on Information processing in sensor networks

  • Orpwood R, Gibbs C, Adlam T, Faulkner R, Meeeahawatte D (2005) The design of smart homes for people with dementia—user-interface aspects. Univ Access Inf Soc 4:156–164

    Article  Google Scholar 

  • Osman N. Yogurtcu, Engin Erzin, Attila Gursoy (2006) Extracting gene regulation information from microarray time-series data using Hidden Markov Models, vol 4263. Lecture Notes in Computer Science, Springer, pp 144–153

  • Park SH, Lee JH, Song JW, Park TS (2009) Forecasting change directions for financial time series using Hidden Markov Model. vol 5589. Lecture Notes in Computer Science, Springer, pp 184–191

  • Sawai K, Yoshida M (2007) Algorithm to detect abnormal states of elderly persons for home monitoring. Syst Comput Jpn 38:34–42

    Article  Google Scholar 

  • Serna A, Pigot H, Rialle V (2007) Modeling the progression of Alzheimer’s disease for cognitive assistance in smart homes. User Model User Adapt Interact 17:415–438

    Article  Google Scholar 

  • Shi Z, Han M (2007) Support vector echo-state machine for chaotic time-series prediction. IEEE Transactions on Neural Networks 18(2):359–372

    Google Scholar 

  • Singla G, Cook DJ, Maureen Schmitter-Edgecombe (2010) Recognizing independent and joint activities among multiple residents in smart environments. J Ambient Intell Human Comput 1:57–63

    Google Scholar 

  • Skowronski MD, Harris JG (2006) Minimum mean squared error time series classification using an echo state network prediction model. In: Proceedings of the 2006 IEEE International Symposium on Circuits and Systems (ISCAS), Island of Kos, Greece, pp 3156–3159

  • Tapia DI, Abraham A, Corchado JM, Alonso RS (2010) Agents and ambient intelligence: case studies. J Ambient Intell Humaniz Comput 1(2):85–93

    Google Scholar 

  • Tapia EM, Intille SS, Larson K (2004) Activity Recognition in the Home using Simple and Ubiquitous Sensors. In: Ferscha A, Mattern F (eds) PERVASIVE, LNCS, vol 3001. Springer, Heidelberg, pp 158–175

  • Vrotsou K, Ellegard K, Cooper M (2007) Everyday life discoveries: mining and visualizing activity patterns in social science diary data, information visualization, 11th International Conference, pp 130–138

  • Wimo A, Winblad B, Jonsson L (2007) An estimate of the total worldwide societal costs of dementia in 2005. Alzheimers Dement 3(2):81–91

    Article  Google Scholar 

  • Yu X (2008) Approaches and principles of fall detection for elderly and patient, in 2008 10th International Conference on e-Health Networking, Applications and Services (Healthcom), pp 42–47

  • Zheng H, Wang H, Black N (2008) Human activity detection in smart home environment with self-adaptive neural networks. IEEE International Conference on Networking, Sensing and Control, pp 1505–1510

Download references


This research was partially supported by Nottingham Trent University’s Stimulating Innovation for Success (SIS) programme. The authors would like to thank Just Checking Ltd. ( for their support of this work.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Ahmad Lotfi.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Lotfi, A., Langensiepen, C., Mahmoud, S.M. et al. Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behaviour. J Ambient Intell Human Comput 3, 205–218 (2012).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Smart home
  • Dementia
  • Alzheimer
  • Assistive technology
  • Prediction
  • Abnormality detection
  • Time series
  • Sensor network
  • Intelligent environment