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

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Abstract

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.

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Acknowledgments

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. (www.justchecking.co.uk) for their support of this work.

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Authors and Affiliations

  1. School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK

    Ahmad Lotfi, Caroline Langensiepen & Sawsan M. Mahmoud

  2. Centre for Innovation and Technology Exploitation, Nottingham Trent University, Nottingham, UK

    M. J. Akhlaghinia

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  1. Ahmad Lotfi
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  2. Caroline Langensiepen
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  3. Sawsan M. Mahmoud
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  4. M. J. Akhlaghinia
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Correspondence to Ahmad Lotfi.

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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). https://doi.org/10.1007/s12652-010-0043-x

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  • DOI: https://doi.org/10.1007/s12652-010-0043-x

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