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Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques

Author

Listed:
  • Shirin Enshaeifar
  • Ahmed Zoha
  • Andreas Markides
  • Severin Skillman
  • Sahr Thomas Acton
  • Tarek Elsaleh
  • Masoud Hassanpour
  • Alireza Ahrabian
  • Mark Kenny
  • Stuart Klein
  • Helen Rostill
  • Ramin Nilforooshan
  • Payam Barnaghi

Abstract

The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study that is called Technology Integrated Health Management (TIHM). TIHM is a technology assisted monitoring system that uses Internet of Things (IoT) enabled solutions for continuous monitoring of people with dementia in their own homes. We have developed machine learning algorithms to analyse the correlation between environmental data collected by IoT technologies in TIHM in order to monitor and facilitate the physical well-being of people with dementia. The algorithms are developed with different temporal granularity to process the data for long-term and short-term analysis. We extract higher-level activity patterns which are then used to detect any change in patients’ routines. We have also developed a hierarchical information fusion approach for detecting agitation, irritability and aggression. We have conducted evaluations using sensory data collected from homes of people with dementia. The proposed techniques are able to recognise agitation and unusual patterns with an accuracy of up to 80%.

Suggested Citation

  • Shirin Enshaeifar & Ahmed Zoha & Andreas Markides & Severin Skillman & Sahr Thomas Acton & Tarek Elsaleh & Masoud Hassanpour & Alireza Ahrabian & Mark Kenny & Stuart Klein & Helen Rostill & Ramin Nilf, 2018. "Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-20, May.
    • Handle: RePEc:plo:pone00:0195605
    DOI: 10.1371/journal.pone.0195605
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    References listed on IDEAS

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    1. Jeffrey A. Kaye & Shoshana A. Maxwell & Nora Mattek & Tamara L. Hayes & Hiroko Dodge & Misha Pavel & Holly B. Jimison & Katherine Wild & Linda Boise & Tracy A. Zitzelberger, 2011. "Intelligent Systems for Assessing Aging Changes: Home-Based, Unobtrusive, and Continuous Assessment of Aging," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 66(suppl_1), pages 180-190.
    2. Hubert, M. & Vandervieren, E., 2008. "An adjusted boxplot for skewed distributions," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5186-5201, August.
    3. Shao-Meng Qin & Hannu Verkasalo & Mikael Mohtaschemi & Tuomo Hartonen & Mikko Alava, 2012. "Patterns, Entropy, and Predictability of Human Mobility and Life," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-8, December.
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    Cited by:

    1. Belfiore, Alessandra & Cuccurullo, Corrado & Aria, Massimo, 2022. "IoT in healthcare: A scientometric analysis," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    2. Shirin Enshaeifar & Ahmed Zoha & Severin Skillman & Andreas Markides & Sahr Thomas Acton & Tarek Elsaleh & Mark Kenny & Helen Rostill & Ramin Nilforooshan & Payam Barnaghi, 2019. "Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-22, January.

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