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Canadian Conference on Artificial Intelligence

Canadian AI 2018: Advances in Artificial Intelligence pp 181–193Cite as

Infusing Domain Knowledge to Improve the Detection of Alzheimer’s Disease from Everyday Motion Behaviour

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10832))

Abstract

Alzheimer’s disease can severely impair the independent lifestyle of a person. Dem@Care is an European research project that conducted a study for timely diagnosis of Alzheimers disease by collecting everyday motion data from couples (or dyads), with one of the person in the couple having AD. Their results suggest that AD can be detected using everyday motion data from accelerometers. They did evaluation based on leave-one-person-out cross-validation. However, this evaluation can introduce bias in the classification results because one of the person from the dyad is present in the training set while the other is being tested. In this paper, we revisit the Dem@Care study and propose a new evaluation method that performs leave one-dyad-out cross-validation to remove the dataset selection bias. We then introduce new domain specific features based on dynamic and static intervals of motions that significantly improves the classification results. We further show increase in performance by combining the proposed features with new time, frequency domain and baseline features used in the Dem@Care study.

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Authors and Affiliations

  1. Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada

    Chao Bian & Alex Mihailidis

  2. Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, Canada

    Shehroz S. Khan & Alex Mihailidis

Authors
  1. Chao Bian
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  2. Shehroz S. Khan
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  3. Alex Mihailidis
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Corresponding author

Correspondence to Shehroz S. Khan .

Editor information

Editors and Affiliations

  1. Ryerson University, Toronto, Ontario, Canada

    Ebrahim Bagheri

  2. McGill University, Montréal, Québec, Canada

    Jackie C.K. Cheung

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Bian, C., Khan, S.S., Mihailidis, A. (2018). Infusing Domain Knowledge to Improve the Detection of Alzheimer’s Disease from Everyday Motion Behaviour. In: Bagheri, E., Cheung, J. (eds) Advances in Artificial Intelligence. Canadian AI 2018. Lecture Notes in Computer Science(), vol 10832. Springer, Cham. https://doi.org/10.1007/978-3-319-89656-4_15

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  • DOI: https://doi.org/10.1007/978-3-319-89656-4_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-89655-7

  • Online ISBN: 978-3-319-89656-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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