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Lower-Gait Tracking Application Using Smartphones and Tablets Read chapter Read first chapter

2022 | OriginalPaper | Chapter

Detecting and Monitoring Behavioural Patterns in Individuals with Cognitive Disorders in the Home Environment with Partial Annotations

Authors : Weisong Yang, Rafael Poyiadzi, Yoav Ben-Shlomo, Ian Craddock, Liz Coulthard, Raul Santos-Rodriguez, James Selwood, Niall Twomey

Published in: Integrating Artificial Intelligence and IoT for Advanced Health Informatics

Publisher: Springer International Publishing

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Abstract

The need to automatically monitor the state and progression of chronic neurological diseases such as dementia, together with the emergence of state-of-the-art sensing platforms for the home environment offer unprecedented opportunities for automatic behavioural monitoring as a proxy of disease state. However, when these platforms have been deployed, data challenges, including the lack of reliable annotations, limit the applicability of standard machine learning techniques. This chapter specifically seeks to characterise behavioural signatures of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) dementia. We introduce bespoke machine learning techniques accounting for partial annotations to produce behavioural metrics of key symptoms and use these on a novel dataset of longitudinal sensor data from persons with MCI and AD.

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previous chapter One-Class Classification Approach in Accelerometer-Based Remote Monitoring of Physical Activities for Healthcare Applications
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Footnotes
1
An initial version of our pipeline was presented in [50].
 
2
In this context, with test data we refer to the truly unlabelled data we have—coming from the participants. Not to be confused with the training/validation/test splits in cross-validation procedures.
 
3
The transition matrix could in principle be optimised, but we instead keep it fixed.
 
4
As can be seen in Fig. 4 each half covers all the rooms in the house.
 
5
For houses B and D, during the walkaround there were two wearables present. For houses A and C, it was only one, and therefore a similar model selection procedure could not be followed. Instead, we used information from the selected hyperparameter sets for houses B and D to fit the final models for houses A and C.
 
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Metadata
Title
Detecting and Monitoring Behavioural Patterns in Individuals with Cognitive Disorders in the Home Environment with Partial Annotations
Authors
Weisong Yang
Rafael Poyiadzi
Yoav Ben-Shlomo
Ian Craddock
Liz Coulthard
Raul Santos-Rodriguez
James Selwood
Niall Twomey
Copyright Year
2022
Book
Integrating Artificial Intelligence and IoT for Advanced Health Informatics

Print ISBN: 978-3-030-91180-5

Electronic ISBN: 978-3-030-91181-2

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-030-91181-2_3