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Proceedings of SAI Intelligent Systems Conference

IntelliSys 2018: Intelligent Systems and Applications pp 387–398Cite as

Framework to Predict and Identify Wandering Behavior in Individuals with Alzheimer’s Using Physiological and Kinect Sensors

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 869))

Abstract

Alzheimer affects over 35.6 million people worldwide resulting in a gradual deterioration of cognition and independent daily functioning. Wandering, a behavior associated with Alzheimer’s Disease (AD) is highly challenging to manage by caregivers. It has been defined as an aimless pursuit in a state of disorientation. Even though wandering can lead to falls, physical harm, and in extreme cases death; this problem has been inadequately studied in the literature. The limited literature in gerontechnology has proposed solutions with geolocation devices that explore mechanisms to identify wandering only after the behavior has started to occur. These studies can only assist in developing solutions that are reactive to wandering behavior. Unfortunately, none of the studies aim at prediction of the wandering behavior that can lead to proactive identification and prevention of this problematic behavior. Existing studies have primarily utilized sensors in identification of wandering while it is happening and have not attended to physiological changes that may occur in individuals with AD before the onset of the wandering behavior such as a rise in heart rate, blood pressure, or body temperature. To address this gap in the knowledge base, we are proposing a framework that integrates a combination of wireless physiological sensors such as heart rate and blood pressure sensors, kinects, and infrared to predict and identify wandering in individuals with AD. This paper also explores various existing solutions that utilize mobile technology and GPS location devices and then presents a framework for an all-encompassing mechanism that can predict and identify wandering behavior.

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Author information

Authors and Affiliations

  1. Computer Science Department, University of Minnesota Duluth, Duluth, MN, USA

    Arshia Khan & Arshia Zernab Hassan

Authors
  1. Arshia Khan
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  2. Arshia Zernab Hassan
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Corresponding author

Correspondence to Arshia Khan .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, UK

    Rahul Bhatia

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Khan, A., Hassan, A.Z. (2019). Framework to Predict and Identify Wandering Behavior in Individuals with Alzheimer’s Using Physiological and Kinect Sensors. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 869. Springer, Cham. https://doi.org/10.1007/978-3-030-01057-7_31

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