Modeling and mobile home monitoring of behavioral and psychological symptoms of dementia (BPSD)

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作者
Haihang Yuan
Tianyi Yang
Qiaolian Xie
Guilhem Lledos
Wen-Huei Chou
Wenwei Yu
机构
[1] Chiba University,Department of Medical Engineering
[2] University of Shanghai for Science and Technology,Institute of Rehabilitation Engineering and Technology
[3] UPSSITECH - Paul Sabatier University of Toulouse,Department of Digital Media Design
[4] National Yunlin University of Science and Technology,Center for Frontier Medical Engineering
[5] Chiba University,undefined
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关键词
BPSD models; Home monitoring robots; Identification of occurrence of BPSD;
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摘要
With the increasing global aging population, dementia care has rapidly become a major social problem. Current diagnosis of Behavior and Psychological Symptoms of Dementia (BPSD) relies on clinical interviews, and behavioral rating scales based on a period of behavior observation, but these methods are not suitable for identification of occurrence of BPSD in the daily living, which is necessary for providing appropriate interventions for dementia, though, has been studied by few research groups in the literature. To address these issues, in this study developed a BPSD monitoring system consisting of a Psycho-Cognitive (PsyCo) BPSD model, a Behavior-Physio-Environment (BePhyEn) BPSD model, and an implementation platform. The PsyCo BPSD model provides BPSD assessment support to caregivers and care providers, while the BePhyEn BPSD model provides instantaneous alerts for BPSD enabled by a 24-hour home monitoring platform for early intervention, and thereby alleviation of burden to patients and caregivers. Data for acquiring the models were generated through extensive literature review and regularity determined. A mobile robot was utilized as the implementation platform for improving sensitivity of sensors for home monitoring, and elderly individual following algorithms were investigated. Experiments in a virtual home environment showed that, a virtual BPSD elderly individual can be followed safely by the robot, and BPSD occurrence could be identified accurately, demonstrating the possibility of modeling and identification of BPSD in home environment.
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