Corrigendum: Sensing Technology to Monitor Behavioral and Psychological Symptoms and to Assess Treatment Response in People With Dementia. A Systematic Review

Husebo, Bettina S.; Heintz, Hannah; Berge, Line Iden; Owoyemi, Praise; Rahman, Aniqa; Vahia, Ipsit V.

doi:10.3389/fphar.2020.00254

Cited by 3 publications

(1 citation statement)

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“…Capturing early symptoms and aiding in-home dementia care have been among the important research preventing unplanned and avoidable hospital admissions and improving the quality of life of people affected by dementia [16]. The characteristics of these behavioural symptoms (e.g., agitation) make it hard to choose suitable algorithms and sensory technologies.…”

Section: Related Workmentioning

confidence: 99%

Semi-supervised Learning for Identifying the Likelihood of Agitation in People with Dementia

Rezvani¹,

Kouchaki²,

Nilforooshan³

et al. 2021

Preprint

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Interpreting the environmental, behavioural and psychological data from in-home sensory observations and measurements can provide valuable insights into the health and well-being of individuals. Presents of neuropsychiatric and psychological symptoms in people with dementia have a significant impact on their well-being and disease prognosis. Agitation in people with dementia can be due to many reasons such as pain or discomfort, medical reasons such as side effects of a medicine, communication problems and environment. This paper discusses a model for analysing the risk of agitation in people with dementia and how in-home monitoring data can support them. We proposed a semi-supervised model which combines a self-supervised learning model and a Bayesian ensemble classification. We train and test the proposed model on a dataset from a clinical study. The dataset was collected from sensors deployed in 96 homes of patients with dementia. The proposed model outperforms the state-of-the-art models in recall and f1-score values by 20%. The model also indicates better generalisability compared to the baseline models.

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Section: Related Workmentioning

confidence: 99%