Predicting neuropsychiatric symptoms of persons with dementia in a day care center using a facial expression recognition system

Liang Yu Chen*, Tsung Hsien Tsai, Andy Ho, Chun Hsien Li, Li Ju Ke, Li Ning Peng, Ming Hsien Lin, Fei Yuan Hsiao, Liang Kung Chen

*此作品的通信作者

研究成果: Article同行評審

5 引文 斯高帕斯(Scopus)

摘要

Background: Behavioral and psychological symptoms of dementia (BPSD) affect 90% of persons with dementia (PwD), resulting in various adverse outcomes and aggravating care burdens among their caretakers. This study aimed to explore the potential of artificial intelligence-based facial expression recognition systems (FERS) in predicting BPSDs among PwD. Methods: A hybrid of human labeling and a preconstructed deep learning model was used to differentiate basic facial expressions of individuals to predict the results of Neuropsychiatric Inventory (NPI) assessments by stepwise linear regression (LR), random forest (RF) with importance ranking, and ensemble method (EM) of equal importance, while the accuracy was determined by mean absolute error (MAE) and root-mean-square error (RMSE) methods. Results: Twenty-three PwD from an adult day care center were enrolled with ≥ 11,500 FERS data series and 38 comparative NPI scores. The overall accuracy was 86% on facial expression recognition. Negative facial expressions and variance in emotional switches were important features of BPSDs. A strong positive correlation was identified in each model (EM: r = 0.834, LR: r = 0.821, RF: r = 0.798 by the patientwise method; EM: r = 0.891, LR: r = 0.870, RF: r = 0.886 by the MinimPy method), and EM exhibited the lowest MAE and RMSE. Conclusions: FERS successfully predicted the BPSD of PwD by negative emotions and the variance in emotional switches. This finding enables early detection and management of BPSDs, thus improving the quality of dementia care.

原文English
頁(從 - 到)1280-1291
頁數12
期刊Aging
14
發行號3
DOIs
出版狀態Published - 2022

指紋

深入研究「Predicting neuropsychiatric symptoms of persons with dementia in a day care center using a facial expression recognition system」主題。共同形成了獨特的指紋。

引用此