Behind the Study: Facial Expression Recognition Predicts Neuropsychiatric Symptoms of Dementia

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Aging (Aging-US)

Dr. Liang-Kung Chen from the National Yang-Ming Chiao-Tung University, Taipei, Taiwan details a research paper he co-authored that was published by Aging (Aging-US) in Volume 14, Issue 3, entitled, “Predicting neuropsychiatric symptoms of persons with dementia in a day care center using a facial expression recognition system.” DOI - https://doi.org/10.18632/aging.203869 Corresponding author - Liang-Kung Chen - lkchen2@vghtpe.gov.tw Video transcript - https://aging-us.net/2022/06/28/behind-the-study-facial-expression-recognition-predicts-neuropsychiatric-symptoms-of-dementia/ Abstract 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. Sign up for free Altmetric alerts about this article - https://aging.altmetric.com/details/email_updates?id=10.18632%2Faging.203869 Keywords - aging, artificial intelligence, behavioral and psychological symptoms of dementia, dementia, facial expression recognition system, machine learning About Aging-US Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways. Please visit our website at http://www.Aging-US.com​​ or connect with us: SoundCloud - https://soundcloud.com/Aging-Us Facebook - https://www.facebook.com/AgingUS/ Twitter - https://twitter.com/AgingJrnl Instagram - https://www.instagram.com/agingjrnl/ YouTube - https://www.youtube.com/agingus​ LinkedIn - https://www.linkedin.com/company/aging/ Pinterest - https://www.pinterest.com/AgingUS/ Aging-US is published by Impact Journals, LLC: http://www.ImpactJournals.com​​ Media Contact 18009220957 MEDIA@IMPACTJOURNALS.COM