JCNRJournal of Clinical and Nursing Research2208-36852208-3693Bio-Byword Scientific Publishing Pty. Ltd.10.26689/jcnr.v5i4.2325ArticleValidation Research on the Application of Depth-wise Separable Convolutional AI Facial Expression Recognition in Non-pharmacological Treatment of BPSD https://artdesignp.com/journal/JCNR/5/4/10.26689/jcnr.v5i4.2325LiuXiangyu2021542021-08-02One of the most obvious clinical reasons of dementia or The Behavioral and Psychological Symptoms of Dementia (BPSD) are the lack of emotional expression, the increased frequency of negative emotions, and the impermanence of emotions. Observing the reduction of BPSD in dementia through emotions can be considered effective and widely used in the field of non-pharmacological therapy. At present, this article will verify whether the image recognition artificial intelligence (AI) system can correctly reflect the emotional performance of the elderly with dementia through a questionnaire survey of three professional elderly nursing staff. The ANOVA (sig.=0.50) is used to determine that the judgment given by the nursing staff has no obvious deviation, and then Kendall’s test (0.722**) and spearman’s test (0.863**) are used to verify the judgment severity of the emotion recognition system and the nursing staff unanimously. This implies the usability of the tool. Additionally, it can be expected to be further applied in the research related to BPSD elderly emotion detection.

Cytowic R, 2003, The Man Who Tasted Shapes, April 2003 MIT Press.

Wolf K, 2015, Measuring facial expression of motion, Dialogues Clin Neurosci. Dec; 17(4): 457–462.

Andrew G., 2017, Howard, MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, arXiv:1704.04861v1.

Pröve PL, 2017, An Introduction to Different Types of Convolutions in Deep Learning, https://towardsdatascience.com/types-of-convolutions-in-deep-learning-717013397f4d.

Yoon K, 2014, Convolutional Neural Networks for Sentence Classification, EMNLP, 1746-1751

Chollet F, 2017, Xception: Deep Learning with Depth-Wise Separable Convolutions, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1251-1258.

Arriaga O, Valdenegro-Toro M, Plöger P, 2017, Real-Time Convolutional Neural Networks for Emotion and Gender Classification. arXiv preprint arXiv:1710.07557.

Jain DK, Shamsolmoali P, Sehdev P, 2019, Extended Deep Neural Network for Facial Emotion Recognition. Pattern Recognition Letters, 120: 69-74.

Rachael EJ, 2012, Facial Expressions of Emotion are Not Culturally Universal, PNAS, 109 (19): 7241-7244

Heerink M, Krose B, Evers V, Wielinga B, 2010, Assessing Acceptance of Assistive Social Agent Technology by Older Adults: The Almere Model. Int J Soc Robot 2:361–375

Stafford RQ, et al., 2010, Improved Robot Attitudes and Emotions at a Retirement Home After Meeting a Robot, 19th IEEE International Symposium on Robot and Human Interactive Communication, Principe di Piemonte - Viareggio, Italy.