Tytuł artykułu
Autorzy
Wybrane pełne teksty z tego czasopisma
Identyfikatory
DOI
Warianty tytułu
Konferencja
Federated Conference on Computer Science and Information Systems (16 ; 02-05.09.2021 ; online)
Języki publikacji
Abstrakty
Empathy is an important social ability in early childhood development. One of the significant characteristics of children with autism spectrum disorder (ASD) is their lack of empathy, which makes it difficult for them to understand other's emotions and to judge other's behavioral intentions, leading to social disorders. This research designed and implemented a facial expression analysis system that could obtain and analyze the real-time expressions of children when viewing stimulus, and evaluate the empathy differences between ASD children and typical development children. The research results provided new ideas for evaluation of ASD children, and helped to develop empathy intervention plans.
Rocznik
Tom
Strony
319--325
Opis fizyczny
Bibliogr. 15 poz., il., wykr., tab.
Twórcy
autor
- Faculty of Artificial Intelligence in Education, National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
autor
- Faculty of Artificial Intelligence in Education, National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
autor
- Faculty of Artificial Intelligence in Education, National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
autor
- Faculty of Artificial Intelligence in Education, National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
autor
- Faculty of Artificial Intelligence in Education, National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
Bibliografia
- 1. American Psychiatric Association, “Diagnostic and Statistical Manual of Mental Disorders: DSM-V,” Washington, DC: American Psychiatric Publishing, pp. 55-59, 2013. ISBN: 978-0-89042-554-1.
- 2. K. Pancerz, W. Paja and J. Gomuła, “Random forest feature selection for data coming from evaluation sheets of subjects with ASDs,” 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), 2016, pp. 299-302. http://dx.doi.org/10.15439/2016F274.
- 3. A. Kołakowska, A. Landowska, M. R. Wrobel, et al, “Applications for investigating therapy progress of autistic children,” 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), 2016, pp. 1693-1697. http://dx.doi.org/10.15439/2016F507.
- 4. Xiaoxia Zhang, Ye Wang, Xin Liu, et al, “A Review of Studies on Empathy Development of People with Autistic Spectrum Disorders,” Chinese Journal of Special Education, vol. 8, pp. 48-55, 2019. (in Chinese). http://dx.doi.org/10.3969/j.issn.1007-3728.2019.08.009.
- 5. Rozga A, King T Z, Vuduc R W, et al, “Undifferentiated facial electromyography responses to dynamic, audio‐visual emotion displays in individuals with autism spectrum disorders,” Developmental Science, vol. 16(4), pp. 499-514, 2013. http://dx.doi.org/10.1111/desc.12062.
- 6. Beall P M, Moody E J, Mcintosh D N, et al, “Rapid facial reactions to emotional facial expressions in typically developing children and children with autism spectrum disorder,” Journal of Experimental Child Psychology, vol. 101(3), pp. 206-223, 2008. http://dx.doi.org/10.1016/j.jecp.2008.04.004.
- 7. Mathersul D, Mcdonald S, Rushby J A, “Automatic facial responses to affective stimuli in high-functioning adults with autism spectrum disorder,” Physiology & Behavior, vol. 109(1), pp.14-22, 2013. http://dx.doi.org/10.1016/j.physbeh.2012.10.008.
- 8. Samad M D, Bobzien J L, Harrington J W, et al, “Non-intrusive optical imaging of face to probe physiological traits in Autism Spectrum Disorder,” Optics & Laser Technology, vol. 77, pp. 221-228, 2016. http://dx.doi.org/10.1016/j.optlastec.2015.09.030.
- 9. Leo M, Carcagnì P, Distante C, et al, “Computational assessment of facial expression production in ASD children,” Sensors, vol. 18(11), pp. 3993, 2018. http://dx.doi.org/10.3390/s18113993.
- 10. Coco M D, Leo M, Carcagni P, et al, “A Computer Vision Based Approach for Understanding Emotional Involvements in Children with Autism Spectrum Disorders,” International Conference on Computer Vision Workshop, pp. 1401-1407, 2017. http://dx.doi.org/10.1109/ICCVW.2017.166.
- 11. Zhang K, Zhang Z, Li Z, et al, “Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks,” IEEE Signal Processing Letters, vol. 23(10), pp. 1499-1503, 2016. http://dx.doi.org/10.1109/LSP.2016.2603342.
- 12. Liping Gu, Jin Jing, Yu Jin, et al, “Research of the relationship between the ability of emotion understanding and social adaptation in children with high functioning autism,” Chinese Journal of Child Health Care, vol. 21(1), pp. 16-19, 2013. (in Chinese)
- 13. Hou Hu, Mingfan Wu, Shenghua Hu, “Facial emotion recognition in children with high functioning autism,” Chinese Journal of School Health, vol. 35(8), pp. 1146-1149, 2014. (in Chinese)
- 14. Anastassiou‐Hadjicharalambous X, Warden D, “Convergence between physiological, facial and verbal self‐report measures of affective empathy in children,” Infant and Child Development: An International Journal of Research and Practice, vol. 16(3), pp. 237-254, 2007. http://dx.doi.org/10.1002/icd.464.
- 15. Li B, Mehta S, Aneja D, et al, “A Facial Affect Analysis System for Autism Spectrum Disorder,” IEEE International Conference on Image Processing, pp. 4549-4553, 2019. http://dx.doi.org/10.1109/ICIP.2019.8803604.
Uwagi
1. National Natural Science Foundation of China (No. 61807014) and the Fundamental Research Funds for the Central Universities (No. CCNU20QN026, CCNU19QN039)
2. Track 2: Computer Science and Systems
3. Session: 14th International Symposium on Multimedia Applications and Processing
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3e43a68c-45e2-408c-8fe4-fc3d06f87ed5
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