系統識別號 U0026-1902202000034200
論文名稱(中文) 眼動資料提取方法之差異及機器學習模型差異分析-應用於泛自閉症兒童偵測
論文名稱(英文) Differences in eye movement data extraction methods and machine learning models-Applied to the detection of children with autism
校院名稱 成功大學
系所名稱(中) 心理學系
系所名稱(英) Department of Psychology
學年度 108
學期 1
出版年 109
研究生(中文) 黃得恩
研究生(英文) Te-En Huang
電子信箱 qaz910048@gmail.com
學號 U76074059
學位類別 碩士
語文別 中文
論文頁數 74頁
口試委員 指導教授-蕭富仁
中文關鍵字 自閉症  人工智慧  機器學習  眼球追蹤  深度神經網絡  支援向量機  K-means 
英文關鍵字 Autism  Machine Learning  Artificial Intelligence  Eye-tracking  Deep Learning  Support Vector Machine  K-means 
中文摘要 在先前的研究中發現自閉症兒童有著與其他正常小孩不同的眼球追蹤模式(Sasson & Elison, 2012),藉由這些眼球追蹤模式的探討,我們可以研判某些孩童可能會患有自閉症相關症狀的傾向。如果能隨著自閉症孩童的症狀進行較為早期的偵測,亦可有效的改善自閉症孩童的情況,所以本研究便想使用眼球追蹤裝置來掌握孩童的眼球運動反應模式,進而發展出能夠偵測自閉症傾向的自動判別系統。
英文摘要 The detection of children with autism spectrum disorders (ASD) have always been a difficult problem. For children with ASD, it is hard to pay attention to others as well as to interact with people in society (Myles, Brenda & Simpson, 2001). If we do not concern those children with ASD, they will have difficulties making social connection throughout their lives. In order to reduce the behavioral difference in children with autism, we need some ways to detect ASD more easily first.

To distinguish between children with ASD and typically developed children, we used an eye-tracker to record the eye-movement of the children participants. An eye-tracker is a device that tracks the center of visual field of a person. By using this device, the researchers can know where a participant is focusing on. This device also provides some analysis details like the average of fixation durations, etc. Also, many of the previous studies about ASD used an eye-tracker for measurement since it is an easier tool to record and analyze children with ASD. So, eye-tracker is widely used for experiments concerning ASD. If you want to analyze the attention of people, it is a good way to use an eye-tracker as the measuring device.

The eye tracking data can help us detect ASD, because the visual attentional pattern of children with ASD is different from that of normal children. Most of the children with ASD exhibit abnormal eye-tracking patterns when looking at various people and objects (Sasson & Elison, 2012), especially when looking at strangers. This is because while recognizing a less familiar human, children with ASD tend to avoid the important parts of the face, such as eyes, nose, mouth, etc. Some research also used the unfamiliar faces as the stimuli for experiment and obtained several good findings. Based on those findings, we used unfamiliar faces as stimuli in our experiment as well.

We aim to develop a tool which can detect children with ASD more easily and quickly, so that maybe by assigning homework for them to improve their behavior, we can help them earlier. In order to further improve the detection of ASD, we used pictures of both native and foreign faces. Some research pointed out that while recognizing native faces and foreign faces, the eye-movement of children with ASD is different from that of normal children (Wilson, Palermo, Burton & Brock, 2011). This implies that using foreign faces as stimuli might increase the difference of eye-movement between children with ASD and normal children, and therefore reduce the ambiguity and improve the accuracy of the detection. Hence in this study, we compared the results of the two conditions, using either native faces or foreign faces as the stimuli.

For the detection of autism, previous research has proposed several prediction models (Liu, Li & Yi, 2016; Tyagi, Mishra & Bajpai, 2018) with different characteristics. Among those models, most studies used SVM or DNN models to predict ASD. However, there is no benchmark that allows us to have a fare comparison with those models. Although Tyagi, Mishra & Bajpai (2018) made a comparison between different predict models to predict adult autism, they did not separate the eye-tracker variables and questionnaire variables, so it was hard to tell which of the variables is the most important. Moreover, they only used adult participants in the research, so the results cannot be utilized in early detection and early treatment. Thus, in this research, we also compared the methods and structures of different machine learning models.

Before analyzing the eye-movement recorded by the eye-tracker, the researchers need to select their areas of interest (AOI) on the stimuli. The eye-tracker research in the past used human area of interest (human AOI) to select the important areas in the visual stimuli. Liu, Li & Yi (2016) have used k-means algorithm to select the important parts of the interest (Auto AOI) and obtained more than 80% of accuracy. However, they did not have a comparison between human AOI & Auto AOI, so it is unclear which AOI selection method is the best way for detecting children with ASD.

We hope our research can be used to develop a powerful system that can automatically distinguish between children with ASD and normal children based on the pattern of eye-movement. To achieve our goals, we divided our research into three sections. First, we compared the results of ASD detection of different machine learning models, namely support vector machine (SVM) and deep neural network (DNN). Second, we examined whether the stimuli of faces from different countries help our model to detect autism more accurately or not. Third, we compared the difference between Human AOI and Auto AOI, which was generated by k-means algorithm.
The results of the research can provide not only a system to detect ASD easier and more accurately, but also an advance of mental disorder diagnosis. For future research, we hope that our research can be used as a basis for diagnosis of any clinical disease. Furthermore, we hope our study can connect machine learning technology with clinical disease, in order to achieve early detection and early treatment.
論文目次 第一章 緒論 1
第一節 自閉症介紹 1
自閉症與正常人之行為差異 2
第二節 眼球運動分析儀運用於ASD偵測之研究 3
一、眼動儀簡介 3
二、早期分析眼動儀及ASD之間關連性的方法 4
第三節 機器學習、眼動儀、自閉症之關係 5
一、機器學習簡介 5
二、機器學習模型運用於自閉症偵測 9
第四節 研究問題及研究假設 10
第二章 研究一: 以人工AOI及DNN預測是否有自閉症 12
第一節 實驗目的、推論與假設 12
第二節 實驗方法 12
一、受試者 12
二、研究材料 13
三、研究設備 13
四、研究設計 14
五、眼動指標 14
六、研究程序 15
第三節 結果 16
一、 不同刺激材料之比較 16
二、 不同刺激材料之組合比較 19
第四節 討論 23
第三章 研究二: 以人工AOI及SVM預測是否有自閉症 25
第一節 實驗目的、推論與假設 25
第二節 實驗方法 25
一、受試者、實驗材料、實驗設備、實驗設計、眼動指標皆同研究一 25
二、實驗程序 25
第三節 結果 26
一、 不同刺激材料之比較 26
二、 不同刺激材料之組合比較 28
第四節 討論 31
第四章 研究三: 以K-MEANS自動AOI及DNN預測是否有自閉症 32
第一節 實驗目的、推論與假設 32
第二節 實驗方法 32
一、受試者、實驗材料、實驗設備、實驗設計皆同研究一。 32
二、眼動指標 32
三、實驗程序 33
第三節 結果 34
一、 不同刺激材料之比較 34
二、 不同刺激材料之組合比較 37
第四節 討論 41
第五章 研究四: 以K-MEANS自動AOI及SVM預測是否有自閉症 42
第一節 實驗目的、推論與假設 42
第二節 實驗方法 42
一、受試者、實驗材料、實驗設備、實驗設計皆同研究一,眼動指標同研究三。 42
二、實驗程序 42
第三節 結果 43
一、 不同刺激材料之比較 43
二、 不同刺激材料之組合比較 45
第四節 討論 47
第六章 綜合討論 49
第一節 主要發現 49
第二節 結論 56
第三節 未來研究之方向建議 58
參考文獻 60
中文參考文獻: 60
英文參考文獻: 60
附錄 66
參考文獻 中文參考文獻:
王南凱、吳岱穎、鄒國蘇、黃宜靜、郭冠良、吳逸帆、陳建志. (2013). 淺談自閉症類群障礙. 北市醫學雜誌, 10(3), 173–181. https://doi.org/10.6200/TCMJ.2013.10.3.01
唐大崙、 張文瑜 (2007). 利用眼動追蹤法探索傳播研究. 中華傳播學刊, (12), 165–211. https://doi.org/10.6195/cjcr.2007.12.05
鳳華 (2001)。中部地區自閉症者心智理論之發展現況及教學成效結案報告。
余勝皓、 陳學志、林慧麗(2018).。以眼動儀探討罹患自閉症類群障礙症之兒童對自然情境圖片中社會訊息之凝視型態:ASD自然情境圖片眼動研究. 特殊教育研究學刊, 43(2), 65–92. https://doi.org/10.6172/BSE.201807_43(2).0003

Ashwin, C., Baron-Cohen, S., Wheelwright, S., O’Riordan, M., &Bullmore, E. T. (2007). Differential activation of the amygdala and the ‘social brain’ during fearful face-processing in Asperger Syndrome. Neuropsychologia, 45(1), 2–14. https://doi.org/10.1016/J.NEUROPSYCHOLOGIA.2006.04.014
Ashwin, C., Chapman, E., Colle, L., & Baron-Cohen, S. (2006). Impaired recognition of negative basic emotions in autism: a test of the amygdala theory. Social Neuroscience, 1(3–4), 349–363. https://doi.org/10.1080/17470910601040772
Baron-cohen, S., Riordan, M. O., Stone, V., Jones, R., & Plaisted, K. (1999). A new test of social sensitivity : Detection of faux pas in normal children and children with Asperger syndrome : Journal of Autism and Developmental Disorders, 29, 407–418.
BATTY, M., & TAYLOR, M. J. (2002). Visual categorization during childhood: An ERP study. Psychophysiology, 39(4), S0048577202010764. https://doi.org/10.1017/S0048577202010764
Batty, M., & Taylor, M. J. (2006). The development of emotional face processing during childhood. Developmental Science, 9(2), 207–220. https://doi.org/10.1111/j.1467-7687.2006.00480.x
Boyle, C. L., & Lutzker, J. R. (2005). Teaching young children to discriminate abusive from nonabusive situations using multiple exemplars in a modified discrete trial teaching format. Journal of Family Violence, 20(2), 55–69. https://doi.org/10.1007/s10896-005-3169-4
Casanova, M. F. (2007). The neuropathology of autism. Brain Pathology, 17(4), 422–433. https://doi.org/10.1111/j.1750-3639.2007.00100.x
Celani, G., Battacchi, M. W., & Arcidiacono, L. (1999). The Understanding of the Emotional Meaning of Facial Expressions in People with Autism. Journal of Autism and Developmental Disorders, 29(1), 57–66. https://doi.org/10.1023/A:1025970600181
Chang, C. C., & Lin, C. J. (2011). LIBSVM: A Library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3). https://doi.org/10.1145/1961189.1961199
Chua, H. F., Boland, J. E., & Nisbett, R. E. (2005). Cultural variation in eye movements during scene perception. Proceedings of the National Academy of Sciences of the United States of America, 102(35), 12629–12633. https://doi.org/10.1073/pnas.0506162102
Dawson, G., Meltzoff, A. N., Osterling, J., Rinaldi, J., & Brown, E. (1998). Children with Autism Fail to Orient to Naturally Occurring Social Stimuli. Journal of Autism and Developmental Disorders, 28(6), 479–485. https://doi.org/10.1023/A:1026043926488
DiLollo, V., Kawahara, J. I., Zuvic, S. M., & Visser, T. A. W. (2001). The preattentive emperor has no clothes: A dynamic redressing. Journal of Experimental Psychology: General, 130(3), 479–492. https://doi.org/10.1037/0096-3445.130.3.479
Downs, A., & Downs, R. C. (2013). Training new instructors to implement discrete trial teaching strategies with children with autism in a community-based intervention program. Focus on Autism and Other Developmental Disabilities, 28(4), 212–221. https://doi.org/10.1177/1088357612465120
Feng, H., Lo, Y., Tsai, S., & Cartledge, G. (2008). The Effects of Theory-of-Mind and Social Skill Training on the Social Competence of a Sixth-Grade Student With Autism. Journal of Positive Behavior Interventions, 10(4), 228–242. https://doi.org/10.1177/1098300708319906
Fletcher-Watson, S., Leekam, S. R., Benson, V., Frank, M. C., & Findlay, J. M. (2009). Eye-movements reveal attention to social information in autism spectrum disorder. Neuropsychologia, 47(1), 248–257. https://doi.org/10.1016/J.NEUROPSYCHOLOGIA.2008.07.016
Gillberg, C. (1998). Asperger syndrome and high-functioning autism. British Journal of Psychiatry, 172(3), 200–209. https://doi.org/10.1192/bjp.172.3.200
Golan, O., Baron-Cohen, S., & Golan, Y. (2008). The ‘Reading the Mind in Films’ Task [Child Version]: Complex Emotion and Mental State Recognition in Children with and without Autism Spectrum Conditions. Journal of Autism and Developmental Disorders, 38(8), 1534–1541. https://doi.org/10.1007/s10803-007-0533-7
Gosselin, F., & Schyns, P. G. (2001). Bubbles: A technique to reveal the use of information in recognition tasks. Vision Research, 41(17), 2261–2271. https://doi.org/10.1016/S0042-6989(01)00097-9
Hall, S. S., Hustyi, K. M., Hammond, J. L., Hirt, M., & Reiss, A. L. (2014). Using Discrete Trial Training to Identify Specific Learning Impairments in Boys with Fragile X Syndrome. Journal of Autism and Developmental Disorders, 44(7), 1659–1670. https://doi.org/10.1007/s10803-014-2037-6
Harms, M. B., Martin, A., & Wallace, G. L. (2010). Facial Emotion Recognition in Autism Spectrum Disorders: A Review of Behavioral and Neuroimaging Studies. Neuropsychology Review, 20(3), 290–322. https://doi.org/10.1007/s11065-010-9138-6
Hsu, H.-Y., &Chien, S. H.-L. (2011). Exploring the other-race effect in Taiwanese infants and adults. [Exploring the other-race effect in Taiwanese infants and adults.]. Chinese Journal of Psychology, 53(1), 35–57.
Huang, J., Shao, X., & Wechsler, H. (2002). Face pose discrimination using support vector machines (SVM). 154–156. https://doi.org/10.1109/icpr.1998.711102
Jiang, M., & Zhao, Q. (n.d.). Learning Visual Attention to Identify People with Autism Spectrum Disorder. 3267–3276.
Ketkar, N., & Ketkar, N. (2017). Introduction to Keras. In Deep Learning with Python. https://doi.org/10.1007/978-1-4842-2766-4_7
Klin, A., Jones, W., Schultz, R., Volkmar, F., & Cohen, D. (2002). Visual fixation patterns during viewing of naturalistic social situations as predictors of social competence in individuals with autism. Archives of General Psychiatry, 59(9), 809–816. https://doi.org/10.1001/archpsyc.59.9.809
Kocsis, R. N. (2013). Book Review: Diagnostic and Statistical Manual of Mental Disorders: Fifth Edition (DSM-5). International Journal of Offender Therapy and Comparative Criminology, 57(12), 1546–1548. https://doi.org/10.1177/0306624X13511040
Kodinariya, T. M., & Makwana, P. R. (2013). Review on determining number of Cluster in K-Means Clustering. International Journal of Advance Research in Computer Science and Management Studies, 1(6), 2321–7782.
Li, M.-A., & Tsai, C.-H. (2016). Text Categorization for Chinese News : A Comparative Study Text Categorization for Chinese News : A Comparative Study. (June).
Lindner, J. L., & Rosén, L. A. (2006). Decoding of Emotion through Facial Expression, Prosody and Verbal Content in Children and Adolescents with Asperger’s Syndrome. Journal of Autism and Developmental Disorders, 36(6), 769–777. https://doi.org/10.1007/s10803-006-0105-2
Liu, W., Li, M., & Yi, L. (2016). Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework. Autism Research, 9(8), 888–898. https://doi.org/10.1002/aur.1615
Liu, W., Yi, L., Yu, Z., Zou, X., Raj, B., & Li, M. (2015). Efficient autism spectrum disorder prediction with eye movement: A machine learning framework. 2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015, 649–655. https://doi.org/10.1109/ACII.2015.7344638
Loughland, C. M., Williams, L. M., & Gordon, E. (2002). Schizophrenia and affective disorder show different visual scanning behavior for faces: a trait versus state-based distinction? Biological Psychiatry, 52(4), 338–348. https://doi.org/10.1016/S0006-3223(02)01356-2
Mundy, P., & Rebecca Neal, A. (2000). Neural plasticity, joint attention, and a transactional social-orienting model of autism. International Review of Research in Mental Retardation, 23, 139–168. https://doi.org/10.1016/S0074-7750(00)80009-9
Myles, Brenda; Simpson, R. (2001). Focus on Exceptional Children: Effective Practices for Students with Asperger Syndrome.
Njiokiktjien, C., Verschoor, A., deSonneville, L., Huyser, C., Op het Veld, V., & Toorenaar, N. (2001). Disordered recognition of facial identity and emotions in three Asperger type autists. European Child & Adolescent Psychiatry, 10(1), 79–90. https://doi.org/10.1007/s007870170050
Noton, D., & Stark, L. (1971). Scanpaths in Eye Movements during Pattern Perception. Science, 171(3968), 308 LP – 311. https://doi.org/10.1126/science.171.3968.308
Pelphrey, K. A., Sasson, N. J., Reznick, J. S., Paul, G., Goldman, B. D., & Piven, J. (2002). Visual Scanning of Faces in Autism. Journal of Autism and Developmental Disorders, 32(4), 249–261. https://doi.org/10.1023/A:1016374617369
Rutherford, M. D., & McIntosh, D. N. (2007). Rules versus Prototype Matching: Strategies of Perception of Emotional Facial Expressions in the Autism Spectrum. Journal of Autism and Developmental Disorders, 37(2), 187–196. https://doi.org/10.1007/s10803-006-0151-9
Sasson, N. J., & Elison, J. T. (2012). Eye tracking young children with autism. Journal of Visualized Experiments : JoVE, (61), 1–5. https://doi.org/10.3791/3675
Simpson, R. L. (2005). Evidence-Based Practices and Students With Autism Spectrum Disorders. Focus on Autism and Other Developmental Disabilities, 20(3), 140–149. https://doi.org/10.1177/10883576050200030201
Smith, T. (2001). Discrete Trial Training in the Treatment of Autism. Focus on Autism and Other Developmental Disabilities, 16(2), 86–92. https://doi.org/10.1177/108835760101600204
Spezio, M. L., Adolphs, R., Hurley, R. S. E., & Piven, J. (2007). Abnormal Use of Facial Information in High-Functioning Autism. Journal of Autism and Developmental Disorders, 37(5), 929–939. https://doi.org/10.1007/s10803-006-0232-9
Suykens, J. A. K., & Vandewalle, J. (1999). Least Squares Support Vector Machine Classifiers. Neural Processing Letters, 9(3), 293–300. https://doi.org/10.1023/A:1018628609742
Thomas, L. A., DeBellis, M. D., Graham, R., & LaBar, K. S. (2007). Development of emotional facial recognition in late childhood and adolescence: REPORT. Developmental Science, 10(5), 547–558. https://doi.org/10.1111/j.1467-7687.2007.00614.x
Tyagi, B., Mishra, R., & Bajpai, N. (2018). Machine Learning Techniques to Predict Autism Spectrum Disorder. 1st International Conference on Data Science and Analytics, PuneCon 2018 - Proceedings. https://doi.org/10.1109/PUNECON.2018.8745405
Varoquaux, G., Buitinck, L., Louppe, G., Grisel, O., Pedregosa, F., & Mueller, A. (2015). Scikit-learn. GetMobile: Mobile Computing and Communications, 19(1), 29–33. https://doi.org/10.1145/2786984.2786995
Villalba, J., Miguel, A., Ortega, A., & Lleida, E. (2015). Spoofing detection with DNN and one-class SVM for the ASVspoof 2015 challenge. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2015-Janua(1), 2067–2071.
Wilson, C. E., Palermo, R., & Brock, J. (2012). Visual scan paths and recognition of facial identity in autism spectrum disorder and typical development. PLoS ONE, 7(5), 1–9. https://doi.org/10.1371/journal.pone.0037681
Wilson, C. E., Palermo, R., Burton, A. M., & Brock, J. (2011). Recognition of own- and other-race faces in autism spectrum disorders. Quarterly Journal of Experimental Psychology, 64(10), 1939–1954. https://doi.org/10.1080/17470218.2011.603052
Yi, L., Quinn, P. C., Fan, Y., Huang, D., Feng, C., Joseph, L. & Lee, K. (2016). Children with Autism Spectrum Disorder scan own-race faces differently from other-race faces. Journal of Experimental Child Psychology, 141, 177–186. https://doi.org/10.1016/j.jecp.2015.09.011
Zihl, J., VonCramon, D., & Mai, N. (1983). Selective disturbance of movement vision after bilateral brain damage. Brain, 106(2), 313–340. https://doi.org/10.1093/brain/106.2.313
  • 同意授權校內瀏覽/列印電子全文服務,於2025-01-01起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2025-01-01起公開。

  • 如您有疑問,請聯絡圖書館