{"title":"基于强化学习的自闭症儿童辅助治疗系统","authors":"Minjia Li, Xue Li, Lun Xie, Jing Liu, Feifei Wang, Zhiliang Wang","doi":"10.1080/24699322.2019.1649072","DOIUrl":null,"url":null,"abstract":"Abstract Assisted therapy is increasingly used in autism spectrum disorders (ASD) for improving social interaction and communication skills in recent years. A lot of studies have proven that the form of interactive games for therapy has a good effect on children with autism. Thus, our study provided an assisted therapeutic system based on Reinforcement Learning (RL) for children with ASD, which has five interactive subgames. As is well known, it is necessary to establish and maintain compelling interactions in therapeutic process. Therefore, we aim to adjust the interactive content according to the emotions of children with autism. However, due to the atypical and unusually differences in children with autism, most systems are based on off-line training of small samples of individuals and online recognition, so the existing assisted systems are limited in their ability to automatically update system parameters of new mappings. The integration of RL and Convolutional Neural Network (CNN)-Support Vector Regression (SVR) was used to deal with the updating online of prediction model’s weights. The normalized emotion labels were evaluated by the therapists. Eleven children with autism as subjects were invited in this experiment and captured facial video images. The experiment lasted for five weeks of intermittent assisted therapy, and the results were evaluated for the system and the therapy effect. Finally, we achieved a general reduction in the root mean square error of the model prediction results and labels. Although there is no significant difference in Social Responsiveness Scale (SRS) scores before and after assisted therapy (p value = 0.60), in individual subjects (Sub. 1, Sub. 2 and Sub.3), the SRS total score is significantly reduced (Average drop of 19 points). These results demonstrate the effectiveness of prediction model based on RL and show the feasibility of assisted therapeutic system in children with autism.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2019-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24699322.2019.1649072","citationCount":"5","resultStr":"{\"title\":\"Assisted therapeutic system based on reinforcement learning for children with autism\",\"authors\":\"Minjia Li, Xue Li, Lun Xie, Jing Liu, Feifei Wang, Zhiliang Wang\",\"doi\":\"10.1080/24699322.2019.1649072\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Assisted therapy is increasingly used in autism spectrum disorders (ASD) for improving social interaction and communication skills in recent years. A lot of studies have proven that the form of interactive games for therapy has a good effect on children with autism. Thus, our study provided an assisted therapeutic system based on Reinforcement Learning (RL) for children with ASD, which has five interactive subgames. As is well known, it is necessary to establish and maintain compelling interactions in therapeutic process. Therefore, we aim to adjust the interactive content according to the emotions of children with autism. However, due to the atypical and unusually differences in children with autism, most systems are based on off-line training of small samples of individuals and online recognition, so the existing assisted systems are limited in their ability to automatically update system parameters of new mappings. The integration of RL and Convolutional Neural Network (CNN)-Support Vector Regression (SVR) was used to deal with the updating online of prediction model’s weights. The normalized emotion labels were evaluated by the therapists. Eleven children with autism as subjects were invited in this experiment and captured facial video images. The experiment lasted for five weeks of intermittent assisted therapy, and the results were evaluated for the system and the therapy effect. Finally, we achieved a general reduction in the root mean square error of the model prediction results and labels. Although there is no significant difference in Social Responsiveness Scale (SRS) scores before and after assisted therapy (p value = 0.60), in individual subjects (Sub. 1, Sub. 2 and Sub.3), the SRS total score is significantly reduced (Average drop of 19 points). These results demonstrate the effectiveness of prediction model based on RL and show the feasibility of assisted therapeutic system in children with autism.\",\"PeriodicalId\":56051,\"journal\":{\"name\":\"Computer Assisted Surgery\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2019-08-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/24699322.2019.1649072\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Assisted Surgery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1080/24699322.2019.1649072\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"SURGERY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Assisted Surgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/24699322.2019.1649072","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"SURGERY","Score":null,"Total":0}
Assisted therapeutic system based on reinforcement learning for children with autism
Abstract Assisted therapy is increasingly used in autism spectrum disorders (ASD) for improving social interaction and communication skills in recent years. A lot of studies have proven that the form of interactive games for therapy has a good effect on children with autism. Thus, our study provided an assisted therapeutic system based on Reinforcement Learning (RL) for children with ASD, which has five interactive subgames. As is well known, it is necessary to establish and maintain compelling interactions in therapeutic process. Therefore, we aim to adjust the interactive content according to the emotions of children with autism. However, due to the atypical and unusually differences in children with autism, most systems are based on off-line training of small samples of individuals and online recognition, so the existing assisted systems are limited in their ability to automatically update system parameters of new mappings. The integration of RL and Convolutional Neural Network (CNN)-Support Vector Regression (SVR) was used to deal with the updating online of prediction model’s weights. The normalized emotion labels were evaluated by the therapists. Eleven children with autism as subjects were invited in this experiment and captured facial video images. The experiment lasted for five weeks of intermittent assisted therapy, and the results were evaluated for the system and the therapy effect. Finally, we achieved a general reduction in the root mean square error of the model prediction results and labels. Although there is no significant difference in Social Responsiveness Scale (SRS) scores before and after assisted therapy (p value = 0.60), in individual subjects (Sub. 1, Sub. 2 and Sub.3), the SRS total score is significantly reduced (Average drop of 19 points). These results demonstrate the effectiveness of prediction model based on RL and show the feasibility of assisted therapeutic system in children with autism.
期刊介绍:
omputer Assisted Surgery aims to improve patient care by advancing the utilization of computers during treatment; to evaluate the benefits and risks associated with the integration of advanced digital technologies into surgical practice; to disseminate clinical and basic research relevant to stereotactic surgery, minimal access surgery, endoscopy, and surgical robotics; to encourage interdisciplinary collaboration between engineers and physicians in developing new concepts and applications; to educate clinicians about the principles and techniques of computer assisted surgery and therapeutics; and to serve the international scientific community as a medium for the transfer of new information relating to theory, research, and practice in biomedical imaging and the surgical specialties.
The scope of Computer Assisted Surgery encompasses all fields within surgery, as well as biomedical imaging and instrumentation, and digital technology employed as an adjunct to imaging in diagnosis, therapeutics, and surgery. Topics featured include frameless as well as conventional stereotactic procedures, surgery guided by intraoperative ultrasound or magnetic resonance imaging, image guided focused irradiation, robotic surgery, and any therapeutic interventions performed with the use of digital imaging technology.