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Improving communication skills of children with autism through support of applied behavioral analysis treatments using multimedia computing: a survey

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Abstract

Naturalistic applied behavior analysis (ABA) techniques have been shown to help children with autism improve their communication skills. Recognizing that individuals who interact with children regularly are in the position to utilize treatments with profound effects, researchers have examined methodologies for training parents, teachers, and peers to implement treatments. These programs are time intensive and often unable to support trainees after training. Technologies need to be examined to determine how they can aid in the educational and support process. Academic publications and publicly available training programs were reviewed to determine the types of participants, methodologies, and training durations that have been reported for instructing interventionists. These resources illustrate a need to make programs more accessible. To address this, selected computer science research is applied to methods of evaluating ABA implementations in order to recommend how the technologies could be utilized to make training and support programs more accessible. Review results of instructional programs, both in research and available in the community, illustrate the challenges in providing training in ABA methodologies. Modern research in multimedia data processing and machine learning could be applied to reduce the human cost of training and support individuals implementing ABA techniques. Utilizing machine learning techniques to analyze video probes of naturalistic ABA treatment implementation could alleviate the human cost of evaluating fidelity, allowing for greater support for individuals interested in the treatments. These technologies could be used in the future to expand data collection to provide more perspective on the treatments.

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References

  1. Aneeja, G., Yegnanarayana, B.: Single frequency filtering approach for discriminating speech and nonspeech. IEEE/ACM Trans. Audio Speech Lang. Process. (TASLP) 23(4), 705–717 (2015)

    Google Scholar 

  2. Baer, D.M., Wolf, M.M., Risley, T.R.: Some current dimensions of applied behavior analysis. J. Appl. Behav. Anal. 1(1), 91–97 (1968)

    Google Scholar 

  3. Bagaiolo, L.F., Mari, JdJ, Bordini, D., Ribeiro, T.C., Martone, M.C.C., Caetano, S.C., Brunoni, D., Brentani, H., Paula, C.S.: Procedures and compliance of a video modeling applied behavior analysis intervention for brazilian parents of children with autism spectrum disorders. Autism 21, 603–610 (2017)

    Google Scholar 

  4. Baker-Ericzén, M.J., Stahmer, A.C., Burns, A.: Child demographics associated with outcomes in a community-based pivotal response training program. J. Positive Behav. Interv. 9(1), 52–60 (2007)

    Google Scholar 

  5. Bastianelli, E., Castellucci, G., Croce, D., Basili, R., Nardi, D.: Effective and robust natural language understanding for human-robot interaction. In: Proceedings of the Twenty-first European Conference on Artificial Intelligence, pp. 57–62. IOS Press (2014)

  6. Baxter, R.H., Leach, M.J., Mukherjee, S.S., Robertson, N.M.: An adaptive motion model for person tracking with instantaneous head-pose features. IEEE Signal Process. Lett. 22(5), 578–582 (2015)

    Google Scholar 

  7. Bazzani, L., Cristani, M., Tosato, D., Farenzena, M., Paggetti, G., Menegaz, G., Murino, V.: Social interactions by visual focus of attention in a three-dimensional environment. Exp. Syst. 30(2), 115–127 (2013)

    Google Scholar 

  8. Boril, H., Zhang, Q., Ziaei, A., Hansen, J.H., Xu, D., Gilkerson, J., Richards, J.A., Zhang, Y., Xu, X., Mao, H., others: Automatic assessment of language background in toddlers through phonotactic and pitch pattern modeling of short vocalizations. In: WOCCI, pp. 39–43 (2014)

  9. Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2d pose estimation using part affinity fields. In: CVPR (2017)

  10. Chan, W., Jaitly, N., Le, Q., Vinyals, O.: Listen, attend and spell: A neural network for large vocabulary conversational speech recognition. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4960–4964. IEEE (2016)

  11. Chang, J.H., Kim, N.S., Mitra, S.K.: Voice activity detection based on multiple statistical models. IEEE Trans. Signal Process. 54(6), 1965–1976 (2006)

    MATH  Google Scholar 

  12. Chen, C.Y., Grauman, K.: Efficient activity detection in untrimmed video with max-subgraph search. IEEE Trans. Pattern Anal. Mach. Intell. 39(5), 908–921 (2017)

    Google Scholar 

  13. Children’s Hospital at Sacred Heart: Children’s hospital at sacred heart—autism center https://sacred-heart.org/childrenshospital/main/services/page/?id=1002. Acessed 19 May 2018 (2018)

  14. Choice Autism Center: Our programs. https://choiceautismcenter.com/our-programs/. Accessed 19 May 2018 (2018)

  15. Coolican, J., Smith, I.M., Bryson, S.E.: Brief parent training in pivotal response treatment for preschoolers with autism. J. Child Psychol. Psychiatry 51(12), 1321–1330 (2010)

    Google Scholar 

  16. Coronato, A., De Pietro, G., Paragliola, G.: A situation-aware system for the detection of motion disorders of patients with autism spectrum disorders. Exp. Syst. Appl. 41(17), 7868–7877 (2014)

    Google Scholar 

  17. Dave, N.: Feature extraction methods LPC, PLP and MFCC in speech recognition. Int. J. Adv. Res. Eng. Technol. 1(6), 1–4 (2013)

    Google Scholar 

  18. Deng, J., Cummins, N., Schmitt, M., Qian, K., Ringeval, F., Schuller, B.: Speech-based diagnosis of autism spectrum condition by generative adversarial network representations. In: Proceedings of the 2017 International Conference on Digital Health, pp. 53–57. ACM (2017)

  19. Deng, Z., Vahdat, A., Hu, H., Mori, G.: Structure inference machines: Recurrent neural networks for analyzing relations in group activity recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4772–4781 (2016)

  20. Dorfmüller, K.: Robust tracking for augmented reality using retroreflective markers. Comput. Gr. 23(6), 795–800 (1999)

    Google Scholar 

  21. Drugman, T., Stylianou, Y., Kida, Y., Akamine, M.: Voice activity detection: merging source and filter-based information. IEEE Signal Process. Lett. 23(2), 252–256 (2016)

    Google Scholar 

  22. Dudy, S., Bedrick, S., Asgari, M., Kain, A.: Automatic analysis of pronunciations for children with speech sound disorders. Comput. Speech Lang. 50, 62–84 (2017)

    Google Scholar 

  23. Duffner, S., Garcia, C.: Visual focus of attention estimation with unsupervised incremental learning. IEEE Trans. Circuits Syst. Video Technol. 26(12), 2264–2272 (2016)

    Google Scholar 

  24. Estes, A., Vismara, L., Mercado, C., Fitzpatrick, A., Elder, L., Greenson, J., Lord, C., Munson, J., Winter, J., Young, G.: The impact of parent-delivered intervention on parents of very young children with autism. J. Autism Dev. Disord. 44(2), 353–365 (2014)

    Google Scholar 

  25. Foster, M.E., Gaschler, A., Giuliani, M.: How can i help you’: comparing engagement classification strategies for a robot bartender. In: Proceedings of the 15th ACM on International conference on multimodal interaction, pp. 255–262. ACM (2013)

  26. Fragkiadaki, K., Levine, S., Felsen, P., Malik, J.: Recurrent network models for human dynamics. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4346–4354 (2015)

  27. Gengoux, G.W., Berquist, K.L., Salzman, E., Schapp, S., Phillips, J.M., Frazier, T.W., Minjarez, M.B., Hardan, A.Y.: Pivotal response treatment parent training for autism: findings from a 3-month follow-up evaluation. J. Autism Dev. Disord. 45(9), 2889–2898 (2015)

    Google Scholar 

  28. Gillesen, J.C., Barakova, E., Huskens, B.E., Feijs, L.M.: From training to robot behavior: Towards custom scenarios for robotics in training programs for ASD. In: 2011 IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 1–7. IEEE (2011)

  29. Gillett, J.N., LeBlanc, L.A.: Parent-implemented natural language paradigm to increase language and play in children with autism. Res. Autism Spec. Disord. 1(3), 247–255 (2007)

    Google Scholar 

  30. Goldberg, Y.: Neural network methods for natural language processing. Synth. Lect. Human Lang. Technol. 10(1), 1–309 (2017)

    Google Scholar 

  31. Gosztolya, G.: Detecting laughter and filler events by time series smoothing with genetic algorithms. In: International Conference on Speech and Computer, pp. 232–239 (2016)

  32. Gosztolya, G., Grósz, T., Busa-Fekete, R., Tóth, L.: Determining native language and deception using phonetic features and classifier combination. Interspeech (2016)

  33. Graves, A., Mohamed, A.r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (icassp), pp. 6645–6649. IEEE (2013)

  34. Górriz, J.M., Ramírez, J., Lang, E.W., Puntonet, C.G.: Hard c-means clustering for voice activity detection. Speech Commun. 48(12), 1638–1649 (2006)

    Google Scholar 

  35. Hardan, A.Y., Gengoux, G.W., Berquist, K.L., Libove, R.A., Ardel, C.M., Phillips, J., Frazier, T.W., Minjarez, M.B.: A randomized controlled trial of pivotal response treatment group for parents of children with autism. J. Child Psychol. Psychiatry 56(8), 884–892 (2015)

    Google Scholar 

  36. Harper, C.B., Symon, J.B., Frea, W.D.: Recess is time-in: using peers to improve social skills of children with autism. J. Autism Dev. Disord. 38(5), 815–826 (2008)

    Google Scholar 

  37. Heath, C.D., Venkateswara, H., McDaniel, T., Panchanathan, S.: Detecting attention in pivotal response treatment video probes. In: International Conference on Smart Multimedia (2018)

  38. Heath, C.D., McDaniel, T., Venkateswara, H., Panchanathan, S.: Parent and child voice activity detection in pivotal response treatment video probes. In: Human Computer Interaction International (2019)

  39. Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)

    Google Scholar 

  40. Jain, A., Zamir, A.R., Savarese, S., Saxena, A.: Structural-RNN: Deep learning on spatio-temporal graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5308–5317 (2016)

  41. Jazouli, M., Elhoufi, S., Majda, A., Zarghili, A., Aalouane, R.: Stereotypical motor movement recognition using microsoft kinect with artificial neural network. World Acad. Sci. Eng. Technol. Int. J. Comput. Electr. Autom. Control Inf. Eng. 10(7), 1270–1274 (2016)

    Google Scholar 

  42. Jernite, Y., Srinet, K., Gray, J., Szlam, A.: CraftAssist instruction parsing: semantic parsing for a minecraft assistant. Preprint arXiv:1905.01978 (2019)

  43. Johnson, N., Frenn, M., Feetham, S., Simpson, P.: Autism spectrum disorder: parenting stress, family functioning and health-related quality of life. Fam. Syst. Health 29(3), 232 (2011)

    Google Scholar 

  44. Jones, E.A., Feeley, K.M.: Parent implemented joint attention intervention for preschoolers with autism. J. Speech Lang. Pathol. Appl. Behav. Anal. 4(1), 74–89 (2009). https://doi.org/10.1037/h0100251

    Article  Google Scholar 

  45. Kane, M., Connell, J.E., Pellecchia, M.: A quantitative analysis of language interventions for children with autism. Behav. Anal. Today 11(2), 128 (2010)

    Google Scholar 

  46. Kasari, C., Gulsrud, A., Paparella, T., Hellemann, G., Berry, K.: Randomized comparative efficacy study of parent-mediated interventions for toddlers with autism. J. Consult. Clin. Psychol. 83(3), 554 (2015)

    Google Scholar 

  47. Khan, N.A., Sawand, M.A., Qadeer, M., Owais, A., Junaid, S., Shahnawaz, P.: Autism detection using computer vision. Int. J. Comput. Sci. Netw. Secur. (IJCSNS) 17(4), 256 (2017)

    Google Scholar 

  48. Kim, J., Hahn, M.: Voice activity detection using an adaptive context attention model. IEEE Signal Process. Lett. 25(8), 1181 (2018)

    Google Scholar 

  49. Kim, J., Englebienne, G., Truong, K., Evers, V.: Towards speech emotion recognition” in the wild” using aggregated corpora and deep multi-task learning. Interspeech (2017)

  50. Kitsantas, A., Kavussanu, M.: Acquisition of sport knowledge and skill. In: Zimmerman, B., Schunk, D. (eds.) Handbook of Self-regulation of Learning and Performance, pp. 217–233. Routledge, New York, London (2011)

    Google Scholar 

  51. Koegel, L.K., Camarata, S.M., Valdez-Menchaca, M., Koegel, R.L.: Setting generalization of question-asking by children with autism. Am. J. Mental Retard. 102(4), 346–357 (1997)

    Google Scholar 

  52. Koegel, L.K., Koegel, R.L., Harrower, J.K., Carter, C.M.: Pivotal response intervention i: overview of approach. J. Assoc. Persons Severe Handicaps 24(3), 174–185 (1999)

    Google Scholar 

  53. Koegel, L.K., Koegel, R.L., Shoshan, Y., McNerney, E.: Pivotal response intervention II: preliminary long-term outcome data. J. Assoc. Persons Severe Handicaps 24(3), 186–198 (1999)

    Google Scholar 

  54. Koegel, L.K., Carter, C.M., Koegel, R.L.: Teaching children with autism self-initiations as a pivotal response. Top. Lang. Disord. 23(2), 134–145 (2003)

    Google Scholar 

  55. Koegel, L.K., Koegel, R.L., Green-Hopkins, I., Barnes, C.C.: Brief report: question-asking and collateral language acquisition in children with autism. J. Autism Dev. Disord. 40(4), 509–515 (2010)

    Google Scholar 

  56. Koegel, L.K., Singh, A.K., Koegel, R.L., Hollingsworth, J.R., Bradshaw, J.: Assessing and improving early social engagement in infants. J. Positive Behav. Interv. 16(2), 69–80 (2014)

    Google Scholar 

  57. Koegel, R.L., Schreibman, L., Good, A., Cerniglia, L., Murphy, C., Koegel, L.: How to teach pivotal behaviors to children with autism: a training manual. University of California, Santa Barbara (1988)

    Google Scholar 

  58. Koegel, R.L.: A natural language teaching paradigm for nonverbal autistic children. J. Autism Dev. Disord. 17(2), 187–200 (1987)

    Google Scholar 

  59. Koegel, R.L., O’Dell, M., Dunlap, G.: Producing speech use in nonverbal autistic children by reinforcing attempts. J. Autism Dev. Disord. 18(4), 525–538 (1988)

    Google Scholar 

  60. Koegel, R.L., Koegel, L.K., Surratt, A.: Language intervention and disruptive behavior in preschool children with autism. J. Autism Dev. Disord. 22(2), 141–153 (1992)

    Google Scholar 

  61. Koegel, R.L., Bimbela, A., Schreibman, L.: Collateral effects of parent training on family interactions. J. Autism Dev. Disord. 26(3), 347–359 (1996)

    Google Scholar 

  62. Koegel, R.L., Camarata, S., Koegel, L.K., Ben-Tall, A., Smith, A.E.: Increasing speech intelligibility in children with autism. J. Autism Dev. Disord. 28(3), 241–251 (1998)

    Google Scholar 

  63. Koegel, R.L., Symon, J.B., Kern Koegel, L.: Parent education for families of children with autism living in geographically distant areas. J. Positive Behav. Interv. 4(2), 88–103 (2002)

    Google Scholar 

  64. Koegel, R.L., Vernon, T.W., Koegel, L.K.: Improving social initiations in young children with autism using reinforcers with embedded social interactions. J. Autism Dev. Disord. 39(9), 1240–1251 (2009)

    Google Scholar 

  65. Koegel, R.L., Bradshaw, J.L., Ashbaugh, K., Koegel, L.K.: Improving question-asking initiations in young children with autism using pivotal response treatment. J. Autism Dev. Disord. 44(4), 816–827 (2014)

    Google Scholar 

  66. Koh, Y.J., Kim, C.S.: Primary object segmentation in videos based on region augmentation and reduction. In: CVPR, vol. 1, p. 7 (2017)

  67. Kumar, M., Bone, D., McWilliams, K., Williams, S., Lyon, T.D., Narayanan, S.: Multi-scale context adaptation for improving child automatic speech recognition in child-adult spoken interactions. Proc. Interspeech 2017, 2730–2734 (2017)

    Google Scholar 

  68. Laski, K.E., Charlop, M.H., Schreibman, L.: Training parents to use the natural language paradigm to increase their autistic children’s speech. J. Appl. Behav. Anal. 21(4), 391–400 (1988)

    Google Scholar 

  69. Lawton, K., Kasari, C.: Teacher-implemented joint attention intervention: pilot randomized controlled study for preschoolers with autism. J. Consul. Clin. Psychol. 80(4), 687 (2012)

    Google Scholar 

  70. Leaf, J.B., Leaf, R., McEachin, J., Taubman, M., Ala’i-Rosales, S., Ross, R.K., Smith, T., Weiss, M.J.: Applied behavior analysis is a science and therefore, progressive. J. Autism Dev. Disord. 46(2), 720–731 (2016)

    Google Scholar 

  71. Lecavalier, L., Smith, T., Johnson, C., Bearss, K., Swiezy, N., Aman, M.G., Sukhodolsky, D.G., Deng, Y., Dziura, J., Scahill, L.: Moderators of parent training for disruptive behaviors in young children with autism spectrum disorder. J. Abnormal Child Psychol. 45(6), 1235–1245 (2017)

    Google Scholar 

  72. Lee, S., Potamianos, A., Narayanan, S.: Acoustics of children’s speech: developmental changes of temporal and spectral parameters. J. Acoust. Soc. Am. 105(3), 1455–1468 (1999)

    Google Scholar 

  73. Li, J., Deng, L., Gong, Y., Haeb-Umbach, R.: An overview of noise-robust automatic speech recognition. IEEE/ACM Trans. Audio Speech Lang. Process. 22(4), 745–777 (2014)

    Google Scholar 

  74. Li, L., Xu, Q., Tan, Y.K.: Attention-based addressee selection for service and social robots to interact with multiple persons. In: Proceedings of the Workshop at SIGGRAPH Asia, pp. 131–136. ACM (2012)

  75. Liao, H., Pundak, G., Siohan, O., Carroll, M.K., Coccaro, N., Jiang, Q.M., Sainath, T.N., Senior, A., Beaufays, F., Bacchiani, M.: Large vocabulary automatic speech recognition for children. In: Sixteenth Annual Conference of the International Speech Communication Association, pp. 1611–1615 (2015)

  76. Ma, S., Sigal, L., Sclaroff, S.: Learning activity progression in lstms for activity detection and early detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1942–1950 (2016)

  77. Machalicek, W., O’Reilly, M.F., Rispoli, M., Davis, T., Lang, R., Franco, J.H., Chan, J.M.: Training teachers to assess the challenging behaviors of students with autism using video tele-conferencing. Educ. Train. Autism Dev. Disabil. 45, 203–215 (2010)

    Google Scholar 

  78. Marchi, E., Schuller, B., Baron-Cohen, S., Golan, O., Bölte, S., Arora, P., Häb-Umbach, R.: Typicality and emotion in the voice of children with autism spectrum condition: Evidence across three languages. In: Sixteenth Annual Conference of the International Speech Communication Association, pp. 115–119 (2015)

  79. McLoughlin, I.V.: The use of low-frequency ultrasound for voice activity detection. In: Fifteenth Annual Conference of the International Speech Communication Association (2014)

  80. Mehner, W., Boltes, M., Mathias, M., Leibe, B.: Robust marker-based tracking for measuring crowd dynamics. In: International Conference on Computer Vision Systems, pp. 445–455. Springer (2015)

  81. Microsoft: Microsoft cognitive toolkit (CNTK), an open source deep-learning toolkit https://github.com/microsoft/cntk. Accessed 24 June 2018 (2018)

  82. Mohammadzaheri, F., Koegel, L.K., Rezaee, M., Rafiee, S.M.: A randomized clinical trial comparison between pivotal response treatment (PRT) and structured applied behavior analysis (ABA) intervention for children with autism. J. Autism Dev. Disord. 44(11), 2769–2777 (2014)

    Google Scholar 

  83. Mohammadzaheri, F., Koegel, L.K., Rezaei, M., Bakhshi, E.: A randomized clinical trial comparison between pivotal response treatment (PRT) and adult-driven applied behavior analysis (ABA) intervention on disruptive behaviors in public school children with autism. J. Autism Dev. Disord. 45(9), 2899–2907 (2015)

    Google Scholar 

  84. Naim, I., Tanveer, M.I., Gildea, D., Hoque, M.E.: Automated prediction and analysis of job interview performance: the role of what you say and how you say it. In: 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), vol. 1, pp. 1–6. IEEE (2015)

  85. Nefdt, N., Koegel, R., Singer, G., Gerber, M.: The use of a self-directed learning program to provide introductory training in pivotal response treatment to parents of children with autism. J. Positive Behav. Interv. 12(1), 23–32 (2010)

    Google Scholar 

  86. Pawar, R., Albin, A., Gupta, U., Rao, H., Carberry, C., Hamo, A., Jones, R.M., Lord, C., Clements, M.A.: Automatic analysis of LENA recordings for language assessment in children aged five to fourteen years with application to individuals with autism. In: 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pp. 245–248. IEEE (2017)

  87. Perrier, A.: Google upgrades its speech-to-text service with tailored deep-learning models https://www.infoq.com/news/2018/05/google-speech-to-text-api. Accessed 24 June 2018 (2018)

  88. Pi, J., Gu, Y., Hu, K., Cheng, X., Zhan, Y., Wang, Y.: Real-time scale-adaptive correlation filters tracker with depth information to handle occlusion. J. Electron. Imag. 25(4), 043022 (2016)

    Google Scholar 

  89. Pierce, K., Schreibman, L.: Increasing complex social behaviors in children with autism: effects of peer-implemented pivotal response training. J. Appl. Behav. Anal. 28(3), 285–295 (1995)

    Google Scholar 

  90. Pierce, K., Schreibman, L.: Multiple peer use of pivotal response training to increase social behaviors of classmates with autism: results from trained and untrained peers. J. Appl. Behav. Anal. 30(1), 157–160 (1997)

    Google Scholar 

  91. Poon, H., Domingos, P.: Unsupervised semantic parsing. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: vol. 1, pp. 1–10. Association for Computational Linguistics (2009)

  92. Poria, S., Cambria, E., Howard, N., Huang, G.B., Hussain, A.: Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing 174, 50–59 (2016)

    Google Scholar 

  93. Potamianos, A., Narayanan, S.: Spoken dialog systems for children. In: Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, 1998, vol. 1, pp. 197–200. IEEE (1998)

  94. Pradhan, S.S., Ward, W.H., Hacioglu, K., Martin, J.H., Jurafsky, D.: Shallow semantic parsing using support vector machines. Proc. Human Lang. Technol. Conf. North Am. Chapter Assoc. Comput. Linguist. HLT-NAACL 2004, 233–240 (2004)

    Google Scholar 

  95. Presti, L., Sclaroff, S., Rozga, A.: Joint alignment and modeling of correlated behavior streams. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 730–737 (2013)

  96. Pusiol, G., Soriano, L., Frank, M.C., Fei-Fei, L.: Discovering the signatures of joint attention in child-caregiver interaction. In: Proceedings of the Cognitive Science Society, vol. 36 (2014)

  97. Rajagopalan, S.S., Murthy, O.R., Goecke, R., Rozga, A.: Play with me-measuring a child’s engagement in a social interaction. In: 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), vol. 1, pp. 1–8. IEEE (2015)

  98. Rajagopalan, S.S., Morency, L.P., Baltrusaitis, T., Goecke, R.: Extending long short-term memory for multi-view structured learning. In: European Conference on Computer Vision, pp. 338–353. Springer (2016)

  99. Raptis, M., Sigal, L.: Poselet key-framing: A model for human activity recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2650–2657 (2013)

  100. Sadjadi, S.O., Hansen, J.H.: Unsupervised speech activity detection using voicing measures and perceptual spectral flux. IEEE Signal Process. Lett. 20(3), 197–200 (2013)

    Google Scholar 

  101. Sainath, T.N., Vinyals, O., Senior, A., Sak, H.: Convolutional, long short-term memory, fully connected deep neural networks. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4580–4584. IEEE (2015)

  102. Sanghvi, J., Castellano, G., Leite, I., Pereira, A., McOwan, P.W., Paiva, A.: Automatic analysis of affective postures and body motion to detect engagement with a game companion. In: 2011 6th ACM/IEEE International Conference on Human–Robot Interaction (HRI), pp. 305–311. IEEE (2011)

  103. Schreibman, L., Kaneko, W.M., Koegel, R.L.: Positive affect of parents of autistic children: a comparison across two teaching techniques. Behav. Ther. 22(4), 479–490 (1991)

    Google Scholar 

  104. Schreibman, L., Dawson, G., Stahmer, A.C., Landa, R., Rogers, S.J., McGee, G.G., Kasari, C., Ingersoll, B., Kaiser, A.P., Bruinsma, Y.: others: Naturalistic developmental behavioral interventions: empirically validated treatments for autism spectrum disorder. J. Autism Dev. Disord. 45(8), 2411–2428 (2015)

    Google Scholar 

  105. Sener, F., Ikizler-Cinbis, N.: Two-person interaction recognition via spatial multiple instance embedding. J. Vis. Commun. Image Represent. 32, 63–73 (2015)

    Google Scholar 

  106. Shaalan, K.: Extending prolog for better natural language analysis. In: Proceeding of the 1st Conference on Language Engineering, Egyptian Society of Language Engineering (ELSE), pp. 225–236 (2019)

  107. Shahroudy, A., Liu, J., Ng, T.T., Wang, G.: NTU RGB+ d: A large scale dataset for 3d human activity analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1010–1019 (2016)

  108. Sherer, M.R., Schreibman, L.: Individual behavioral profiles and predictors of treatment effectiveness for children with autism. J. Consul. Clin. Psychol. 73(3), 525 (2005)

    Google Scholar 

  109. Shin, J.W., Chang, J.H., Kim, N.S.: Voice activity detection based on statistical models and machine learning approaches. Comput. Speech Lang. 24(3), 515–530 (2010)

    Google Scholar 

  110. Shivakumar, S.S., Loeb, H., Bogen, D.K., Shofer, F., Bryant, P., Prosser, L., Johnson, M.J.: Stereo 3d tracking of infants in natural play conditions. In: 2017 International Conference on Rehabilitation Robotics (ICORR), pp. 841–846. IEEE (2017)

  111. Smith, D., Sneddon, A., Ward, L., Duenser, A., Freyne, J., Silvera-Tawil, D., Morgan, A.: Improving child speech disorder assessment by incorporating out-of-domain adult speech. Proc. Interspeech 2017, 2690–2694 (2017)

    Google Scholar 

  112. Smith, I.M., Flanagan, H.E., Garon, N., Bryson, S.E.: Effectiveness of community-based early intervention based on pivotal response treatment. J. Autism Dev. Disord. 45(6), 1858–1872 (2015)

    Google Scholar 

  113. SoundHound Inc.: Houndify by SoundHound inc. https://houndify.com/. Accessed 2 Feb 2019 (2019)

  114. Southwest Autism Research and Resouce Center: Parents and caregivers https://autismcenter.org/parents-and-caregivers. Accessed 19 May 2018 (2016)

  115. Stahmer, A.C.: Teaching symbolic play skills to children with autism using pivotal response training. J. Autism Dev. Disord. 25(2), 123–141 (1995)

    Google Scholar 

  116. Stahmer, A.C., Schreibman, L., Powell, N.P.: Social validation of symbolic play training for children with autism. J. Early Intensive Behav. Interv. 3(2), 196 (2006)

    Google Scholar 

  117. Steiner, A.M., Gengoux, G.W., Klin, A., Chawarska, K.: Pivotal response treatment for infants at-risk for autism spectrum disorders: a pilot study. J. Autism Dev. Disord. 43(1), 91–102 (2013)

    Google Scholar 

  118. Suhrheinrich, J., Chan, J.: Exploring the effect of immediate video feedback on coaching. J. Spec. Educ. Technol. 32(1), 47–53 (2017)

    Google Scholar 

  119. Suhrheinrich, J., Reed, S., Schreibman, L., Bolduc, C.: Classroom pivotal response teaching for children with autism. Guilford Press, New York (2011)

    Google Scholar 

  120. Symon, J.B.: Expanding interventions for children with autism: parents as trainers. J. Positive Behav. Interv. 7(3), 159–173 (2005)

    Google Scholar 

  121. Tamura, Y., Yano, S., Osumi, H.: Modeling of human attention based on analysis of magic. In: Proceedings of the 2014 ACM/IEEE international conference on Human–robot interaction, pp. 302–303. ACM (2014)

  122. Tamura, Y., Akashi, T., Yano, S., Osumi, H.: Human visual attention model based on analysis of magic for smooth human–robot interaction. Int. J. Soc. Robot. 8(5), 685–694 (2016)

    Google Scholar 

  123. The Help Group: Parenting classes. http://www.thehelpgroup.org/parent/parenting-classes/. Accessed 19 May 2018 (2018)

  124. Thorp, D.M., Stahmer, A.C., Schreibman, L.: Effects of sociodramatic play training on children with autism. J. Autism Dev. Disord. 25(3), 265–282 (1995)

    Google Scholar 

  125. Tripathi, S., Lipton, Z.C., Belongie, S., Nguyen, T.: Context matters: Refining object detection in video with recurrent neural networks. Preprint arXiv:1607.04648 (2016)

  126. Tsatsoulis, P.D., Kordas, P., Marshall, M., Forsyth, D., Rozga, A.: The static multimodal dyadic behavior dataset for engagement prediction. In: Computer Vision-ECCV 2016 Workshops, pp. 386–399. Springer (2016)

  127. University of Washington: Parent family trainings. https://depts.washington.edu/uwautism/training/uwac-workshops/parentfamily/. Accessed 19 May 2018 (2018)

  128. Van Gemeren, C., Poppe, R., Veltkamp, R.C.: Spatio-temporal detection of fine-grained dyadic human interactions. In: International Workshop on Human Behavior Understanding, pp. 116–133. Springer (2016)

  129. Ventola, P., Friedman, H.E., Anderson, L.C., Wolf, J.M., Oosting, D., Foss-Feig, J., McDonald, N., Volkmar, F., Pelphrey, K.A.: Improvements in social and adaptive functioning following short-duration PRT program: a clinical replication. J. Autism Dev. Disord. 44(11), 2862–2870 (2014)

    Google Scholar 

  130. Vismara, L.A., Lyons, G.L.: Using perseverative interests to elicit joint attention behaviors in young children with autism: theoretical and clinical implications for understanding motivation. J. Positive Behav. Interv. 9(4), 214–228 (2007)

    Google Scholar 

  131. Vismara, L.A., Young, G.S., Stahmer, A.C., Griffith, E.M., Rogers, S.J.: Dissemination of evidence-based practice: Can we train therapists from a distance? J. Autism Dev. Disord. 39(12), 1636 (2009)

    Google Scholar 

  132. Vismara, L.A., Young, G.S., Rogers, S.J.: Telehealth for expanding the reach of early autism training to parents. Autism Res. Treat. 2012, 12 (2012)

    Google Scholar 

  133. Vismara, L.A., McCormick, C., Young, G.S., Nadhan, A., Monlux, K.: Preliminary findings of a telehealth approach to parent training in autism. J. Autism Dev. Disord. 43(12), 2953–2969 (2013)

    Google Scholar 

  134. Wang, Y., Berant, J., Liang, P.: Building a semantic parser overnight. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Vol. 1: Long Papers), pp. 1332–1342 (2015)

  135. Wei, P., Xie, D., Zheng, N., Zhu, S.C.: Inferring human attention by learning latent intentions. In: Proceedings of the Twenty-Sixth International Joint Conference of Artificial Intelligence (2017)

  136. Wong, C., Odom, S.L., Hume, K.A., Cox, A.W., Fettig, A., Kucharczyk, S., Brock, M.E., Plavnick, J.B., Fleury, V.P., Schultz, T.R.: Evidence-based practices for children, youth, and young adults with autism spectrum disorder: a comprehensive review. J. Autism Dev. Disord. 45(7), 1951–1966 (2015)

    Google Scholar 

  137. Xu, D., Yapanel, U., Gray, S., Gilkerson, J., Richards, J., Hansen, J.: Signal processing for young child speech language development. In: First Workshop on Child, Computer and Interaction (2008)

  138. Xu, D., Gilkerson, J., Richards, J., Yapanel, U., Gray, S.: Child vocalization composition as discriminant information for automatic autism detection. In: Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, pp. 2518–2522. IEEE (2009)

  139. Young, T., Hazarika, D., Poria, S., Cambria, E.: Recent trends in deep learning based natural language processing. Preprint arXiv:1708.02709 (2017)

  140. Zhang, X.L., Wang, D.: Boosting contextual information for deep neural network based voice activity detection. IEEE/ACM Trans. Audio Speech Lang. Process. (TASLP) 24(2), 252–264 (2016)

    Google Scholar 

  141. Zhang, Y., Liu, X., Chang, M.C., Ge, W., Chen, T.: Spatio-temporal phrases for activity recognition. In: European Conference on Computer Vision, pp. 707–721. Springer (2012)

  142. Zhang, Y., Pezeshki, M., Brakel, P., Zhang, S., Bengio, C.L.Y., Courville, A.: Towards end-to-end speech recognition with deep convolutional neural networks. Preprint arXiv:1701.02720 (2017)

  143. Zhang, Y., Yan, D., Yuan, Y.: An object tracking algorithm with embedded gyro information. In: Seventh International Conference on Electronics and Information Engineering, vol. 10322, p. 103220U. International Society for Optics and Photonics (2017)

  144. Zhao, R., Ali, H., van der Smagt, P.: Two-stream RNN/CNN for action recognition in 3d videos. Preprint arXiv:1703.09783 (2017)

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Acknowledgements

The authors thank Arizona State University and the National Science Foundation for their funding support. This material is partially based upon work supported by the National Science Foundation under Grant Nos. 1069125 and 1828010.

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  1. School of Computing, Informatics and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85251, USA

    Corey D. C. Heath, Troy McDaniel, Hemanth Venkateswara & Sethuraman Panchanathan

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Heath, C.D.C., McDaniel, T., Venkateswara, H. et al. Improving communication skills of children with autism through support of applied behavioral analysis treatments using multimedia computing: a survey. Univ Access Inf Soc 20, 13–30 (2021). https://doi.org/10.1007/s10209-019-00707-5

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