Abstract
According to the literature review the autistic children behavior is strictly connected to their emotions. Moreover, it is repeatable and – although it is understandable only for their caregivers – it seems to be predictable. We state a hypothesis supported by the literature’s findings that it should be possible to describe autistic child’s behavior using the statistic models. However, every autistic person is different and therefore the model should be developed individually for each of them. We present a preliminary research on behavior-based emotion recognition and methods for behavior model estimation by using Hidden Markov Models. The behavior model may be created for any combination of following types of events: body positions, activities based on position changes, activities based on hand movements. The conducted experiments provide very satisfactory results. The major conclusion is to use the complex events such as activities.
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References
Seach, D., Lloyd, M., Preston, M.: Supporting Children with Autism in Mainstreem Schools. The Questions Publishing Company Ltd. (2003). ISBN 83-60215-17-0
American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders, 4th edn. American Psychiatric Publishing Inc., Washington (2013)
Autism (in Polish). http://dajmiczas.pl/autyzm/
Ricks, D.M., Wing, L.: Language, communication, and the use of symbols in normal and autistic children. J. Autism Child. Schizophr. 5(3), 191–221 (1975)
Endelson, S.M., Johnson, J.B.: Autoaggressive behaviors in autism. The reasons and procedures. Wydawnictwo Harmonia (2018). (in Polish)
Ekman, P., Friesen, W.V.: Unmasking the Face. A Guide to Recognizing Emotions from Facial Clues. Institute for Study of Human Knowledge (2015)
Kahou, S.E., et al.: EmoNets: multimodal deep learning approaches for emotion recognition in video. J. Multimodal User Interfaces 10(2), 99–111 (2016)
Dhall, A., Ramana Murthy, O.V., Goecke, R., Joshi, J., Gedeon, T.: Video and image based emotion recognition challenges in the wild: EmotiW 2015. In: Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, pp. 423–426 (2015)
Trigeorgis, G., et al.: Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5200–5204 (2016)
Han, K., Yu, D., Tashev, I.: Speech emotion recognition using deep neural network and extreme learning machine. In: INTERSPEECH-2014, no. September, pp. 223–227 (2014)
Schuller, B., Rigoll, G., Lang, M.: Hidden Markov model-based speech emotion recognition. In: 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2003), vol. 2, pp. 401–404 (2003)
Manzi, A., Fiorini, L., Limosani, R., Dario, P., Cavallo, F.: Two-person activity recognition using skeleton data. IET Comput. Vis. 12, 27–35 (2017)
Rabiner, L., Juang, B.: An introduction to hidden Markov models. IEEE ASSP Mag. 3(January), 4–16 (1986)
Postawka, A., Śliwiński, P.: A kinect-based support system for children with autism spectrum disorder. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS (LNAI), vol. 9693, pp. 189–199. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39384-1_17
Postawka, A.: Exercise recognition using averaged hidden Markov models. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2017, Part II. LNCS (LNAI), vol. 10246, pp. 137–147. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59060-8_14
Postawka, A., Rudy, J.: Lifelogging system based on averaged hidden Markov models: dangerous activities recognition. Comput. Sci. 19(3), 257–278 (2018)
Postawka, A.: Real-time monitoring system for potentially dangerous activities detection. In: Proceedings of the 22nd International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 1005–1008. IEEE Xplore Digital Library (2017)
Acknowledgment
This work was supported by the statutory funds of the Faculty of Electronics 0401/0140/18, Wroclaw University of Science and Technology, Wroclaw, Poland.
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Postawka, A. (2019). Behavior-Based Emotion Recognition Using Kinect and Hidden Markov Models. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11509. Springer, Cham. https://doi.org/10.1007/978-3-030-20915-5_23
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DOI: https://doi.org/10.1007/978-3-030-20915-5_23
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