Abstract
Adaptive behavioural assessments are useful in the diagnosis of autism. This research proposes a strategy of assessing autistic children’s adaption skills through the change of hand behaviour complexity based on deep learning and complex systems. Specifically, we implement a sparse representation of high-dimensional features of hand movements utilize convolutional neural network (CNN) and Bag of Word model (BoW) and explain in detail how two quantify measurements (complexity and diversity) reflect adaption behavioural capacity. This paper introduces our ongoing projects and demonstrates the preliminary experimental setups and motion protocol design. Future work includes improving the interaction scenarios, establishing a data set, and enhancing the interpretability of the results of the adaption behaviour skill measurements.
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Zhang, D., Toptan, C.M., Zhao, S., Zhang, G., Liu, H. (2022). ASD Children Adaption Behaviour Assessment via Hand Movement Properties: A RoadMap. In: Jansen, T., Jensen, R., Mac Parthaláin, N., Lin, CM. (eds) Advances in Computational Intelligence Systems. UKCI 2021. Advances in Intelligent Systems and Computing, vol 1409. Springer, Cham. https://doi.org/10.1007/978-3-030-87094-2_42
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DOI: https://doi.org/10.1007/978-3-030-87094-2_42
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