Given the current increasing prevalence of autism, expensive and time-consuming manual diagnosis is highly detrimental to the management of the condition. With the development of computer-based methods of human behavioural analysis, these methods are expected to provide more accurate, objective and reproducible methods of early screening and diagnosis of autism. To advance the field of behavioural quantification in autism research, this study utilises human skeletal behavioural data from publicly available autism datasets and ADOS scores from clinical professionals in a first attempt to build deep neural networks that can predict ADOS scores from behavioural data using the AQA approach. This paper finds a moderately correlated between the ground truth ADOS score and the predicted ADOS score, it reveals the potential use of the AQA method in ASD diagnoses.
|2023 International Conference on Machine Learning and Cybernetics (ICMLC)
|The 22nd International Conference on Machine Learning and Cybernetics
|9/07/23 → 11/07/23
- behaviour evaluation