ITM Web Conf.
Volume 56, 2023First International Conference on Data Science and Advanced Computing (ICDSAC 2023)
|Number of page(s)||11|
|Section||Machine Learning & Neural Networks|
|Published online||09 August 2023|
Autism Spectrum Disorder Detection Using Enhanced Convolutional Neural Network and Wearable Sensors
1 Research Scholar, Bharathiar University, Coimbatore, Tamil Nadu, India
2 MCA Department, Sona College of Technology, Salem, Tamil Nadu, India
* Corresponding author: email@example.com
Stereotypical Motor Movements (SMMs) may seriously impede learning and social relationships are one of the distinctive and typical postural or motor behaviours linked with autism spectrum disorders (ASDs). A reliable infrastructure for automatic and quick SMM detection is provided by wireless retail sensor technology, which would facilitate targeted intervention and perhaps provide early warning of meltdown occurrences. However, because of significant inter- and intra-subject variability that is challenging for handmade features to handle, the detection and quantification of SMM patterns remain challenging. In this work, we suggest using the Enhanced Convolutional Neural Network (ECNN) to extract distinguishing characteristics directly from multi-sensor accelerometer inputs. Parameters of the ECNN are tuned using whale optimization. Results with Enhanced convolutional neural networks produce accurate and robust SMM detectors.
Key words: Autism Spectrum Disorders / Stereotypical Motor Movements / Convolutional neural network / Multi-Sensor / and Whale Optimization
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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