Autism Spectrum Disorder Detection Using Enhanced Convolutional Neural Network and Wearable Sensors

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...

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Bibliographic Details
Published in:ITM web of conferences 2023, Vol.56, p.5018
Main Authors: Haroon, A. Syed, Padma, T.
Format: Article
Language:English
Subjects:
Accelerometers
and whale optimization
Artificial neural networks
Autism
autism spectrum disorders
convolutional neural network
multi-sensor
Optimization
stereotypical motor movements
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Summary: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.
ISSN:2271-2097
2431-7578
2271-2097
DOI:10.1051/itmconf/20235605018