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

Abstract

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.