The authors of this article have designed an Internet-of-Things (IoT) framework named WearSense that leverages the sensing capabilities of modern smartwatches to detect stereotypic behaviors in children with autism. Their study used the inbuilt accelerometer of a smartwatch to detect three behaviors, including hand flapping , painting, and sibbing that are commonly observed in children with autism. They used and compared several classification techniques obtained an accuracy of 94.6%, which shows the quality of the data collected from the smartwatch and feature extraction methods used in the study. The automatic recognition of these behaviors would be helpful in monitoring individuals with autistic behaviors, since the smartwatch can send the data to the cloud for comprehensive analysis and also to help parents, caregivers, and clinicians make informed decisions.
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