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dc.creatorAmiri, Amir Mohammad
dc.creatorPeltier, Nicholas
dc.creatorGoldberg, Cody
dc.creatorSun, Yan
dc.creatorNathan, Anoo
dc.creatorHiremath, Shivayogi V
dc.creatorMankodiya, Kunal
dc.date.accessioned2021-01-25T14:46:54Z
dc.date.available2021-01-25T14:46:54Z
dc.date.issued2017-03
dc.identifier.issn2227-9032
dc.identifier.issn2227-9032
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/4926
dc.identifier.otherFS1QG (isidoc)
dc.identifier.other28264474 (pubmed)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/4944
dc.description.abstractAutism is a complex developmental disorder that affects approximately 1 in 68 children (according to the recent survey conducted by the Centers for Disease Control and Prevention-CDC) in the U.S., and has become the fastest growing category of special education. Each student with autism comes with her or his own unique needs and an array of behaviors and habits that can be severe and which interfere with everyday tasks. Autism is associated with intellectual disability, impairments in social skills, and physical health issues such as sleep and abdominal disturbances. We 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. In this work, we present a study that 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. In this feasibility study, we recruited 14 subjects to record the accelerometer data from the smartwatch worn on the wrist. The processing part extracts 34 different features in each dimension of the three-axis accelerometer, resulting in 102 features. Using and comparing various classification techniques revealed that an ensemble of 40 decision trees has the best accuracy of around 94.6%. This accuracy shows the quality of the data collected from the smartwatch and feature extraction methods used in this study. The recognition of these behaviors by using a smartwatch 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.
dc.format.extent11-11
dc.language.isoen
dc.relation.haspartHEALTHCARE
dc.relation.isreferencedbyMDPI AG
dc.rightsCC BY
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectautism
dc.subjectm-health
dc.subjectsmartwatch
dc.subjectASD
dc.subjectactivity recognition
dc.titleWearSense: Detecting Autism Stereotypic Behaviors through Smartwatches
dc.typeArticle
dc.type.genreJournal Article
dc.relation.doi10.3390/healthcare5010011
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.date.updated2021-01-25T14:46:50Z
refterms.dateFOA2021-01-25T14:46:55Z


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