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WearSense: Detecting Autism Stereotypic Behaviors through Smartwatches

Department of Physical Therapy, College of Public Health, Temple University, Philadelphia, PA 19140, USA
Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA
Smart Monitor Co., San Jose, CA 95119, USA
Author to whom correspondence should be addressed.
Academic Editor: Sampath Parthasarathy
Healthcare 2017, 5(1), 11;
Received: 14 December 2016 / Revised: 16 February 2017 / Accepted: 21 February 2017 / Published: 28 February 2017
Autism 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. View Full-Text
Keywords: autism; m-health; smartwatch; ASD; activity recognition autism; m-health; smartwatch; ASD; activity recognition
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MDPI and ACS Style

Amiri, A.M.; Peltier, N.; Goldberg, C.; Sun, Y.; Nathan, A.; Hiremath, S.V.; Mankodiya, K. WearSense: Detecting Autism Stereotypic Behaviors through Smartwatches. Healthcare 2017, 5, 11.

AMA Style

Amiri AM, Peltier N, Goldberg C, Sun Y, Nathan A, Hiremath SV, Mankodiya K. WearSense: Detecting Autism Stereotypic Behaviors through Smartwatches. Healthcare. 2017; 5(1):11.

Chicago/Turabian Style

Amiri, Amir M., Nicholas Peltier, Cody Goldberg, Yan Sun, Anoo Nathan, Shivayogi V. Hiremath, and Kunal Mankodiya. 2017. "WearSense: Detecting Autism Stereotypic Behaviors through Smartwatches" Healthcare 5, no. 1: 11.

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