WearSense: Detecting Autism Stereotypic Behaviors through Smartwatches

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 h...

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Main Authors: Amir Mohammad Amiri, Nicholas Peltier, Cody Goldberg, Yan Sun, Anoo Nathan, Shivayogi V. Hiremath, Kunal Mankodiya
Format: Article
Language:English
Published: MDPI AG 2017-02-01
Series:Healthcare
Subjects:
ASD
Online Access:http://www.mdpi.com/2227-9032/5/1/11
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spelling doaj-18c65c4208214d78adb0dae4878035a12020-11-24T22:12:57ZengMDPI AGHealthcare2227-90322017-02-01511110.3390/healthcare5010011healthcare5010011WearSense: Detecting Autism Stereotypic Behaviors through SmartwatchesAmir Mohammad Amiri0Nicholas Peltier1Cody Goldberg2Yan Sun3Anoo Nathan4Shivayogi V. Hiremath5Kunal Mankodiya6Department of Physical Therapy, College of Public Health, Temple University, Philadelphia, PA 19140, USADepartment of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USADepartment of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USADepartment of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USASmart Monitor Co., San Jose, CA 95119, USADepartment of Physical Therapy, College of Public Health, Temple University, Philadelphia, PA 19140, USADepartment of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USAAutism 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.http://www.mdpi.com/2227-9032/5/1/11autismm-healthsmartwatchASDactivity recognition
collection DOAJ
language English
format Article
sources DOAJ
author Amir Mohammad Amiri
Nicholas Peltier
Cody Goldberg
Yan Sun
Anoo Nathan
Shivayogi V. Hiremath
Kunal Mankodiya
spellingShingle Amir Mohammad Amiri
Nicholas Peltier
Cody Goldberg
Yan Sun
Anoo Nathan
Shivayogi V. Hiremath
Kunal Mankodiya
WearSense: Detecting Autism Stereotypic Behaviors through Smartwatches
Healthcare
autism
m-health
smartwatch
ASD
activity recognition
author_facet Amir Mohammad Amiri
Nicholas Peltier
Cody Goldberg
Yan Sun
Anoo Nathan
Shivayogi V. Hiremath
Kunal Mankodiya
author_sort Amir Mohammad Amiri
title WearSense: Detecting Autism Stereotypic Behaviors through Smartwatches
title_short WearSense: Detecting Autism Stereotypic Behaviors through Smartwatches
title_full WearSense: Detecting Autism Stereotypic Behaviors through Smartwatches
title_fullStr WearSense: Detecting Autism Stereotypic Behaviors through Smartwatches
title_full_unstemmed WearSense: Detecting Autism Stereotypic Behaviors through Smartwatches
title_sort wearsense: detecting autism stereotypic behaviors through smartwatches
publisher MDPI AG
series Healthcare
issn 2227-9032
publishDate 2017-02-01
description 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.
topic autism
m-health
smartwatch
ASD
activity recognition
url http://www.mdpi.com/2227-9032/5/1/11
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