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Identifying Autism with Head Movement Features by Implementing Machine Learning Algorithms

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Bibliographic Details
Title: Identifying Autism with Head Movement Features by Implementing Machine Learning Algorithms
Language: English
Authors: Zhao, Zhong, Zhu, Zhipeng, Zhang, Xiaobin, Tang, Haiming, Xing, Jiayi, Hu, Xinyao, Lu, Jianping, Qu, Xingda (ORCID 0000-0003-1764-0357)
Source: Journal of Autism and Developmental Disorders. Jul 2022 52(7):3038-3049.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 12
Publication Date: 2022
Document Type: Journal Articles
Reports - Research
Descriptors: Autism, Pervasive Developmental Disorders, Motion, Human Body, Children, Identification, Nonverbal Communication, Artificial Intelligence
DOI: 10.1007/s10803-021-05179-2
ISSN: 0162-3257
Abstract: Our study investigated the feasibility of using head movement features to identify individuals with autism spectrum disorder (ASD). Children with ASD and typical development (TD) were required to answer ten yes--no questions, and they were encouraged to nod/shake head while doing so. The head rotation range (RR) and the amount of rotation per minute (ARPM) in the pitch (head nodding direction), yaw (head shaking direction) and roll (lateral head inclination) directions were computed, and further fed into machine learning classifiers as the input features. The maximum classification accuracy of 92.11% was achieved with the decision tree classifier with two features (i.e., RR_Pitch and ARPM_Yaw). Our study suggests that head movement dynamics contain objective biomarkers that could identify ASD.
Abstractor: As Provided
Entry Date: 2022
Accession Number: EJ1339362
Database: ERIC
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Abstract:Our study investigated the feasibility of using head movement features to identify individuals with autism spectrum disorder (ASD). Children with ASD and typical development (TD) were required to answer ten yes--no questions, and they were encouraged to nod/shake head while doing so. The head rotation range (RR) and the amount of rotation per minute (ARPM) in the pitch (head nodding direction), yaw (head shaking direction) and roll (lateral head inclination) directions were computed, and further fed into machine learning classifiers as the input features. The maximum classification accuracy of 92.11% was achieved with the decision tree classifier with two features (i.e., RR_Pitch and ARPM_Yaw). Our study suggests that head movement dynamics contain objective biomarkers that could identify ASD.
ISSN:0162-3257
DOI:10.1007/s10803-021-05179-2