Evaluation of Autism Spectrum Disorder Based on the Healthcare by Using Artificial Intelligence Strategies

The behaviors of children with autism spectrum disorder (ASD) are often erratic and difficult to predict. Most of the time, they are unable to communicate effectively in their own language. Instead, they communicate using hand gestures and pointing phrases. Because of this, it can be difficult for c...

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Bibliographic Details
Published in:Journal of sensors 2023-03, Vol.2023 (1)
Main Authors: Sundas, Amit, Badotra, Sumit, Rani, Shalli, Gyaang, Raymond
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Autism
Autistic children
Data mining
Deep learning
Heart rate
Internet of Things
Literature reviews
Machine learning
Patients
Quality of life
Robots
Sensors
Smartphones
Taxonomy
Vital signs
Wearable computers
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Summary:The behaviors of children with autism spectrum disorder (ASD) are often erratic and difficult to predict. Most of the time, they are unable to communicate effectively in their own language. Instead, they communicate using hand gestures and pointing phrases. Because of this, it can be difficult for caregivers to grasp their patients’ requirements, although early detection of the condition can make this much simpler. Assistive technology and the Internet of Things (IoT) can alleviate the absence of verbal and nonverbal communication in the community. The IoT-based solutions use machine Learning (ML) and deep learning (DL) algorithms to diagnose and enhance the lives of patients. A thorough review of ASD techniques in the setting of IoT devices is presented in this research. Identifying important trends in IoT-based health care research is the primary objective of this review. There is also a technical taxonomy for organizing the current articles on ASD algorithms and methodologies based on different factors such as AI, SS network, ML, and IoT. On the basis of criteria such as accuracy and sensitivity, the statistical and operational analyses of the examined ASD techniques are presented.
ISSN:1687-725X
1687-7268
DOI:10.1155/2023/5382375