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Autism Spectrum Disorder

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Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
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Related Experiment Video

Updated: Jul 20, 2025

Measuring Cardiac Autonomic Nervous System ANS Activity in Children

Measuring Cardiac Autonomic Nervous System ANS Activity in Children

Published on: April 29, 2013

In-the-Wild Affect Analysis of Children with ASD Using Heart Rate.

Kamran Ali¹, Sachin Shah^1,2, Charles E Hughes¹

¹Synthetic Reality Lab, Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA.

Sensors (Basel, Switzerland)

|July 29, 2023

Summary

This summary is machine-generated.

This study developed a smartwatch-based framework to recognize emotions in children with autism spectrum disorder (ASD) using heart rate (HR) signals in real-world settings. The system effectively classifies affective states, aiding personalized support for children with ASD.

Keywords:

autism spectrum disorder (ASD)emotion recognition heart rate smart bracelet wearable

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Eye Tracking Young Children with Autism

Eye Tracking Young Children with Autism

Published on: March 27, 2012

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Measuring Cardiac Autonomic Nervous System ANS Activity in Children

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Published on: April 29, 2013

Measuring Cardiac Autonomic Nervous System ANS Activity in Toddlers - Resting and Developmental Challenges

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Published on: February 25, 2016

Eye Tracking Young Children with Autism

Eye Tracking Young Children with Autism

Published on: March 27, 2012

Area of Science:

Affective computing
Developmental psychology
Biomedical engineering

Background:

Recognizing emotions in children with autism spectrum disorder (ASD) is vital for tailored interventions but challenging in uncontrolled environments.
Existing emotion recognition methods often lack real-world applicability due to reliance on lab settings and limited datasets.
A need exists for a framework to assess affective states of children with ASD in naturalistic settings.

Purpose of the Study:

To present a novel framework for recognizing the affective state of children with ASD in in-the-wild settings using heart rate (HR) data.
To develop and evaluate an algorithm for classifying emotions (positive, negative, neutral) from real-time HR signals.
To compare the performance of raw HR signal analysis with feature extraction using discrete wavelet transform (DWT).

Main Methods:

Utilized a smartwatch application to acquire real-time HR data during a robot coding and avatar interaction task.
Developed a semi-automated annotation technique leveraging facial expression recognition for HR data.
Implemented and compared two HR signal analysis methods: raw signal classification and DWT-based feature extraction.

Main Results:

The proposed framework successfully classified affective states in children with ASD using HR signals in an uncontrolled setting.
The HR-based emotion classification algorithm demonstrated performance comparable to state-of-the-art methods.
Both raw HR signal analysis and DWT-based feature extraction showed effectiveness in emotion recognition.

Conclusions:

The developed framework enables real-world affect analysis for children with ASD using HR information.
This approach holds potential for improving the continuous monitoring and understanding of emotional well-being in children with ASD.
The study highlights the feasibility of unobtrusive emotion recognition in naturalistic environments for supporting children with ASD.