Key result
A heart rate-based emotion recognition framework produced comparable performance to state-of-the-art techniques for classifying affective states in children with ASD in an uncontrolled setting.
Why the study?
Does a heart rate-based algorithm accurately classify emotions in children with ASD in an uncontrolled setting?
Does a heart rate-based algorithm accurately classify emotions in children with ASD in an uncontrolled setting?
Heart rate data from a smartwatch can be used to classify affective states in children with ASD in real-world, uncontrolled settings.
Supports feasibility of HR-based affective monitoring in real-world pediatric ASD; leaves open larger validation before clinical use.
This paper presents a framework to recognize the affective state of children with Autism Spectrum Disorder (ASD) in an in-the-wild setting using Heart Rate (HR) information. Our algorithm classifies a child’s emotion into positive, negative, or neutral states by analyzing the heart rate signal. The HR signal is obtained from a smartwatch in real-time using our smartwatch application. The heart rate data is acquired when the child learns to code a robot while interacting with an avatar that assists the child in communications skills and programming the robot. In this paper, we also present a comparison of using HR data for the classification of emotions with classification based on features extracted from HR signals using Discrete Wavelet Transform (DWT). Our experimental results show that the proposed method produces a comparable performance with the state-of-the-art HR-based emotion recognition techniques, despite the fact that our experiments are performed in an uncontrolled setting as opposed to a lab environment. This work contributes to real-world affect analysis of children with ASD using HR information.
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Ali et al. (2023) studied Autism Spectrum Disorder (ASD). Heart rate-based emotion recognition framework vs. Classification based on features extracted using Discrete Wavelet Transform (DWT) was evaluated on Classification of emotion into positive, negative, or neutral states. A heart rate-based emotion recognition framework produced comparable performance to state-of-the-art techniques for classifying affective states in children with ASD in an uncontrolled setting.
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