article · Soft Computing
Autism Spectrum Disorder causes neurodevelopmental challenges affecting communication, learning, and social interaction. Because early diagnosis based on brain abnormalities is difficult, analysing facial expressions provides an alternative approach. A real-time emotion identification system was created to recognise six distinct emotional states in children with autism: anger, fear, joy, natural, sadness, and surprise. The architecture functions through three sequential phases consisting of face identification, facial feature extraction, and classification. An autoencoder handles feature extraction and selection alongside deep convolutional neural networks, evaluating pre-trained architectures including ResNet, MobileNet, and Xception. Among these, the Xception model achieved the highest performance across evaluated metrics. The framework incorporates Internet of Things devices and fog computing to minimise latency, handle large data volumes, and ensure rapid, location-aware processing. This approach provides assistive support for families and medical experts.
Early diagnosis and daily support for children with autism can be difficult because brain-based indicators are challenging to detect early. Automated facial emotion recognition provides an objective, non-invasive method to monitor emotional states such as anger or distress. Using real-time assistive technology can help parents and clinicians respond more quickly to a child's needs, improving their day-to-day care and overall quality of life.
The architecture could enable connected assistive healthcare devices and remote monitoring applications for clinicians and caregivers supporting autistic children. By incorporating fog computing and Internet of Things sensors, the system is designed for real-time, low-latency deployment in domestic or clinical settings. It remains at an applied research stage, validated on image datasets using pre-trained neural networks rather than through commercial trials or clinical integration.
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Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that is characterized by abnormalities in the brain, leading to difficulties in social interaction and communication, as well as learning and attention. Early diagnosis of ASD is challenging as it mainly relies on detecting abnormalities in brain function, which may not be evident in the early stages of the disorder. Facial expression analysis has shown promise as an alternative and efficient solution for early diagnosis of ASD, as children with ASD often exhibit distinctive patterns that differentiate them from typically developing children. Assistive technology has emerged as a crucial tool in improving the quality of life for individuals with ASD. In this study, we developed a real-time emotion identification system to detect the emotions of autistic children in case of pain or anger. The emotion recognition system consists of three stages: face identification, facial feature extraction, and feature categorization. The proposed system can detect six facial emotions: anger, fear, joy, natural, sadness, and surprise. To achieve high-performance accuracy in classifying the input image efficiently, we proposed a deep convolutional neural network (DCNN) architecture for facial expression recognition. An autoencoder was used for feature extraction and feature selection, and a pre-trained model (ResNet, MobileNet, and Xception) was applied due to the size of the dataset. The Xception model achieved the highest performance, with an accuracy of 0.9523%, sensitivity of 0.932, specificity of 0.9421, and AUC of 0.9134%. The proposed emotion detection framework leverages fog and IoT technologies to reduce latency for real-time detection with fast response and location awareness. Using fog computing is particularly useful when dealing with big data. Our study demonstrates the potential of using facial expression analysis and deep learning algorithms for real-time emotion recognition in autistic children, providing medical experts and families with a valuable tool for improving the quality of life for individuals with ASD.
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DOI: 10.1007/s00500-023-09477-y
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