Real-time facial emotion recognition system among children with autism based on deep learning and IoT

Abstract

Diagnosis of autism considers a challenging task for medical experts since the medical diagnosis mainly depends on the abnormalities in the brain functions that may not appear in the early stages of early onset of autism disorder. Facial expression can be an alternative and efficient solution for the early diagnosis of Autism. This is due to Autistic children usually having distinctive patterns which facilitate distinguishing them from normal children. Assistive technology has proven to be one of the most important innovations in helping people with autism improve their quality of life. A real-time emotion identification system for autistic youngsters was developed in this study. Face identification, facial feature extraction, and feature categorization are the three stages of emotion recognition. A total of six facial emotions are detected by the propound system: anger, fear, joy, natural, sadness, and surprise. This section proposes an enhanced deep learning (EDL) technique to classify the emotions using convolutional neural network. The proposed emotion detection framework takes the benefit from using fog and IoT to reduce the latency for real-time detection with fast response and to be a location awareness. From the results, EDL outperforms other techniques as it achieved 99.99% accuracy. EDL used GA to select the optimal hyperparameters for the CNN.

SENSES-ASD: a social-emotional nurturing and skill enhancement system for autism spectrum disorder

“…The advantage of this study is not only applying deep learning models for ASD, but additionally this model was integrated with a user interface, carefully tailored for high-functioning autistic adults, which complements the system’s real-time streaming and media versatility, which are considered a comparable features that were not included in the previous studies applied in Talaat (2023) , Dhuheir et al (2021) , Li & Wang (2018) .…”

“…Most facial recognition deep learning models are generalized and fail to implement the technology as an intervention method for ASD. Some models like the one in Talaat (2023) are implemented in a way opposing to the one proposed in this research, by instead using the model to detect the emotion of those diagnosed with autism, owing to the deficits in self-expression that autistic people exhibit.…”

“… Talaat (2023) suggested an Internet of Things (IoT) smartphone application for real-time emotion recognition system that helps carers of autistic children understand their feelings. A wide spectrum of emotions, such as neutrality, fear, joy, sadness, and surprise, can be detected by the suggested system.…”

Explainable Enhanced Recurrent Neural Network for lie detection using voice stress analysis

“…Future research may involve testing the model's effectiveness in real-world scenarios and exploring its scalability to accommodate larger datasets. In the future, the proposed algorithm can be used with OCNN [36][37][38][39][40][41][42][43] and make use of Resnet [44]. ERNN can be used for stress detection as in [45].…”

Bibliometric Analysis and Visualization of Clinical Trials on Psychological Stress and Oral Health (1967-2024)

“…Cognitive behavioral therapy has been proven beneficial in reducing dental phobia and dental anxiety [ 19 , 20 ]. Technological advancements, including wearable sensors and telehealth platforms, have facilitated personalized interventions and remote monitoring [ 21 - 24 ]. Microbiome research has shed light on the role of oral microbiota in mediating stress-related oral health disparities [ 25 ].…”