IoT and AI-Based Application for Automatic Interpretation of the Affective State of Children Diagnosed with Autism

Popescu, Aura-Loredana; Popescu, Nirvana; Dobre, Ciprian; Apostol, Elena-Simona; Popescu, Decebal

doi:10.3390/s22072528

Cited by 10 publications

(2 citation statements)

References 17 publications

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“…Popescu et al [ 29 ] proposed machine learning-based mobile application called PandaSays, which was improved and integrated with an Alpha 1 Pro robot, and discussed the performance evaluation using deep convolutional neural networks and residual neural networks. The model trained with MobileNet convolutional neural network had an accuracy of 56.25%, performing better than ResNet50 and VGG16.…”

Section: Related Workmentioning

confidence: 99%

An AI-Enabled Internet of Things Based Autism Care System for Improving Cognitive Ability of Children with Autism Spectrum Disorders

Hameed¹,

Hassaballah

Hosney³

et al. 2022

Computational Intelligence and Neuroscience

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Smart monitoring and assisted living systems for cognitive health assessment play a central role in assessment of individuals’ health conditions. Autistic children suffer from some difficulties including social skills, repetitive behaviors, speech and nonverbal communication, and accommodating to the environment around them. Thus, dealing with autistic children is a serious public health problem as it is hard to determine what they feel with a lack of emotional cognitive ability. Currently, no medical treatments have been shown to cure autistic children, with most of the social assistive research to date focusing on Autism Spectrum Disorder (ASD) without suggesting a real treatment. In this paper, we focus on improving cognitive ability and daily living skills and maximizing the ability of the autistic child to function and participate positively in the community. Through utilizing intelligent systems based Artificial Intelligence (AI) and Internet of Things (IoT) technologies, we facilitate the process of adaptation to the world around the autistic children. To this end, we propose an AI-enabled IoT system embodied in a sensor for measuring the heart rate to predict the state of the child and then sending the state to the guardian with feeling and expected behavior of the child via a mobile application. Further, the system can provide a new virtual environment to help the child to be capable of improving eye contact with other people. This way is represented in pictures of these persons in 3D models that break this child’s fear barrier. The system follows strategies that have focused on social communication skill development particularly at young ages to be more interactive with others.

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Section: Related Workmentioning

confidence: 99%

An AI-Enabled Internet of Things Based Autism Care System for Improving Cognitive Ability of Children with Autism Spectrum Disorders

Hameed¹,

Hassaballah

Hosney³

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Also, the ACHI equipment can help calm down of autistic kids. PandaSays, a ML-based smartphone application, was developed and integrated with an Alpha 1 Pro robot by Popescu et al [25]. They also covered performance evaluation using CNN and residual neural networks.…”

Section: Related Workmentioning

confidence: 99%

Upset or Collapse Detection System for ASD Children Using Smart Watch with Machine Learning Algorithm

M.E.L. Subair Ali

2023

IJRITCC

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ASD is characterised by severe and violent behavioural issues that are referred to as "meltdowns (upset) or tantrums (collapse)" and can include aggression, hyperactivity, intolerance, unpredictability and self-injury. This research work intends to develop and implement a non-invasive real-time Upset or Collapse Detection System (UCDS) for people with ASD. With a certain model of smart watch, the non-invasive biological indications such as Pulse Rate (PR), Skin Temperature (ST), and Galvanic Skin Reaction (GSR) can be artificially captured. In order to create the UCDS, deep learning algorithms like CNN, LSTM, and the hybrid of CNN-LSTM are given the physiological signals that are captured to a server. The deep learning algorithm could recognise aberrant upset or collapse states from real-time physiological signs after being trained. Deep learning algorithms including CNN, LSTM, and CNN-LSTM are used to train and test the proposed UCDS system, and it is discovered that hybrid CNN-LSTM beat them all with an average training and testing accuracy of 96% and a low mean absolute error (MAE) of 0.10 for training and 0.04 for testing. Furthermore, the suggested UCDS system is supported by 93% of the ASD caretakers.

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Expression Detection Of Autistic Children Using CNN Algorithm

Syed

Durrani

Shahid

et al. 2023

2023 Global Conference on Wireless and Optical Technologies (GCWOT)

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IoT and AI-Based Application for Automatic Interpretation of the Affective State of Children Diagnosed with Autism

Cited by 10 publications

References 17 publications

An AI-Enabled Internet of Things Based Autism Care System for Improving Cognitive Ability of Children with Autism Spectrum Disorders

An AI-Enabled Internet of Things Based Autism Care System for Improving Cognitive Ability of Children with Autism Spectrum Disorders

Upset or Collapse Detection System for ASD Children Using Smart Watch with Machine Learning Algorithm

Expression Detection Of Autistic Children Using CNN Algorithm

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