Exploring the pattern of Emotion in children with ASD as an early biomarker through Recurring-Convolution Neural Network (R-CNN)

• URL: http://arxiv.org/abs/2112.14983v1
• Date: Thu, 30 Dec 2021 09:35:05 GMT
• Title: Exploring the pattern of Emotion in children with ASD as an early biomarker through Recurring-Convolution Neural Network (R-CNN)
• Authors: Abirami S P, Kousalya G and Karthick R
• Abstract summary: The paper implements in identifying basic facial expression and exploring their emotions upon a time variant factor. The emotions are analyzed by incorporating the facial expression identified through CNN using 68 landmark points plotted on the frontal face with a prediction network formed by RNN known as RCNN-FER system.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autism Spectrum Disorder (ASD) is found to be a major concern among various occupational therapists. The foremost challenge of this neurodevelopmental disorder lies in the fact of analyzing and exploring various symptoms of the children at their early stage of development. Such early identification could prop up the therapists and clinicians to provide proper assistive support to make the children lead an independent life. Facial expressions and emotions perceived by the children could contribute to such early intervention of autism. In this regard, the paper implements in identifying basic facial expression and exploring their emotions upon a time variant factor. The emotions are analyzed by incorporating the facial expression identified through CNN using 68 landmark points plotted on the frontal face with a prediction network formed by RNN known as RCNN-FER system. The paper adopts R-CNN to take the advantage of increased accuracy and performance with decreased time complexity in predicting emotion as a textual network analysis. The papers proves better accuracy in identifying the emotion in autistic children when compared over simple machine learning models built for such identifications contributing to autistic society.

Related papers

• Hybrid Models for Facial Emotion Recognition in Children [0.0]
  This paper focuses on the use of emotion recognition techniques to assist psychologists in performing children's therapy through remotely robot operated sessions. Embodied Conversational Agents (ECA) as an intermediary tool can help professionals connect with children who face social challenges.
  arXiv  Detail & Related papers  (2023-08-24T04:20:20Z)
• Exploiting the Brain's Network Structure for Automatic Identification of ADHD Subjects [70.37277191524755]
  We show that the brain can be modeled as a functional network, and certain properties of the networks differ in ADHD subjects from control subjects. We train our classifier with 776 subjects and test on 171 subjects provided by The Neuro Bureau for the ADHD-200 challenge.
  arXiv  Detail & Related papers  (2023-06-15T16:22:57Z)
• NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical Development Patterns of Preterm Infants [73.85768093666582]
  We propose an explainable geometric deep network dubbed NeuroExplainer. NeuroExplainer is used to uncover altered infant cortical development patterns associated with preterm birth.
  arXiv  Detail & Related papers  (2023-01-01T12:48:12Z)
• Vision-Based Activity Recognition in Children with Autism-Related Behaviors [15.915410623440874]
  We demonstrate the effect of a region-based computer vision system to help clinicians and parents analyze a child's behavior. The data is pre-processed by detecting the target child in the video to reduce the impact of background noise. Motivated by the effectiveness of temporal convolutional models, we propose both light-weight and conventional models capable of extracting action features from video frames.
  arXiv  Detail & Related papers  (2022-08-08T15:12:27Z)
• Development of an autism screening classification model for toddlers [0.0]
  Autism spectrum disorder ASD is a neurodevelopmental disorder associated with challenges in communication, social interaction, and repetitive behaviors. This work contributes to the early screening of toddlers by helping identify those who have ASD traits and should pursue formal clinical diagnosis.
  arXiv  Detail & Related papers  (2021-09-29T09:07:39Z)
• A Two-stage Multi-modal Affect Analysis Framework for Children with Autism Spectrum Disorder [3.029434408969759]
  We present an open-source two-stage multi-modal approach leveraging acoustic and visual cues to predict three main affect states of children with ASD's affect states in real-world play therapy scenarios. This work presents a novel way to combine human expertise and machine intelligence for ASD affect recognition by proposing a two-stage schema.
  arXiv  Detail & Related papers  (2021-06-17T01:28:53Z)
• Emotion pattern detection on facial videos using functional statistics [62.997667081978825]
  We propose a technique based on Functional ANOVA to extract significant patterns of face muscles movements. We determine if there are time-related differences on expressions among emotional groups by using a functional F-test.
  arXiv  Detail & Related papers  (2021-03-01T08:31:08Z)
• Early Autism Spectrum Disorders Diagnosis Using Eye-Tracking Technology [62.997667081978825]
  Lack of money, absence of qualified specialists, and low level of trust to the correction methods are the main issues that affect the in-time diagnoses of ASD. Our team developed the algorithm that will be able to predict the chances of ASD according to the information from the gaze activity of the child.
  arXiv  Detail & Related papers  (2020-08-21T20:22:55Z)
• A Convolutional Neural Network for gaze preference detection: A potential tool for diagnostics of autism spectrum disorder in children [0.0]
  We propose a convolutional neural network (CNN) algorithm for gaze prediction using images extracted from a one-minute stimulus video. Our model achieved a high accuracy rate and robustness for prediction of gaze direction with independent persons.
  arXiv  Detail & Related papers  (2020-07-28T18:47:21Z)
• Retinopathy of Prematurity Stage Diagnosis Using Object Segmentation and Convolutional Neural Networks [68.96150598294072]
  Retinopathy of Prematurity (ROP) is an eye disorder primarily affecting premature infants with lower weights. It causes proliferation of vessels in the retina and could result in vision loss and, eventually, retinal detachment, leading to blindness. In recent years, there has been a significant effort to automate the diagnosis using deep learning. This paper builds upon the success of previous models and develops a novel architecture, which combines object segmentation and convolutional neural networks (CNN) Our proposed system first trains an object segmentation model to identify the demarcation line at a pixel level and adds the resulting mask as an additional "color" channel in
  arXiv  Detail & Related papers  (2020-04-03T14:07:41Z)
• A Developmental Neuro-Robotics Approach for Boosting the Recognition of Handwritten Digits [91.3755431537592]
  Recent evidence shows that a simulation of the children's embodied strategies can improve the machine intelligence too. This article explores the application of embodied strategies to convolutional neural network models in the context of developmental neuro-robotics.
  arXiv  Detail & Related papers  (2020-03-23T14:55:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.