Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study

• URL: http://arxiv.org/abs/2012.08678v2
• Date: Mon, 3 Jun 2024 22:06:25 GMT
• Title: Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study
• Authors: Peter Washington, Haik Kalantarian, John Kent, Arman Husic, Aaron Kline, Emilie Leblanc, Cathy Hou, Onur Cezmi Mutlu, Kaitlyn Dunlap, Yordan Penev, Maya Varma, Nate Tyler Stockham, Brianna Chrisman, Kelley Paskov, Min Woo Sun, Jae-Yoon Jung, Catalin Voss, Nick Haber, Dennis Paul Wall,
• Abstract summary: Automated emotion classification could aid those who struggle to recognize emotions, including children with developmental behavioral conditions such as autism. Most computer vision emotion recognition models are trained on adult emotion and therefore underperform when applied to child faces. We designed a strategy to gamify the collection and labeling of child emotion-enriched images to boost the performance of automatic child emotion recognition models.
• Score: 5.258326585054865
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Background: Automated emotion classification could aid those who struggle to recognize emotions, including children with developmental behavioral conditions such as autism. However, most computer vision emotion recognition models are trained on adult emotion and therefore underperform when applied to child faces. Objective: We designed a strategy to gamify the collection and labeling of child emotion-enriched images to boost the performance of automatic child emotion recognition models to a level closer to what will be needed for digital health care approaches. Methods: We leveraged our prototype therapeutic smartphone game, GuessWhat, which was designed in large part for children with developmental and behavioral conditions, to gamify the secure collection of video data of children expressing a variety of emotions prompted by the game. Independently, we created a secure web interface to gamify the human labeling effort, called HollywoodSquares, tailored for use by any qualified labeler. We gathered and labeled 2155 videos, 39,968 emotion frames, and 106,001 labels on all images. With this drastically expanded pediatric emotion-centric database (>30 times larger than existing public pediatric emotion data sets), we trained a convolutional neural network (CNN) computer vision classifier of happy, sad, surprised, fearful, angry, disgust, and neutral expressions evoked by children. Results: The classifier achieved a 66.9% balanced accuracy and 67.4% F1-score on the entirety of the Child Affective Facial Expression (CAFE) as well as a 79.1% balanced accuracy and 78% F1-score on CAFE Subset A, a subset containing at least 60% human agreement on emotions labels. This performance is at least 10% higher than all previously developed classifiers evaluated against CAFE, the best of which reached a 56% balanced accuracy even when combining "anger" and "disgust" into a single class.

Related papers

• Emotion Classification of Children Expressions [0.0]
  The model developed is achieved by using advanced concepts of models with Squeeze-andExcitation blocks, Convolutional Block Attention modules, and robust data augmentation. The model designed using Batch Normalisation, Dropout, and SE Attention mechanisms for the classification of children's emotions achieved an accuracy rate of 89%.
  arXiv  Detail & Related papers  (2024-11-12T10:47:31Z)
• Fuzzy Approach for Audio-Video Emotion Recognition in Computer Games for Children [0.0]
  We propose a novel framework that integrates a fuzzy approach for the recognition of emotions through the analysis of audio and video data. We use the FER dataset to detect facial emotions in video frames recorded from the screen during the game. For the audio emotion recognition of sounds a kid produces during the game, we use CREMA-D, TESS, RAVDESS, and Savee datasets.
  arXiv  Detail & Related papers  (2023-08-31T21:22:00Z)
• 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)
• EmoSet: A Large-scale Visual Emotion Dataset with Rich Attributes [53.95428298229396]
  We introduce EmoSet, the first large-scale visual emotion dataset annotated with rich attributes. EmoSet comprises 3.3 million images in total, with 118,102 of these images carefully labeled by human annotators. Motivated by psychological studies, in addition to emotion category, each image is also annotated with a set of describable emotion attributes.
  arXiv  Detail & Related papers  (2023-07-16T06:42:46Z)
• MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wild [56.61912265155151]
  We propose MAFW, a large-scale compound affective database with 10,045 video-audio clips in the wild. Each clip is annotated with a compound emotional category and a couple of sentences that describe the subjects' affective behaviors in the clip. For the compound emotion annotation, each clip is categorized into one or more of the 11 widely-used emotions, i.e., anger, disgust, fear, happiness, neutral, sadness, surprise, contempt, anxiety, helplessness, and disappointment.
  arXiv  Detail & Related papers  (2022-08-01T13:34:33Z)
• Adults as Augmentations for Children in Facial Emotion Recognition with Contrastive Learning [1.0323063834827415]
  We study the application of data augmentation-based contrastive learning to overcome data scarcity in facial emotion recognition for children. We investigate different ways by which adult facial expression images can be used alongside those of children.
  arXiv  Detail & Related papers  (2022-02-10T17:43:11Z)
• Training and Profiling a Pediatric Emotion Recognition Classifier on Mobile Devices [1.996835144477268]
  We optimized and profiled various machine learning models designed for inference on edge devices. Our best model, a MobileNet-V2 network pre-trained on ImageNet, achieved 65.11% balanced accuracy and 64.19% F1-score on CAFE. This balanced accuracy is only 1.79% less than the current state of the art for CAFE, which used a model that contains 26.62x more parameters and was unable to run on the Moto G6.
  arXiv  Detail & Related papers  (2021-08-22T01:48:53Z)
• A Circular-Structured Representation for Visual Emotion Distribution Learning [82.89776298753661]
  We propose a well-grounded circular-structured representation to utilize the prior knowledge for visual emotion distribution learning. To be specific, we first construct an Emotion Circle to unify any emotional state within it. On the proposed Emotion Circle, each emotion distribution is represented with an emotion vector, which is defined with three attributes.
  arXiv  Detail & Related papers  (2021-06-23T14:53:27Z)
• Learning Emotional-Blinded Face Representations [77.7653702071127]
  We propose two face representations that are blind to facial expressions associated to emotional responses. This work is motivated by new international regulations for personal data protection.
  arXiv  Detail & Related papers  (2020-09-18T09:24:10Z)
• Emotion Recognition From Gait Analyses: Current Research and Future Directions [48.93172413752614]
  gait conveys information about the walker's emotion. The mapping between various emotions and gait patterns provides a new source for automated emotion recognition. gait is remotely observable, more difficult to imitate, and requires less cooperation from the subject.
  arXiv  Detail & Related papers  (2020-03-13T08:22:33Z)
• Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor [70.2226417364135]
  It is crucial that the machine should be able to recognize the emotional state of the user with high accuracy. Deep neural networks have been used with great success in recognizing emotions. We present a new model for continuous emotion recognition based on facial expression recognition.
  arXiv  Detail & Related papers  (2020-01-31T17:47:16Z)

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.