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Found 6 papers, 1 papers with code
An Exploration of Active Learning for Affective Digital Phenotyping
Using frames collected from gameplay acquired from a therapeutic smartphone game for children with autism, we run a simulation of active learning using gameplay prompts as metadata to aid in the active learning process.
Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images
Computer Vision (CV) classifiers which distinguish and detect nonverbal social human behavior and mental state can aid digital diagnostics and therapeutics for psychiatry and the behavioral sciences.
Classification of Abnormal Hand Movement for Aiding in Autism Detection: Machine Learning Study
This work aims to demonstrate the feasibility of deep learning technologies for detecting hand flapping from unstructured home videos as a first step towards validating whether models and digital technologies can be leveraged to aid with autism diagnoses.
Activity Recognition with Moving Cameras and Few Training Examples: Applications for Detection of Autism-Related Headbanging
Activity recognition computer vision algorithms can be used to detect the presence of autism-related behaviors, including what are termed "restricted and repetitive behaviors", or stimming, by diagnostic instruments.
Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labels
While the F1-score for a one-hot encoded classifier is much higher (94. 33% vs. 78. 68%) with respect to the ground truth CAFE labels, the output probability vector of the crowd-trained classifier more closely resembles the distribution of human labels (t=3. 2827, p=0. 0014).
Training an Emotion Detection Classifier using Frames from a Mobile Therapeutic Game for Children with Developmental Disorders
The classifier achieved 66. 9% balanced accuracy and 67. 4% F1-score on the entirety of CAFE as well as 79. 1% balanced accuracy and 78. 0% F1-score on CAFE Subset A, a subset containing at least 60% human agreement on emotions labels.
