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Found 20 papers, 1 papers with code
Evaluating Fair Feature Selection in Machine Learning for Healthcare
Our approach addresses both distributive and procedural fairness within the fair machine learning context.
TikTokActions: A TikTok-Derived Video Dataset for Human Action Recognition
We find that the performance of the model pre-trained using our Tik-Tok dataset is comparable to models trained on larger action recognition datasets (95. 3% on UCF101 and 53. 24% on HMDB51).
Personalization of Affective Models to Enable Neuropsychiatric Digital Precision Health Interventions: A Feasibility Study
We then compared the personalized models against a generalized model trained using data from all 10 participants.
A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study
Objective: We aim to study the differences between personalized and generalized machine learning models for three-class emotion classification (neutral, stress, and amusement) using wearable biosignal data.
Addressing Racial Bias in Facial Emotion Recognition
Fairness in deep learning models trained with high-dimensional inputs and subjective labels remains a complex and understudied area.
Personalization of Stress Mobile Sensing using Self-Supervised Learning
This personalized learning method can enable precision health systems which are tailored to each subject and require few annotations by the end user, thus allowing for the mobile sensing of increasingly complex, heterogeneous, and subjective outcomes such as stress.
Self-Supervised Learning for Audio-Based Emotion Recognition
While the field of affective computing using audio data is rich, a major barrier to achieve consistently high-performance models is the paucity of available training labels.
Personalized Prediction of Recurrent Stress Events Using Self-Supervised Learning on Multimodal Time-Series Data
We test our model on the Wearable Stress and Affect Detection (WESAD) dataset, demonstrating that our SSL models outperform non-SSL models while utilizing less than 5% of the annotations.
Computer Vision Estimation of Emotion Reaction Intensity in the Wild
Emotions play an essential role in human communication.
A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism
We review the literature of digital health methods for autism behavior quantification using data science.
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.
MLPro: A System for Hosting Crowdsourced Machine Learning Challenges for Open-Ended Research Problems
The task of developing a machine learning (ML) model for a particular problem is inherently open-ended, and there is an unbounded set of possible solutions.
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.
Classifying Autism from Crowdsourced Semi-Structured Speech Recordings: A Machine Learning Approach
We train our classifiers on our novel dataset of cellphone-recorded child speech audio curated from Stanford's Guess What?
Training and Profiling a Pediatric Emotion Recognition Classifier on Mobile Devices
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, even when fully optimized.
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.
A Wearable Social Interaction Aid for Children with Autism
With most recent estimates giving an incidence rate of 1 in 68 children in the United States, the autism spectrum disorder (ASD) is a growing public health crisis.
