An Exploration of Active Learning for Affective Digital Phenotyping

no code implementations • 5 Apr 2022 • Peter Washington, Cezmi Mutlu, Aaron Kline, Cathy Hou, Kaitlyn Dunlap, Jack Kent, Arman Husic, Nate Stockham, Brianna Chrisman, Kelley Paskov, Jae-Yoon Jung, Dennis P. Wall

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.

Active Learning

Classifying Autism from Crowdsourced Semi-Structured Speech Recordings: A Machine Learning Approach

no code implementations • 4 Jan 2022 • Nathan A. Chi, Peter Washington, Aaron Kline, Arman Husic, Cathy Hou, Chloe He, Kaitlyn Dunlap, Dennis Wall

We train our classifiers on our novel dataset of cellphone-recorded child speech audio curated from Stanford's Guess What?

BIG-bench Machine Learning

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

no code implementations • 16 Dec 2020 • 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

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.

Emotion Classification Emotion Recognition