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

Training an Emotion Detection Classifier using Frames from a Mobile Therapeutic Game for Children with Developmental Disorders

no code implementations • 16 Dec 2020 • Peter Washington, Haik Kalantarian, Jack Kent, Arman Husic, Aaron Kline, Emilie Leblanc, Cathy Hou, Cezmi Mutlu, Kaitlyn Dunlap, Yordan Penev, Maya Varma, Nate Stockham, Brianna Chrisman, Kelley Paskov, Min Woo Sun, Jae-Yoon Jung, Catalin Voss, Nick Haber, Dennis P. Wall

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.

Emotion Classification