Search Results for author: Kelley Paskov

Found 6 papers, 1 papers with code

An Exploration of Active Learning for Affective Digital Phenotyping

no code implementations5 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

Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images

no code implementations26 Jan 2022 Peter Washington, Cezmi Onur Mutlu, Aaron Kline, Kelley Paskov, Nate Tyler Stockham, Brianna Chrisman, Nick Deveau, Mourya Surhabi, Nick Haber, Dennis P. Wall

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.

Active Learning BIG-bench Machine Learning +4

Classification of Abnormal Hand Movement for Aiding in Autism Detection: Machine Learning Study

1 code implementation18 Aug 2021 Anish Lakkapragada, Aaron Kline, Onur Cezmi Mutlu, Kelley Paskov, Brianna Chrisman, Nate Stockham, Peter Washington, Dennis Wall

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.

Action Detection Activity Detection +2

Activity Recognition with Moving Cameras and Few Training Examples: Applications for Detection of Autism-Related Headbanging

no code implementations10 Jan 2021 Peter Washington, Aaron Kline, Onur Cezmi Mutlu, Emilie Leblanc, Cathy Hou, Nate Stockham, Kelley Paskov, Brianna Chrisman, Dennis P. Wall

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.

Action Detection Activity Detection +1

Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labels

no code implementations10 Jan 2021 Peter Washington, Onur Cezmi Mutlu, Emilie Leblanc, Aaron Kline, Cathy Hou, Brianna Chrisman, Nate Stockham, Kelley Paskov, Catalin Voss, Nick Haber, Dennis Wall

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).

BIG-bench Machine Learning

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

no code implementations16 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

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