Search Results for author: Nate Stockham

Found 4 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

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

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