Výsledky vyhledávání - "Stockham, Nate"

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An Exploration of Active Learning for Affective Digital Phenotyping

Autor: Washington, Peter, Mutlu, Cezmi, Kline, Aaron, Hou, Cathy, Dunlap, Kaitlyn, Kent, Jack, Husic, Arman, Stockham, Nate, Chrisman, Brianna, Paskov, Kelley, Jung, Jae-Yoon, Wall, Dennis P.

Some of the most severe bottlenecks preventing widespread development of machine learning models for human behavior include a dearth of labeled training data and difficulty of acquiring high quality labels. Active learning is a paradigm for using alg

Externí odkaz: http://arxiv.org/abs/2204.01915

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

Autor: Washington, Peter, Mutlu, Cezmi Onur, Kline, Aaron, Paskov, Kelley, Stockham, Nate Tyler, Chrisman, Brianna, Deveau, Nick, Surhabi, Mourya, Haber, Nick, Wall, Dennis P.

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. While CV classifiers for traditional and structure

Externí odkaz: http://arxiv.org/abs/2201.11197

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

Autor: Lakkapragada, Anish, Kline, Aaron, Mutlu, Onur Cezmi, Paskov, Kelley, Chrisman, Brianna, Stockham, Nate, Washington, Peter, Wall, Dennis

A formal autism diagnosis can be an inefficient and lengthy process. Families may wait months or longer before receiving a diagnosis for their child despite evidence that earlier intervention leads to better treatment outcomes. Digital technologies w

Externí odkaz: http://arxiv.org/abs/2108.07917

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Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labels

Autor: Washington, Peter, Mutlu, Onur Cezmi, Leblanc, Emilie, Kline, Aaron, Hou, Cathy, Chrisman, Brianna, Stockham, Nate, Paskov, Kelley, Voss, Catalin, Haber, Nick, Wall, Dennis

Emotion classifiers traditionally predict discrete emotions. However, emotion expressions are often subjective, thus requiring a method to handle subjective labels. We explore the use of crowdsourcing to acquire reliable soft-target labels and evalua

Externí odkaz: http://arxiv.org/abs/2101.03477

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

Autor: Washington, Peter, Kline, Aaron, Mutlu, Onur Cezmi, Leblanc, Emilie, Hou, Cathy, Stockham, Nate, Paskov, Kelley, Chrisman, Brianna, Wall, Dennis P.

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. The limited data that exist in th

Externí odkaz: http://arxiv.org/abs/2101.03478

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

Autor: Washington, Peter, Kalantarian, Haik, Kent, John, Husic, Arman, Kline, Aaron, Leblanc, Emilie, Hou, Cathy, Mutlu, Onur Cezmi, Dunlap, Kaitlyn, Penev, Yordan, Varma, Maya, Stockham, Nate Tyler, Chrisman, Brianna, Paskov, Kelley, Sun, Min Woo, Jung, Jae-Yoon, Voss, Catalin, Haber, Nick, Wall, Dennis Paul

Publikováno v: JMIR pediatrics and parenting 5.2 (2022): e26760

Background: Automated emotion classification could aid those who struggle to recognize emotions, including children with developmental behavioral conditions such as autism. However, most computer vision emotion recognition models are trained on adult

Externí odkaz: http://arxiv.org/abs/2012.08678

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