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cs.HC2025

Predicting Volleyball Season Performance Using Pre-Season Wearable Data and Machine Learning

Melik Ozolcer, Tongze Zhang, Sang Won Bae

Predicting performance outcomes has the potential to transform training approaches, inform coaching strategies, and deepen our understanding of the factors that contribute to athle…

cs.HC2025

AXAI-CDSS : An Affective Explainable AI-Driven Clinical Decision Support System for Cannabis Use

Tongze Zhang, Tammy Chung, Anind Dey +1

As cannabis use has increased in recent years, researchers have come to rely on sophisticated machine learning models to predict cannabis use behavior and its impact on health. How…

cs.HC2024

MoodCam: Mood Prediction Through Smartphone-Based Facial Affect Analysis in Real-World Settings

Rahul Islam, Tongze Zhang, Sang Won Bae

MoodCam introduces a novel method for assessing mood by utilizing facial affect analysis through the front-facing camera of smartphones during everyday activities. We collected fac…

cs.HC2024

MoodPupilar: Predicting Mood Through Smartphone Detected Pupillary Responses in Naturalistic Settings

Rahul Islam, Tongze Zhang, Priyanshu Singh Bisen +1

MoodPupilar introduces a novel method for mood evaluation using pupillary response captured by a smartphone's front-facing camera during daily use. Over a four-week period, data wa…

cs.HC2024

FacePsy: An Open-Source Affective Mobile Sensing System -- Analyzing Facial Behavior and Head Gesture for Depression Detection in Naturalistic Settings

Rahul Islam, Sang Won Bae

Depression, a prevalent and complex mental health issue affecting millions worldwide, presents significant challenges for detection and monitoring. While facial expressions have sh…

cs.HC2024

Revolutionizing Mental Health Support: An Innovative Affective Mobile Framework for Dynamic, Proactive, and Context-Adaptive Conversational Agents

Rahul Islam, Sang Won Bae

As we build towards developing interactive systems that can recognize human emotional states and respond to individual needs more intuitively and empathetically in more personalize…