arXiv subjects

Sang Won Bae

Publications and source records attributed to Sang Won Bae.

At least 19 recordsLinked to original sources

arXiv2026

Bichromatic Line-Centers for Point Pairs

We study the \emph{bichromatic line-center problem} for $n$ pairs of points in the plane. A feasible solution assigns one point from each pair to the red set $R$ and the other to the blue set $B$. The goal is to minimize $\max\{w^\circ(R),\,w^\circ(B)\}$, where $w^\circ(X)$ denotes the minimum width of a strip enclosing $X$; the midlines of the corresponding optimal strips define the line-centers of $R$ and $B$. We consider several variants induced by orientational constraints on line-centers and provide efficient algorithms for each. For one line-center, which consists of computing a minimum-width strip that contains at least one point from each pair, we give an $O(n^2)$-time algorithm. For two line-centers, we obtain an $O(n)$-time algorithm when both are horizontal, and $Θ(n\log n)$-time algorithms when the two centers are parallel or when both orientations are prescribed. When exactly one orientation is prescribed, we give an $O(n^2)$-time algorithm. Finally, for the unrestricted case, we present an $O(n^3\log n)$-time algorithm.

cs.CG↗

arXiv2026

Abstract Color Voronoi Diagrams and Circular Sequences of Color Permutations

Abstract Voronoi diagrams are defined in terms of a given system of planar bisecting curves satisfying some simple combinatorial properties. They offer a unifying framework for a wide range of concrete Voronoi instances on generalized sites and metrics. In this paper, we formulate higher-order abstract color Voronoi diagrams of a set $S$ of $n$ colored abstract sites, simultaneously considering all concrete instances under their umbrella. We prove that the number of vertices in the order-$k$ abstract color Voronoi diagram is at most $4k(n-k)-2n$, and present an iterative construction algorithm. The bound directly applies to a family of $m$ disjoint simple polygons of total complexity $n$. For simple polygons the bound can further improve to $O(\min\{k(n-k),(m-k)^2n\})$. A critical ingredient of our proof is a combinatorial analysis on circular sequences of color permutations derived from the unbounded edges of these diagrams, which is interesting in its own right.

cs.CG↗

arXiv2026

Freezing of Gait Prediction using Proactive Agent that Learns from Selected Experience and DDQN Algorithm

Freezing of Gait (FOG) is a debilitating motor symptom commonly experienced by individuals with Parkinson's Disease (PD) which often leads to falls and reduced mobility. Timely and accurate prediction of FOG episodes is essential for enabling proactive interventions through assistive technologies. This study presents a reinforcement learning-based framework designed to identify optimal pre-FOG onset points, thereby extending the prediction horizon for anticipatory cueing systems. The model implements a Double Deep Q-Network (DDQN) architecture enhanced with Prioritized Experience Replay (PER) allowing the agent to focus learning on high-impact experiences and refine its policy. Trained over 9000 episodes with a reward shaping strategy that promotes cautious decision-making, the agent demonstrated robust performance in both subject-dependent and subject-independent evaluations. The model achieved a prediction horizon of up to 8.72 seconds prior to FOG onset in subject-independent scenarios and 7.89 seconds in subject-dependent settings. These results highlight the model's potential for integration into wearable assistive devices, offering timely and personalized interventions to mitigate FOG in PD patients.

cs.LG↗

arXiv2026

Chain-of-Thought Reasoning with Large Language Models for Clinical Alzheimer's Disease Assessment and Diagnosis

Alzheimer's disease (AD) has become a prevalent neurodegenerative disease worldwide. Traditional diagnosis still relies heavily on medical imaging and clinical assessment by physicians, which is often time-consuming and resource-intensive in terms of both human expertise and healthcare resources. In recent years, large language models (LLMs) have been increasingly applied to the medical field using electronic health records (EHRs), yet their application in Alzheimer's disease assessment remains limited, particularly given that AD involves complex multifactorial etiologies that are difficult to observe directly through imaging modalities. In this work, we propose leveraging LLMs to perform Chain-of-Thought (CoT) reasoning on patients' clinical EHRs. Unlike direct fine-tuning of LLMs on EHR data for AD classification, our approach utilizes LLM-generated CoT reasoning paths to provide the model with explicit diagnostic rationale for AD assessment, followed by structured CoT-based predictions. This pipeline not only enhances the model's ability to diagnose intrinsically complex factors but also improves the interpretability of the prediction process across different stages of AD progression. Experimental results demonstrate that the proposed CoT-based diagnostic framework significantly enhances stability and diagnostic performance across multiple CDR grading tasks, achieving up to a 15% improvement in F1 score compared to the zero-shot baseline method.

cs.CL↗

arXiv2025

Offline Policy Evaluation of Multi-Turn LLM Health Coaching with Real Users

We study a web-deployed, tool-augmented LLM health coach with real users. In a pilot with seven users (280 rated turns), offline policy evaluation (OPE) over factorized decision heads (Tool/Style) shows that a uniform heavy-tool policy raises average value on logs but harms specific subgroups, most notably low-health-literacy/high-self-efficacy users. A lightweight simulator with hidden archetypes further shows that adding a small early information-gain bonus reliably shortens trait identification and improves goal success and pass@3. Together, these early findings indicate an evaluation-first path to personalization: freeze the generator, learn subgroup-aware decision heads on typed rewards (objective tool outcomes and satisfaction), and always report per-archetype metrics to surface subgroup harms that averages obscure.

cs.AI↗

arXiv2025

Better Late than Never: the Complexity of Arrangements of Polyhedra

Let $\mathcal{A}$ be the subdivision of $\mathbb{R}^d$ induced by $m$ convex polyhedra having $n$ facets in total. We prove that $\mathcal{A}$ has combinatorial complexity $O(m^{\lceil d/2 \rceil} n^{\lfloor d/2 \rfloor})$ and that this bound is tight. The bound is mentioned several times in the literature, but no proof for arbitrary dimension has been published before.

cs.CG↗

arXiv2025

SePA: A Search-enhanced Predictive Agent for Personalized Health Coaching

This paper introduces SePA (Search-enhanced Predictive AI Agent), a novel LLM health coaching system that integrates personalized machine learning and retrieval-augmented generation to deliver adaptive, evidence-based guidance. SePA combines: (1) Individualized models predicting daily stress, soreness, and injury risk from wearable sensor data (28 users, 1260 data points); and (2) A retrieval module that grounds LLM-generated feedback in expert-vetted web content to ensure contextual relevance and reliability. Our predictive models, evaluated with rolling-origin cross-validation and group k-fold cross-validation show that personalized models outperform generalized baselines. In a pilot expert study (n=4), SePA's retrieval-based advice was preferred over a non-retrieval baseline, yielding meaningful practical effect (Cliff's $δ$=0.3, p=0.05). We also quantify latency performance trade-offs between response quality and speed, offering a transparent blueprint for next-generation, trustworthy personal health informatics systems.

cs.HC↗

arXiv2025

Higher-Order Color Voronoi Diagrams and the Colorful Clarkson-Shor Framework

Given a set $S$ of $n$ colored sites, each $s\in S$ associated with a distance-to-site function $δ_s \colon \mathbb{R}^2 \to \mathbb{R}$, we consider two distance-to-color functions for each color: one takes the minimum of $δ_s$ for sites $s\in S$ in that color and the other takes the maximum. These two sets of distance functions induce two families of higher-order Voronoi diagrams for colors in the plane, namely, the minimal and maximal order-$k$ color Voronoi diagrams, which include various well-studied Voronoi diagrams as special cases. In this paper, we derive an exact upper bound $4k(n-k)-2n$ on the total number of vertices in both the minimal and maximal order-$k$ color diagrams for a wide class of distance functions $δ_s$ that satisfy certain conditions, including the case of point sites $S$ under convex distance functions and the $L_p$ metric for any $1\leq p \leq\infty$. For the $L_1$ (or, $L_\infty$) metric, and other convex polygonal metrics, we show that the order-$k$ minimal diagram of point sites has $O(\min\{k(n-k), (n-k)^2\})$ complexity, while its maximal counterpart has $O(\min\{k(n-k), k^2\})$ complexity. To obtain these combinatorial results, we extend the Clarkson--Shor framework to colored objects, and demonstrate its application to several fundamental geometric structures, including higher-order color Voronoi diagrams, colored $j$-facets, and levels in the arrangements of piecewise linear/algebraic curves/surfaces. We also present an iterative approach to compute higher-order color Voronoi diagrams.

cs.CG↗

arXiv2025

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

Predicting performance outcomes has the potential to transform training approaches, inform coaching strategies, and deepen our understanding of the factors that contribute to athletic success. Traditional non-automated data analysis in sports are often difficult to scale. To address this gap, this study analyzes factors influencing athletic performance by leveraging passively collected sensor data from smartwatches and ecological momentary assessments (EMA). The study aims to differentiate between 14 collegiate volleyball players who go on to perform well or poorly, using data collected prior to the beginning of the season. This is achieved through an integrated feature set creation approach. The model, validated using leave-one-subject-out cross-validation, achieved promising predictive performance (F1 score = 0.75). Importantly, by utilizing data collected before the season starts, our approach offers an opportunity for players predicted to perform poorly to improve their projected outcomes through targeted interventions by virtue of daily model predictions. The findings from this study not only demonstrate the potential of machine learning in sports performance prediction but also shed light on key features along with subjective psycho-physiological states that are predictive of, or associated with, athletic success.

cs.HC↗

arXiv2025

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

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. However, many artificial intelligence (AI) models lack transparency and interpretability due to their opaque nature, limiting their trust and adoption in real-world medical applications, such as clinical decision support systems (CDSS). To address this issue, this paper enhances algorithm explainability underlying CDSS by integrating multiple Explainable Artificial Intelligence (XAI) methods and applying causal inference techniques to clarify the model' predictive decisions under various scenarios. By providing deeper interpretability of the XAI outputs using Large Language Models (LLMs), we provide users with more personalized and accessible insights to overcome the challenges posed by AI's "black box" nature. Our system dynamically adjusts feedback based on user queries and emotional states, combining text-based sentiment analysis with real-time facial emotion recognition to ensure responses are empathetic, context-adaptive, and user-centered. This approach bridges the gap between the learning demands of interpretability and the need for intuitive understanding, enabling non-technical users such as clinicians and clinical researchers to interact effectively with AI models.} Ultimately, this approach improves usability, enhances perceived trustworthiness, and increases the impact of CDSS in healthcare applications.

cs.HC↗

arXiv2024

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

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 facial behavior primitives during 15,995 real-world phone interactions involving 25 participants over four weeks. We developed three models for timely intervention: momentary, daily average, and next day average. Notably, our models exhibit AUC scores ranging from 0.58 to 0.64 for Valence and 0.60 to 0.63 for Arousal. These scores are comparable to or better than those from some previous studies. This predictive ability suggests that MoodCam can effectively forecast mood trends, providing valuable insights for timely interventions and resource planning in mental health management. The results are promising as they demonstrate the viability of using real-time and predictive mood analysis to aid in mental health interventions and potentially offer preemptive support during critical periods identified through mood trend shifts.

cs.HC↗

arXiv2024

Constrained Two-Line Center Problems

Given a set P of n points in the plane, the two-line center problem asks to find two lines that minimize the maximum distance from each point in P to its closer one of the two resulting lines. The currently best algorithm for the problem takes $O(n^2\log^2n)$ time by Jaromczyk and Kowaluk in 1995. In this paper, we present faster algorithms for three variants of the two-line center problem in which the orientations of the resulting lines are constrained. Specifically, our algorithms solve the problem in $O(n \log n)$ time when the orientations of both lines are fixed; in $O(n \log^3 n)$ time when the orientation of one line is fixed; and in $O(n^2 α(n) \log n)$ time when the angle between the two lines is fixed, where $α(n)$ denotes the inverse Ackermann function.

cs.CG↗

arXiv2024

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

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 was gathered from 25 participants to develop models capable of predicting daily mood averages. Utilizing the GLOBEM behavior modeling platform, we benchmarked the utility of pupillary response as a predictor for mood. Our proposed model demonstrated a Matthew's Correlation Coefficient (MCC) score of 0.15 for Valence and 0.12 for Arousal, which is on par with or exceeds those achieved by existing behavioral modeling algorithms supported by GLOBEM. This capability to accurately predict mood trends underscores the effectiveness of pupillary response data in providing crucial insights for timely mental health interventions and resource allocation. The outcomes are encouraging, demonstrating the potential of real-time and predictive mood analysis to support mental health interventions.

cs.HC↗

arXiv2024

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

Depression, a prevalent and complex mental health issue affecting millions worldwide, presents significant challenges for detection and monitoring. While facial expressions have shown promise in laboratory settings for identifying depression, their potential in real-world applications remains largely unexplored due to the difficulties in developing efficient mobile systems. In this study, we aim to introduce FacePsy, an open-source mobile sensing system designed to capture affective inferences by analyzing sophisticated features and generating real-time data on facial behavior landmarks, eye movements, and head gestures -- all within the naturalistic context of smartphone usage with 25 participants. Through rigorous development, testing, and optimization, we identified eye-open states, head gestures, smile expressions, and specific Action Units (2, 6, 7, 12, 15, and 17) as significant indicators of depressive episodes (AUROC=81%). Our regression model predicting PHQ-9 scores achieved moderate accuracy, with a Mean Absolute Error of 3.08. Our findings offer valuable insights and implications for enhancing deployable and usable mobile affective sensing systems, ultimately improving mental health monitoring, prediction, and just-in-time adaptive interventions for researchers and developers in healthcare.

cs.HC↗

arXiv2024

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

As we build towards developing interactive systems that can recognize human emotional states and respond to individual needs more intuitively and empathetically in more personalized and context-aware computing time. This is especially important regarding mental health support, with a rising need for immediate, non-intrusive help tailored to each individual. Individual mental health and the complex nature of human emotions call for novel approaches beyond conventional proactive and reactive-based chatbot approaches. In this position paper, we will explore how to create Chatbots that can sense, interpret, and intervene in emotional signals by combining real-time facial expression analysis, physiological signal interpretation, and language models. This is achieved by incorporating facial affect detection into existing practical and ubiquitous passive sensing contexts, thus empowering them with the capabilities to the ubiquity of sensing behavioral primitives to recognize, interpret, and respond to human emotions. In parallel, the system employs cognitive-behavioral therapy tools such as cognitive reframing and mood journals, leveraging the therapeutic intervention potential of Chatbots in mental health contexts. Finally, we propose a project to build a system that enhances the emotional understanding of Chatbots to engage users in chat-based intervention, thereby helping manage their mood.

cs.HC↗

arXiv2024

Exploring Algorithmic Explainability: Generating Explainable AI Insights for Personalized Clinical Decision Support Focused on Cannabis Intoxication in Young Adults

This study explores the possibility of facilitating algorithmic decision-making by combining interpretable artificial intelligence (XAI) techniques with sensor data, with the aim of providing researchers and clinicians with personalized analyses of cannabis intoxication behavior. SHAP analyzes the importance and quantifies the impact of specific factors such as environmental noise or heart rate, enabling clinicians to pinpoint influential behaviors and environmental conditions. SkopeRules simplify the understanding of cannabis use for a specific activity or environmental use. Decision trees provide a clear visualization of how factors interact to influence cannabis consumption. Counterfactual models help identify key changes in behaviors or conditions that may alter cannabis use outcomes, to guide effective individualized intervention strategies. This multidimensional analytical approach not only unveils changes in behavioral and physiological states after cannabis use, such as frequent fluctuations in activity states, nontraditional sleep patterns, and specific use habits at different times and places, but also highlights the significance of individual differences in responses to cannabis use. These insights carry profound implications for clinicians seeking to gain a deeper understanding of the diverse needs of their patients and for tailoring precisely targeted intervention strategies. Furthermore, our findings highlight the pivotal role that XAI technologies could play in enhancing the transparency and interpretability of Clinical Decision Support Systems (CDSS), with a particular focus on substance misuse treatment. This research significantly contributes to ongoing initiatives aimed at advancing clinical practices that aim to prevent and reduce cannabis-related harms to health, positioning XAI as a supportive tool for clinicians and researchers alike.

cs.HC↗

arXiv2024

PupilSense: Detection of Depressive Episodes Through Pupillary Response in the Wild

Early detection of depressive episodes is crucial in managing mental health disorders such as Major Depressive Disorder (MDD) and Bipolar Disorder. However, existing methods often necessitate active participation or are confined to clinical settings. Addressing this gap, we introduce PupilSense, a novel, deep learning-driven mobile system designed to discreetly track pupillary responses as users interact with their smartphones in their daily lives. This study presents a proof-of-concept exploration of PupilSense's capabilities, where we captured real-time pupillary data from users in naturalistic settings. Our findings indicate that PupilSense can effectively and passively monitor indicators of depressive episodes, offering a promising tool for continuous mental health assessment outside laboratory environments. This advancement heralds a significant step in leveraging ubiquitous mobile technology for proactive mental health care, potentially transforming how depressive episodes are detected and managed in everyday contexts.

cs.HC↗

arXiv2024

Maximum-Width Rainbow-Bisecting Empty Annulus

Given a set of $n$ colored points with $k$ colors in the plane, we study the problem of computing a maximum-width rainbow-bisecting empty annulus (of objects specifically axis-parallel square, axis-parallel rectangle and circle) problem. We call a region rainbow if it contains at least one point of each color. The maximum-width rainbow-bisecting empty annulus problem asks to find an annulus $A$ of a particular shape with maximum possible width such that $A$ does not contain any input points and it bisects the input point set into two parts, each of which is a rainbow. We compute a maximum-width rainbow-bisecting empty axis-parallel square, axis-parallel rectangular and circular annulus in $O(n^3)$ time using $O(n)$ space, in $O(k^2n^2\log n)$ time using $O(n\log n)$ space and in $O(n^3)$ time using $O(n^2)$ space respectively.

cs.CG↗