arXiv subjects

Dong Yoon Lee

Publications and source records attributed to Dong Yoon Lee.

6 recordsLinked to original sources

arXiv2026

OriFeel: Origami-Inspired Tactile Feedback via Surface Folding

People passively interact with ambient surfaces such as tables, chair backs, and armrests throughout daily life, making them natural candidates for ubiquitous tactile interfaces. However, transforming these everyday surfaces into practical haptic interfaces remains challenging. Existing solutions typically rely on dense arrays of actuators, resulting in bulky hardware and high power consumption that limit their integration into ambient objects. We present OriFeel, a structure-driven tactile interface that leverages a Miura-ori folding mechanism to distribute actuation across multiple interconnected folding units. Rather than mapping actuators to individual contact points, OriFeel uses embedded cables to exploit structural interconnections, distributing actuation across the surface and enabling spatially controllable tactile output across multiple folding units. We implement prototypes using rigid and soft materials and characterize their ability to distribute actuation across interconnected units. Our results demonstrate the feasibility of structure-driven tactile feedback via coordinated folding of compliant origami surface structures, providing a practical foundation for compact, scalable ambient haptic interfaces.

cs.HC↗

arXiv2026

GEM: Gear-based Environment-Integrated Mobility for Adaptive Indoor Human Sensing

Infrastructure-based sensing systems, like Wi-Fi, thermal, vibration-based approaches, provide continuous and unobtrusive indoor human monitoring services. They are often deployed statically for long-term continuous monitoring, which often leads to inefficient sensing/inflexible deployment due to human mobility or high maintenance/data volume for dense deployments. In contrast, autonomous and human carried mobile devices can better adapt to human mobility. However, their physical presence (e.g., drones or robots) may induce observer effects, while their operation often imposes additional burdens, such as wearing (e.g., wearables) and frequent charging. We present GEM, a hybrid scheme that introduces the mobility to infrastructure-based sensing. GEM integrates a matrix of gears into everyday surfaces (e.g., floors, walls) to turn them into "public transportation" for moving infrastructure sensors around. We design and fabricate a 3 x 3 gear matrix prototype that can effectively move sensors from one location to another. We further validate the scalability of the design through simulation of up to 64 x 64 gear matrix with concurrent sensors.

eess.SY↗

arXiv2026

Home Health System Deployment Experience for Geriatric Care Remote Monitoring

To support aging-in-place, adult children often provide care to their aging parents from a distance. These informal caregivers desire plug-and-play remote care solutions for privacy-preserving continuous monitoring that enabling real-time activity monitoring and intuitive, actionable information. This short paper presents insights from three iterations of deployment experience for remote monitoring system and the iterative improvement in hardware, modeling, and user interface guided by the Geriatric 4Ms framework (matters most, mentation, mobility, and medication). An LLM-assisted solution is developed to balance user experience (privacy-preserving, plug-and-play) and system performance.

cs.HC↗

arXiv2025

A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysis

Foundation models have gained growing interest in the IoT domain due to their reduced reliance on labeled data and strong generalizability across tasks, which address key limitations of traditional machine learning approaches. However, most existing foundation model based methods are developed for specific IoT tasks, making it difficult to compare approaches across IoT domains and limiting guidance for applying them to new tasks. This survey aims to bridge this gap by providing a comprehensive overview of current methodologies and organizing them around four shared performance objectives by different domains: efficiency, context-awareness, safety, and security & privacy. For each objective, we review representative works, summarize commonly-used techniques and evaluation metrics. This objective-centric organization enables meaningful cross-domain comparisons and offers practical insights for selecting and designing foundation model based solutions for new IoT tasks. We conclude with key directions for future research to guide both practitioners and researchers in advancing the use of foundation models in IoT applications.

cs.LG↗

arXiv2025

RARR : Robust Real-World Activity Recognition with Vibration by Scavenging Near-Surface Audio Online

One in four people dementia live alone, leading family members to take on caregiving roles from a distance. Many researchers have developed remote monitoring solutions to lessen caregiving needs; however, limitations remain including privacy preserving solutions, activity recognition, and model generalizability to new users and environments. Structural vibration sensor systems are unobtrusive solutions that have been proven to accurately monitor human information, such as identification and activity recognition, in controlled settings by sensing surface vibrations generated by activities. However, when deploying in an end user's home, current solutions require a substantial amount of labeled data for accurate activity recognition. Our scalable solution adapts synthesized data from near-surface acoustic audio to pretrain a model and allows fine tuning with very limited data in order to create a robust framework for daily routine tracking.

cs.SD↗

arXiv2025

Graph-Based Physics-Guided Urban PM2.5 Air Quality Imputation with Constrained Monitoring Data

This work introduces GraPhy, a graph-based, physics-guided learning framework for high-resolution and accurate air quality modeling in urban areas with limited monitoring data. Fine-grained air quality monitoring information is essential for reducing public exposure to pollutants. However, monitoring networks are often sparse in socioeconomically disadvantaged regions, limiting the accuracy and resolution of air quality modeling. To address this, we propose a physics-guided graph neural network architecture called GraPhy with layers and edge features designed specifically for low-resolution monitoring data. Experiments using data from California's socioeconomically disadvantaged San Joaquin Valley show that GraPhy achieves the overall best performance evaluated by mean squared error (MSE), mean absolute error (MAE), and R-square value (R2), improving the performance by 9%-56% compared to various baseline models. Moreover, GraPhy consistently outperforms baselines across different spatial heterogeneity levels, demonstrating the effectiveness of our model design.

cs.LG↗