Search Results for author:
Found 8 papers, 2 papers with code
Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse Intervention
Participants preferred the adaptive interventions and rated the system highly on intervention time accuracy, effectiveness, and level of trust.
A Comprehensive Dataset and Automated Pipeline for Nailfold Capillary Analysis
In this study, we present a pioneering effort in constructing a comprehensive nailfold capillary dataset-321 images, 219 videos from 68 subjects, with clinic reports and expert annotations-that serves as a crucial resource for training deep-learning models.
MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use Intervention
We first conducted a Wizard-of-Oz study (N=12) and an interview study (N=10) to summarize the mental states behind problematic smartphone use: boredom, stress, and inertia.
EarCough: Enabling Continuous Subject Cough Event Detection on Hearables
Subject cough event detection is the foundation for continuous cough monitoring.
GazeReader: Detecting Unknown Word Using Webcam for English as a Second Language (ESL) Learners
Automatic unknown word detection techniques can enable new applications for assisting English as a Second Language (ESL) learners, thus improving their reading experiences.
Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images
Modeling the trade-off of privacy preservation and machine recognition performance can guide future privacy-preserving computer vision systems using low-resolution image sensors.
MMPD: Multi-Domain Mobile Video Physiology Dataset
Second, most datasets are relatively small and therefore are limited in diversity, both in appearance (e. g., skin tone), behaviors (e. g., motion) and environment (e. g., lighting conditions).
MMTSA: Multimodal Temporal Segment Attention Network for Efficient Human Activity Recognition
The efficiency evaluation on an edge device showed that MMTSA achieved significantly better accuracy, lower computational load, and lower inference latency than SOTA methods.
