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Found 20 papers, 2 papers with code
AI-driven View Guidance System in Intra-cardiac Echocardiography Imaging
Intra-cardiac Echocardiography (ICE) is a crucial imaging modality used in electrophysiology (EP) and structural heart disease (SHD) interventions, providing real-time, high-resolution views from within the heart.
ELMI: Interactive and Intelligent Sign Language Translation of Lyrics for Song Signing
We present ELMI, an accessible song-signing tool that assists in translating lyrics into sign language.
AACessTalk: Fostering Communication between Minimally Verbal Autistic Children and Parents with Contextual Guidance and Card Recommendation
AACessTalk also encouraged parents to explore their own interaction strategies and empowered the children to have more agency in communication.
ExploreSelf: Fostering User-driven Exploration and Reflection on Personal Challenges with Adaptive Guidance by Large Language Models
Our findings demonstrate that participants valued the balance between guided support and freedom to control their reflective journey, leading to deeper engagement and insight.
Goal-conditioned reinforcement learning for ultrasound navigation guidance
Transesophageal echocardiography (TEE) plays a pivotal role in cardiology for diagnostic and interventional procedures.
Understanding the Impact of Long-Term Memory on Self-Disclosure with Large Language Model-Driven Chatbots for Public Health Intervention
Recent large language models (LLMs) offer the potential to support public health monitoring by facilitating health disclosure through open-ended conversations but rarely preserve the knowledge gained about individuals across repeated interactions.
Cardiac ultrasound simulation for autonomous ultrasound navigation
However, the image quality varies with operator skills as acquiring and interpreting ultrasound images requires extensive training due to the imaging artefacts, the range of acquisition parameters and the variability of patient anatomies.
MindfulDiary: Harnessing Large Language Model to Support Psychiatric Patients' Journaling
In the mental health domain, Large Language Models (LLMs) offer promising new opportunities, though their inherent complexity and low controllability have raised questions about their suitability in clinical settings.
EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteria
By simply composing prompts, developers can prototype novel generative applications with Large Language Models (LLMs).
PlanFitting: Tailoring Personalized Exercise Plans with Large Language Models
A personally tailored exercise regimen is crucial to ensuring sufficient physical activities, yet challenging to create as people have complex schedules and considerations and the creation of plans often requires iterations with experts.
ChaCha: Leveraging Large Language Models to Prompt Children to Share Their Emotions about Personal Events
Children typically learn to identify and express emotions through sharing their stories and feelings with others, particularly their family.
Computational Approaches for App-to-App Retrieval and Design Consistency Check
Extracting semantic representations from mobile user interfaces (UI) and using the representations for designers' decision-making processes have shown the potential to be effective computational design support tools.
Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanations
To mitigate the gap, we designed DeepFuse, the first interactive design that realizes the direct feedback loop between a user and CNNs in diagnosing and revising CNN's vulnerability using local explanations.
Revealing User Familiarity Bias in Task-Oriented Dialogue via Interactive Evaluation
Most task-oriented dialogue (TOD) benchmarks assume users that know exactly how to use the system by constraining the user behaviors within the system's capabilities via strict user goals, namely "user familiarity" bias.
Leveraging Large Language Models to Power Chatbots for Collecting User Self-Reported Data
Through an online study (N = 48) where participants conversed with chatbots driven by different designs of prompts, we assessed how prompt designs and conversation topics affected the conversation flows and users' perceptions of chatbots.
Leveraging Pre-Trained Language Models to Streamline Natural Language Interaction for Self-Tracking
Current natural language interaction for self-tracking tools largely depends on bespoke implementation optimized for a specific tracking theme and data format, which is neither generalizable nor scalable to a tremendous design space of self-tracking.
MyMove: Facilitating Older Adults to Collect In-Situ Activity Labels on a Smartwatch with Speech
Current activity tracking technologies are largely trained on younger adults' data, which can lead to solutions that are not well-suited for older adults.
A Wide-area, Low-latency, and Power-efficient 6-DoF Pose Tracking System for Rigid Objects
Overall, our proposed tracking system is capable of tracking a rigid object pose with sub-millimeter accuracy at the mid range of the work space and sub-degree accuracy for all work space under a lab setting.
Towards Automatic Manipulation of Intra-cardiac Echocardiography Catheter
We present a simplified calibration approach for error compensation and verify with complex rotation of the catheter in benchtop and phantom experiments under varying realistic curvature conditions.
Githru: Visual Analytics for Understanding Software Development History Through Git Metadata Analysis
Furthermore, they do not scale for large and complex Git commit graphs, which can play an important role in understanding the overall development history.
Software Engineering Human-Computer Interaction
