activity
20232025
most citedUbiPhysio: Support Daily Functioning, Fitness, and Rehabilitation with Action Understanding and Feedback in Natural Language

1 citations · 2 across the 13 of their papers we have counts for

collaborators
Showing 2024Show all

6 papers · 1 filter

cs.CV2024

PoseAugment: Generative Human Pose Data Augmentation with Physical Plausibility for IMU-based Motion Capture

Zhuojun Li, Chun Yu, Chen Liang +1

The data scarcity problem is a crucial factor that hampers the model performance of IMU-based human motion capture. However, effective data augmentation for IMU-based motion captur…

cs.HC2024★ 1 cited

Say Your Reason: Extract Contextual Rules In Situ for Context-aware Service Recommendation

Yuxuan Li, Jiahui Li, Lihang Pan +2

This paper introduces SayRea, an interactive system that facilitates the extraction of contextual rules for personalized context-aware service recommendations in mobile scenarios.…

cs.HC2024

CasualGaze: Towards Modeling and Recognizing Casual Gaze Behavior for Efficient Gaze-based Object Selection

Yingtian Shi, Yukang Yan, Zisu Li +4

We present CasualGaze, a novel eye-gaze-based target selection technique to support natural and casual eye-gaze input. Unlike existing solutions that require users to keep the eye-…

cs.HC2024

SonarWatch: Field sensing technique for smartwatches based on ultrasound and motion

Yingtian Shi, Chun Yu, Xuyang Lu +3

A smartwatch worn continuously on the wrist has the potential to perceive rich interactive gestures and natural behaviors of the user. Unfortunately, the current interaction functi…

cs.HC2024

Bridging the gap between natural user expression with complex automation programming in smart homes

Yingtian Shi, Xiaoyi Liu, Chun Yu +4

A long-standing challenge in end-user programming (EUP) is to trade off between natural user expression and the complexity of programming tasks. As large language models (LLMs) are…

cs.HC2024

A Human-Computer Collaborative Tool for Training a Single Large Language Model Agent into a Network through Few Examples

Lihang Pan, Yuxuan Li, Chun Yu +1

The capabilities of a single large language model (LLM) agent for solving a complex task are limited. Connecting multiple LLM agents to a network can effectively improve overall pe…