Author name cluster

Huiguo Zhang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

AAMAS Conference 2024 Conference Paper

Engaging the Elderly in Exercise with Agents: A Gamified Stationary Bike System for Sarcopenia Management

Yang Qiu
Ping Chen
Huiguo Zhang
Bo Huang
Di Wang
Zhiqi Shen

This paper introduces a portable, gamified exercise system with an embedded agent, specifically designed to aid the elderly in lowerbody workouts using stationary bikes. The system integrates a custom-made Internet of Things (IoT) sensing unit, a gamified application, and an agent-embedded backend platform. By leveraging real-time feedback along with historical user data, the agent actively contributes to exercise safety and adherence by customizing the intensity of workouts and managing break periods. This novel approach aims to make cycling exercise for sarcopenia prevention and intervention more engaging and effective, promoting regular participation and potentially improving health outcomes.

PDF

AAMAS Conference 2017 Conference Paper

Two Forms of Explanations in Computational Assumption-based Argumentation

Xiuyi Fan
Siyuan Liu
Huiguo Zhang
Chunyan Miao
Cyril Leung

Computational Assumption-based Argumentation (CABA) has been introduced to model argumentation with numerical data processing. To realize the “explanation power” of CABA, we study two forms of argumentative explanations, argument explanations and CU explanations representing diagnosis and repair, resp.

PDF

ECAI Conference 2016 Conference Paper

Explained Activity Recognition with Computational Assumption-Based Argumentation

Xiuyi Fan
Siyuan Liu 0003
Huiguo Zhang
Cyril Leung
Chunyan Miao

Activity recognition is a key problem in multi-sensor systems. In this work, we introduce Computational Assumption-based Argumentation, an argumentation approach that seamlessly combines sensor data processing with high-level inference. Our method gives classification results comparable to machine learning based approaches with reduced training time while also giving explanations.

Details