报告题目：ALAE-TAE-CutMix+: Beyond the State-of-the-Art for Human Activity Recognition Using Wearable Sensors
时间：2023年12月13日 上午10:00-11:30
地点：大学城校区 B8副楼报告厅
报告人：梁浩锋 教授
主持人：蔡毅 教授
Abstract:
Human Activity Recognition (HAR) through wearable sensors greatly improves the quality of human life through its multiple applications in health monitoring, assisted living, and fitness tracking. For HAR, multi-sensor channel information is vital to performance. Current work states that applying an attention neural network to prioritize discriminatory sensor channels helps the model classify activity more precisely. However, getting discriminatory information from multisensory channels is not always trivial. For example, when collecting data from elderly hospitalized patients. In this context, existing HAR methods struggle to classify activities, particularly activities with similar natures. Moreover, HAR deep models predominantly suffer from overfitting due to small datasets, which leads to poor performance. Data augmentation is a viable solution to this problem. However, currently available data augmentation methods to HAR have various drawbacks, including the possibility of being domain-dependent, and resulting in distorted models for test sequences. To address the aforementioned HAR problems, we propose a novel framework that primarily focuses on two aspects. First, enhancing the latent information across each sensor channel and learning to exploit the relation among multiple latent features and the ongoing activity. Consequently, this enriches the discriminatory feature representations of each activity. Second, a new augmentation strategy is introduced to address the shortcomings of existing multi-sensor channel data augmentation to generalize our HAR model. Our model outperforms existing state-of-the-art approaches on the four most commonly used HAR datasets from diverse domains. We extensively demonstrate the effectiveness of the proposed framework through detailed quantitative analysis of experimental results and ablation studies.
Introduction:
梁浩锋教授是计算机科学与工程系名誉教授和前系主任。他的研究兴趣涵盖以人工智能为中心的各个方面，包括多代理系统（强化学习、涌现现象和进化动力学）、博弈论分析、本体论（知识图谱）和大数据分析。梁教授发表了 300 多篇论文，包括 5 部研究专著和 5 部编著。 梁教授曾于 1998 年担任 ACM（香港分会）主席。 他是英国皇家科桃子汉化组移植游戏大全特许Fellow、香港工程师学会Fellow和香港计算机学会正式会员。他是工程委员会注册的特许工程师。梁教授在香港中文大学取得计算机科学理学士和硕士学位，并在伦敦大学帝国桃子汉化组移植游戏大全取得计算机博士学位和帝国桃子汉化组移植游戏大全文凭。
Professor Ho-fung Leung is an Emeritus Professor and a former Chairman of the Department of Computer Science and Engineering. His research interests cover various aspects centring around artificial intelligence, including multiagent systems (reinforcement learning, emergence phenomena, and evolution dynamics), game theoretic analysis, ontologies (knowledge graphs), and big data analytics. Professor Leung has authored more than 300 publications, including 5 research monographs, and 5 edited volumes. Professor Leung was the chairperson of ACM (Hong Kong Chapter) in 1998. He is a Chartered Fellow of the BCS, a Fellow of the HKIE, and a full member the HKCS. He is a Chartered Engineer registered by the Engineering Council. Professor Leung received his BSc and MPhil degrees in Computer Science from The Chinese University of Hong Kong, and his PhD degree from University of London with DIC (Diploma of Imperial College) in Computing from Imperial College London.
