In the realm of human activity recognition (HAR), the integration of explainable Artificial Intelligence (XAI) emerges as a critical necessity to elucidate the decision-making processes of complex models, fostering transparency and trust. Traditional explanatory methods like Class Activation Mapping (CAM) and attention mechanisms, although effective in highlighting regions vital for decisions in various contexts, prove inadequate for HAR. This inadequacy stems from the inherently abstract nature of HAR data, rendering these explanations obscure. In contrast, state-of-th-art post-hoc interpretation techniques for time series can explain the model from other perspectives. However, this requires extra effort. It usually takes 10 to 20 seconds to generate an explanation. To overcome these challenges, we proposes a novel, model-agnostic framework that enhances both the interpretability and efficacy of HAR models through the strategic use of competitive data augmentation. This innovative approach does not rely on any particular model architecture, thereby broadening its applicability across various HAR models. By implementing competitive data augmentation, our framework provides intuitive and accessible explanations of model decisions, thereby significantly advancing the interpretability of HAR systems without compromising on performance.
Towards Explainable Goal Recognition Using Weight of Evidence (WoE): A Human-Centered Approach
The University of Melbourne（墨尔本大学）；King Khalid University（国王哈立德大学）；Macquarie University（麦考瑞大学）；The University of Queensland（昆士兰大学）；Monash University（莫纳什大学）
XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；University of Münster（明斯特大学）；Vienna University of Economics and Business (WU)（维也纳经济大学）
MLlm-DR: Towards Explainable Depression Recognition with MultiModal Large Language Models
National University of Defense Technology（国防科技大学）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine（上海精神卫生中心，上海交通大学医学院）；Nanjing Industria Tenebris Information Technology Co., Ltd.（南京Industria Tenebris信息技术有限公司）；National University of Uzbekistan named after Mirzo Ulugbek（乌兹别克斯坦Mirzo Ulugbek命名的国立大学）
University of Engineering and Technology, Peshawar（白沙瓦工程技术大学）；College of Science and Engineering (CSE), Hamad Bin Khalifa University（哈马德·本·哈利法大学科学与工程学院）；School of Electrical Engineering and Computer Sciences, NUST Islamabad（伊斯兰堡国立科技大学电气与计算机科学学院）
Complying with the EU AI Act: Innovations in Explainable and User-Centric Hand Gesture Recognition
Technical University Munich（慕尼黑工业大学）；Infineon Technologies AG（英飞凌科技股份公司）；Johannes Kepler University Linz（约翰内斯·开普勒大学林茨分校）；Software Competence Center Hagenberg GmbH (SCCH)（哈根堡软件能力中心有限公司（SCCH））
Explainable AI in Speaker Recognition -- Attention Map Visualisation and Evaluation
Centre for Vision, Speech and Signal Processing, University of Surrey（Surrey 大学视觉、语音和信号处理中心）；Department of Informatics, King’s College London（伦敦国王学院信息学院）
XEmoGPT: An Explainable Multimodal Emotion Recognition Framework with Cue-Level Perception and Reasoning
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；th Research Institute, China Electronics Technology Group Corporation（中国电子科技集团第五十四研究所）
An Explainable AI based approach for Monitoring Animal Health
Water Technology Development Hub(AWaDH), Indian Institute of Technology（印度理工学院水技术发展中心）；Academy of Scientific and Innovative Research (AcSIR)（科学与创新研究院）；Department of Higher Education（高等教育部）
Group Activity Recognition in Computer Vision: A Comprehensive Review, Challenges, and Future Perspectives
School of Computer Science, Universiti Sains Malaysia（马来西亚理科大学计算机科学学院）；School of Engineering, College of Technology and Business, Guangzhou（广州工商学院工程学院）
Empirical Characterization of Rationale Stability Under Controlled Perturbations for Explainable Pattern Recognition
The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；Department of Computer Science, The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校计算机科学系）；Department of Mechanical, Aerospace, and Industrial Engineering, The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校机械、航空航天与工业工程系）
