慢性疼痛（CP）患者在体力活动中表现出的保护性行为是了解其身体和情绪状态的关键。现有的自动保护行为检测（PBD）方法依赖于用户预定义的活动的预分割。然而，在现实生活中，人们随意地进行活动。因此，当这些活动对慢性疼痛患者造成困难时，应持续提供技术支持，并自动适应活动类型和保护行为的发生。因此，为了促进无处不在的CP管理，在连续数据上实现准确的PBD变得至关重要。在本文中，我们提出通过一种新的分层HAR-PBD架构将人类活动识别（HAR）与PBD集成在一起，该架构包括图卷积和长短期记忆（GC-LSTM）网络，并使用类平衡的焦点类别交叉熵（CFCC）损失来缓解类不平衡。通过使用CP患者数据集对该方法进行深入评估，我们发现HAR，GC-LSTM网络和CFCC损失的利用导致PBD性能相对于基线明显增加（宏观F1评分为0.81 vs. 0.66，精确召回曲线下面积（PR-AUC）为0.60 vs. 0.44）。最后，我们讨论了CP管理及其他方面的分层架构的可能用例。我们还讨论了目前的局限性和前进的道路。
英文摘要：
Protective behavior exhibited by people with chronic pain (CP) during physical activities is the key to understanding their physical and emotional states. Existing automatic protective behavior detection (PBD) methods rely on pre-segmentation of activities predefined by users. However, in real life, people perform activities casually. Therefore, where those activities present difficulties for people with chronic pain, technology-enabled support should be delivered continuously and automatically adapted to activity type and occurrence of protective behavior. Hence, to facilitate ubiquitous CP management, it becomes critical to enable accurate PBD over continuous data. In this paper, we propose to integrate human activity recognition (HAR) with PBD via a novel hierarchical HAR-PBD architecture comprising graph-convolution and long short-term memory (GC-LSTM) networks, and alleviate class imbalances using a class-balanced focal categorical-cross-entropy (CFCC) loss. Through in-depth evaluation of the approach using a CP patients' dataset, we show that the leveraging of HAR, GC-LSTM networks, and CFCC loss leads to clear increase in PBD performance against the baseline (macro F1 score of 0.81 vs. 0.66 and precision-recall area-under-the-curve (PR-AUC) of 0.60 vs. 0.44). We conclude by discussing possible use cases of the hierarchical architecture in CP management and beyond. We also discuss current limitations and ways forward.
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Department of Statistical Science, Duke University（杜克大学统计科学系）
Leveraging Taxonomy Similarity for Next Activity Prediction in Patient Treatment
German Reseach Centre for Artificial Intelligence (DFKI), SDS Branch Trier（德国人工智能研究中心（DFKI），特里尔分部）；University of Trier, Business Information Systems II（特里尔大学，商业信息系统二系）
Cross-Domain Molecular Relational Learning: Leveraging Chemical Structure-Activity Analysis
Wuhan University of Technology（武汉理工大学）；Yonsei University（延世大学）；Hubei Key Laboratory of Transportation Internet of Things（湖北省交通运输物联网重点实验室）；State Key Laboratory of Silicate Materials for Architectures（建筑硅酸盐材料国家重点实验室）；City University of Macau（澳门城市大学）；Kyung Hee University（庆熙大学）；Dalian University（大连大学）
Ensemble Distribution Distillation for Self-Supervised Human Activity Recognition
organization= The University of New South Wales , city= Sydney , country= Australia；organization= The University of New South Wales \& CSIRO , city= Sydney , country= Australia
Leveraging Large Language Models to Predict Antibody Biological Activity Against Influenza A Hemagglutinin
IBM Research-Israel（IBM以色列研究院）；Case Western Reserve University（凯斯西储大学）；IBM TJ Watson Research Center（IBM TJ Watson研究中心）；Florida Research and Innovation Center, Cleveland Clinic（克利夫兰诊所佛罗里达研究与创新中心）
Diffusion Policies with Offline and Inverse Reinforcement Learning for Promoting Physical Activity in Older Adults Using Wearable Sensors
Department of Statistics and Data Science, University of Central Florida（统计与数据科学系，中央佛罗里达大学）；College of Nursing, University of Central Florida（护理学院，中央佛罗里达大学）；School of Computing and Augmented Intelligence, Arizona State University（计算与增强智能学院，亚利桑那州立大学）
Gan-Based Joint Activity Detection and Channel Estimation For Grant-free Random Access
ShanghaiTech University（上海科技大学）；Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences（中国科学院上海微系统与信息技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
