论文标题
利用活动识别以实现连续数据中的保护行为检测
Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous Data
论文作者
论文摘要
体育活动中患有慢性疼痛（CP）的人表现出的保护行为是理解其身体和情感状态的关键。现有的自动保护行为检测（PBD）方法依赖于用户预定的活动的预分段。但是，在现实生活中，人们随便进行活动。因此，如果这些活动给患有慢性疼痛的人带来困难，应连续并自动适应活动类型和保护行为的发生。因此，为了促进普遍存在的CP管理，至关重要的是，在连续数据上启用准确的PBD。在本文中，我们建议通过一种新型的层次HAR-PBD结构将人类活动识别（HAR）与PBD整合在一起，该结构包括图形卷积和长期记忆（GC-LSTM）网络，并使用类似平衡的焦点局灶性分类分类 - 杂交（CFCC）损失。通过使用CP患者的数据集对方法的深入评估，我们表明，HAR，GC-LSTM网络和CFCC损失的利用会导致PBD性能明显提高，而PBD的基线得分（0.81 vs. 0.66 vs. 0.66 vs. 0.66 vs. 0.66，Precision-Recall recall recall recall recall-recall-recall-recall-recall-recall-curve curve curve（PR-AUC）（PR-AUC）为0.60 VS. 0.44）。我们通过讨论CP管理及其他地区的分层体系结构的可能用例来结束。我们还讨论了当前的局限性和前进方式。
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.
