SMART：面向精神障碍群体的场景-运动感知人体动作识别框架
SMART: Scene-motion-aware human action recognition framework for mental disorder group
AI总结：
本文针对精神障碍患者异常动作识别提出SMART框架，通过场景感知模块和多阶段融合模块融合骨架运动、运动轨迹及人-场景交互特征，并在自建MentalHAD数据集上取得优于现有方法的准确率与泛化性能。
摘要：
精神障碍患者经常表现出危险的异常动作，例如攀爬墙壁或击打窗户，随着物联网（IoT）技术的兴起，智能医疗迫切需要智能视频行为监控。然而，面向这些动作的基于视觉的人体动作识别（HAR）发展受到缺乏专用算法和数据集的制约。在本文中，我们创新性地提出构建一个包含精神障碍群体中常见异常动作的基于视觉的HAR数据集，并随后引入一种新颖的场景-运动感知动作识别技术框架，命名为SMART，该框架包含两个技术模块。首先，我们提出了一个场景感知模块，用于提取人体运动轨迹和人-场景交互特征，该模块引入额外的场景信息，为上述动作提供补充的语义表示。其次，多阶段融合模块融合骨架运动、运动轨迹和人-场景交互特征，增强骨架运动与上述补充表示之间的语义关联，从而生成兼具人体运动和场景信息的综合表示。我们提出的方法的有效性已在我们自采的HAR数据集（MentalHAD）上得到验证，在未见过的主体和场景中分别达到了94.9%和93.1%的准确率，并分别比最先进方法高出6.5%和13.2%。所展示的主体和场景泛化能力使SMART有可能迁移到医疗环境中面向精神障碍患者的智能医疗系统的实际部署中。代码和数据集将公开发布以供进一步研究：https://github.com/Inowlzy/SMART.git。
英文摘要：
Patients with mental disorders often exhibit risky abnormal actions, such as climbing walls or hitting windows, necessitating intelligent video behavior monitoring for smart healthcare with the rising Internet of Things (IoT) technology. However, the development of vision-based Human Action Recognition (HAR) for these actions is hindered by the lack of specialized algorithms and datasets. In this paper, we innovatively propose to build a vision-based HAR dataset including abnormal actions often occurring in the mental disorder group and then introduce a novel Scene-Motion-aware Action Recognition Technology framework, named SMART, consisting of two technical modules. First, we propose a scene perception module to extract human motion trajectory and human-scene interaction features, which introduces additional scene information for a supplementary semantic representation of the above actions. Second, the multi-stage fusion module fuses the skeleton motion, motion trajectory, and human-scene interaction features, enhancing the semantic association between the skeleton motion and the above supplementary representation, thus generating a comprehensive representation with both human motion and scene information. The effectiveness of our proposed method has been validated on our self-collected HAR dataset (MentalHAD), achieving 94.9% and 93.1% accuracy in un-seen subjects and scenes and outperforming state-of-the-art approaches by 6.5% and 13.2%, respectively. The demonstrated subject- and scene- generalizability makes it possible for SMART's migration to practical deployment in smart healthcare systems for mental disorder patients in medical settings. The code and dataset will be released publicly for further research: https://github.com/Inowlzy/SMART.git.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
