随着计算机科学的飞速发展，人体动作识别技术凭借其高效的数据处理能力和精准的动作捕捉特性，在建筑、医疗等多个领域展现出良好的应用前景。在建筑环境中，针对动作的识别与分析能够了解人体的行为模式和行为状态，及时发现潜在隐患，显著降低事故和意外伤害的风险。 然而，当前的研究局限于具体动作的识别，缺乏对行为模式的分析和行为能力的评估。因此，本文提出了一种基于计算机视觉的方法，通过构建行为分析模型，旨在实现建筑环境中室内外重要场景下人体动作的识别与行为能力的评估。具体工作如下：
首先，本研究对 OpenPose 算法进行了改进，提升了其运行速度和适用性，以满足不同场景下人体姿态估计的需求。其次，构建了人体行为分析模型。通过 RGB 相机和 OpenCV 库采集运动数据，利用改进后的 OpenPose 算法提取关键点信息，并基于支持向量机建立了骨架参数和动作之间的映射模型，该模型可用于建筑环境中人体动作的识别与行为能力的分析。随后，本文分别选择了室外和室内环境下的典型场景，即施工场景和居家场景，探索了行为分析模型的应用，以保障建筑的安全施工和运营。 具体地， 针对室外施工场景，通过分析工人的行为，实现了对其站立、行走、攀爬和跌倒等四类基本动作的识别，并进一步分析其在施工活动中的行为模式和行为能力，从而对其疲劳程度进行有效的管理。 针对室内居家场景，建立了老年人行为能力评估体系，并在此基础上，通过识别与分析居家环境下老年人的动作，评估其在日常生活中的行为能力。进一步地， 本文制定了适用于居家场景的个性化训练方案，设计并验证了面向居家老人的行为能力评估系统，以实现对其运动功能的改善。
本文所构建的行为分析模型能够满足建筑施工和居家场景下的使用需求。通过对人体动作的识别以及行为能力的评估，结合相应的解决方案，以提高工人和居住者的安全和健康水平。本研究为解决当前建筑环境中安全事故高发问题提供了新方案。

关键词：建筑环境；人体动作识别；行为能力评估；计算机视觉

With the rapid development of computer science, human action recognition technology, leveraging its efficient data processing capability and precise motion capture characteristics, has shown promising applications in various fields such as construction and healthcare. In building environment, recognition and analysis of actions can provide insights into human behavior patterns and states, promptly identifying potential hazards and significantly reducing the risk of accidents and injuries. However, current research is limited to the recognition of specific actions and lacks analysis of behavior patterns and assessment of behavior capabilities. Therefore, this paper proposes a computer vision-based method that aims to recognize human actions and assess behavior capabilities in critical indoor and outdoor scenarios within building environment. The specific work is as follows:
First, this study improves the OpenPose algorithm to accommodate posture estimation needs in different scenarios. Second, a human behavior analysis model is established. Motion data is collected through RGB cameras and the OpenCV library, and the improved OpenPose algorithm is used to extract key points of the human body. A mapping model between skeleton parameters and actions is built based on the support vector machine, enabling the recognition of human body movements and analysis of behavioral capabilities in construction environments. Subsequently, typical scenarios in outdoor and indoor environments, i.e., construction scenario and home scenario, are selected in this paper to explore the application of the behavioral analysis model to ensure the safe construction and operation of buildings. For the outdoor construction scenario, by analyzing the behaviors of workers, recognition of four basic actions including standing, walking, climbing, and falling is achieved, along with further analysis of their behavior patterns and capabilities in construction activities, effectively managing their fatigue levels. For the indoor home scenario, a behavioral capability assessment system for the elderly is established. By recognizing and analyzing the movements of the elderly in home environments, their behavioral capabilities in daily life are assessed. Furthermore, personalized training programs suitable for home scenarios are developed, and a behavioral capability assessment system for elderly residents is designed and validated to improve their mobility function.
The behavior analysis model constructed in this paper meets the usage requirements under construction and home scenarios. By recognizing human actions and assessing behavior capabilities, combined with corresponding solutions, the safety and health levels of construction workers and residents are improved. This research provides a new solution to the high incidence of safety accidents in current building environments.