Real-time and automatic monitoring of worker behaviors and activities have great potential to improve construction job site operation. Traditional behavior monitoring of construction workers relies on human interpretation to determine workers' semantic conditions (e.g., tasks performing, safety status). Although advanced sensing technologies provide more accurate quantitative data on worker behavior, how to effectively link the data to a worker's semantic condition in a form that is understandable for humans remains a challenge. This paper proposed a novel posture coding scheme based on the worker's body part relative position (BPRP) information. The proposed coding scheme compresses the quantitative 3D skeleton data into qualitative posture descriptions but keeps the body part relative space information. Afterward, an indoor motion test is conducted to validate the reliability of the proposed BPBR coding scheme. The test results showed that by employing the BPRP coding scheme, the manual and automatic posture coding could achieve consistent results. Therefore, the manual posture coding results can be transformed into human skeleton figures and then further processed by the quantitative algorithms. Correspondingly, the computer-captured human skeleton data can be easily connected to the manual observation results by interpreting the BPRB codes

实时，自动监控工人的行为和活动具有极大的潜力，可以改善施工现场的操作。传统的建筑工人行为监控依靠人工解释来确定工人的语义条件（例如，执行任务，安全状态）。尽管先进的传感技术提供了有关工人行为的更准确的定量数据，但是如何以人类可以理解的形式有效地将数据链接到工人的语义条件仍然是一个挑战。本文提出了一种基于工人身体部位相对位置（BPRP）信息的姿态编码方案。提出的编码方案将定量3D骨架数据压缩为定性姿势描述，但保留了身体部位的相对空间信息。之后，进行室内运动测试以验证所提出的BPBR编码方案的可靠性。测试结果表明，采用BPRP编码方案，手动和自动姿态编码均可达到一致的结果。因此，可以将人工姿势编码结果转换成人体骨骼图形，然后通过定量算法进行进一步处理。相应地，通过解释BPRB代码，可以轻松将计算机捕获的人体骨骼数据与人工观察结果联系起来 人工姿势编码结果可以转化为人体骨骼图形，然后通过定量算法进行进一步处理。相应地，通过解释BPRB代码，可以轻松将计算机捕获的人体骨骼数据与人工观察结果联系起来 人工姿势编码结果可以转化为人体骨骼图形，然后通过定量算法进行进一步处理。相应地，通过解释BPRB代码，可以轻松将计算机捕获的人体骨骼数据与人工观察结果联系起来