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A worker posture coding scheme to link automatic and manual coding
Abstract 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
Highlights A worker posture coding scheme is proposed based on a worker's Body Parts Relative Position. An automatic worker posture coding model is developed based on the proposed coding scheme. A consistency test is demonstrated between the automatic coding scheme and the manual coding scheme.
A worker posture coding scheme to link automatic and manual coding
Abstract 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
Highlights A worker posture coding scheme is proposed based on a worker's Body Parts Relative Position. An automatic worker posture coding model is developed based on the proposed coding scheme. A consistency test is demonstrated between the automatic coding scheme and the manual coding scheme.
A worker posture coding scheme to link automatic and manual coding
Chen, Hainan (author) / Luo, Xiaowei (author) / Zhu, Zhenhua (author)
2021-02-07
Article (Journal)
Electronic Resource
English
Human body posture , Coding consistency , Manual and automatic body posture coding connection , BPRP , Body part relative position , IMU , Inertial measurement unit , WMSD , Work-related musculoskeletal disorders , CoP , Center of pressure , BAP , Body action and posture , REBA , Rapid entire body assessment , AMAB , Automated measurement and analysis of body motion , GDL , Gesture description language , BVH , Bounding volume hierarchy , 3D GAN , 3D generative adversarial network
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