Automated task-level activity analysis through fusion of real time location sensors and worker's tho

Abstract Knowledge of workforce productivity and activity is crucial for determining whether a construction project can be accomplished on time and within budget. Significant work has been done on improving and assessing productivity and activity at task, project, or industry levels. Task level productivity and activity analysis are used extensively within the construction industry for various purposes, including cost estimating, claim evaluation, and day-to-day project management. The assessment is mostly performed through visual observations and after-the-fact analyses even though previous studies show automatic translation of operations data into productivity information and provide spatial information of resources for specific construction operations. An original approach is presented that automatically assesses labor activity. Using data fusion of spatio-temporal and workers' thoracic posture data, a framework was developed for identifying and understanding the worker's activity type over time. This information is used to perform automatic work sampling that is expected to facilitate real-time productivity assessment.

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