Abstract: A labeling method includes determining whether there is an intersection between the first annotated segment and the second annotated segment; if Yes, determining whether the first annotated segment and the second annotated segment have different labels from each other; if Yes, sending the label of the first annotated segment to the second agent unit and the label of the second annotated segment to the first agent unit; if No, determining whether a discrepancy-over-union ratio between the first annotated segment and the second annotated segment is below a first threshold; if Yes, determining the intersection of the first annotated segment and the second annotated segment as a final annotated segment for the piece of time-series data; if No, assigning a review tag to a union segment of the piece of time-series data which has a duration from a start of the first annotated segment to an end of the second annotated segment.

Abstract: Provided is a manpower management system comprising a data communication unit configured to receive motion signals from a wearable unit worn by a specific employee to sense motions of the specific employee; a data processing unit configured to label the motion signals with behavior data to generate a labeled training dataset for the specific employee; a deep learning model for the specific employee configured to be trained through machine learning using the labeled training dataset, a recognition unit configured to recognize behavior of the specific employee in response to an input of a motion signal of the specific employee received by the data communication unit, using the trained deep learning model, and output a behavior data of the specific employee; and a simulation unit configured to evaluate employee's work efficiency using the output behavior data.