Bridging Gap Between Human Activity Recognition and Its Applications in Industrial Domains: Case Study of Outlier Detection in Work Processes

Abstract

This study investigates how to handle erroneous results of probabilistic activity recognition models in industrial applications. Sensor-based automated methods to recognize work activities has various practical applications in industrial domains such as detecting bottlenecks in the flow of tasks, detecting outliers in work processes, reviewing work processes, and managing labor. Although errors in the results of activity recognition models that rely on probabilistic machine learning processes are inevitable, to the best of our knowledge, methods for handling the erroneous results in industrial applications have not been thoroughly investigated. As a case study on outlier detection in work processes, we experimentally investigate methods for performing reliable outlier detection based on the outputs of probabilistic activity recognition models by using large-scale data on packaging work processes.