Hierarchical Human Action Recognition to Measure the Performance of Manual Labor

Abstract

Measuring manual-labor performance has been a key element of work scheduling and resource management in many industries. It is performed using a standard data system called Time and Motion Study (TMS). Many industries still rely on direct human effort to execute the TMS methodology which can be time-consuming, error-prone, and expensive. In this paper, we introduce an automatic replacement of the TMS technique that works at two levels of abstraction: primitive and activity actions. We leverage on recent advancements in deep learning methods and employ an encoder-decoder based classifier to recognize primitives and a continuous-time hidden Markov model to recognize activities. We show that our system yields results competitive with those obtained with several common human action recognition models. We also show how our proposed system can help operational decisions by computing productivity indicators such as worker availability, worker performance, and overall labor effectiveness.