A two-phase algorithm for recognizing human activities in the context of industry 4.0 and human-driven processes

Abstract

Future industrial systems, a revolution known as Industry 4.0, are envisioned to integrate people into cyber world as prosumers (service providers and consumers). In this context, human-driven processes appear as an essential reality and instruments to create feedback information loops between the social subsystem (people) and the cyber subsystem (technological components) are required. Although many different instruments have been proposed, nowadays pattern recognition techniques are the most promising ones. However, these solutions present some important pending problems. For example, they are dependent on the selected hardware to acquire information from users; or they present a limit on the precision of the recognition process. To address this situation, in this paper it is proposed a two-phase algorithm to integrate people in Industry 4.0 systems and human-driven processes. The algorithm defines complex actions as compositions of simple movements. Complex actions are recognized using Hidden Markov Models, and simple movements are recognized using Dynamic Time Warping. In that way, only movements are dependent on the employed hardware devices to capture information, and the precision of complex action recognition gets greatly increased. A real experimental validation is also carried out to evaluate and compare the performance of the proposed solution.