Human Activity Recognition for Production and Logistics—A Systematic Literature Review

TLDR

Human Activity Recognition methods are applicable to industrial production and logistics, either directly or by transfer from related domains. This paper presents a systematic literature review of Human Activity Recognition for Production and Logistics. The review examined 1,243 papers, selected 52 that met content criteria, and analyzed them for activities, sensor attachment, datasets, sensor technology, and HAR methods, focusing on marker‑based motion capture and inertial measurement unit applications. The review summarizes state‑of‑the‑art HAR approaches, statistical pattern recognition, and deep learning architectures, and proposes a practitioner‑oriented roadmap for future research.

Abstract

This contribution provides a systematic literature review of Human Activity Recognition for Production and Logistics. An initial list of 1243 publications that complies with predefined Inclusion Criteria was surveyed by three reviewers. Fifty-two publications that comply with the Content Criteria were analysed regarding the observed activities, sensor attachment, utilised datasets, sensor technology and the applied methods of HAR. This review is focused on applications that use marker-based Motion Capturing or Inertial Measurement Units. The analysed methods can be deployed in industrial application of Production and Logistics or transferred from related domains into this field. The findings provide an overview of the specifications of state-of-the-art HAR approaches, statistical pattern recognition and deep architectures and they outline a future road map for further research from a practitioner’s perspective.