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

1
Chair of Materials Handling and Warehousing, TU Dortmund University, Joseph-von-Fraunhofer-Str. 2-4, 44227 Dortmund, Germany
2
Pattern Recognition in Embedded Systems Groups, TU Dortmund University, Otto-Hahn-Str. 16, 44227 Dortmund, Germany
*
Author to whom correspondence should be addressed.
Information 2019, 10(8), 245; https://doi.org/10.3390/info10080245
Received: 14 June 2019 / Revised: 12 July 2019 / Accepted: 19 July 2019 / Published: 24 July 2019
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PDF [511 KB, uploaded 24 July 2019]
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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. View Full-Text
Keywords: Human Activity Recognition; production; Logistics; Motion Capturing; Inertial Measurement Unit; accelerometer; deep learning; statistical pattern recognition Human Activity Recognition; production; Logistics; Motion Capturing; Inertial Measurement Unit; accelerometer; deep learning; statistical pattern recognition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Reining, C.; Niemann, F.; Moya Rueda, F.; Fink, G.A.; ten Hompel, M. Human Activity Recognition for Production and Logistics—A Systematic Literature Review. Information 2019, 10, 245.

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