Recognizing sitting activities of excavator operators using multi-sensor data fusion with machine learning and deep learning algorithms

Abstract

Recognizing excavator operators' sitting activities is crucial for improving their health, safety, and productivity. Moreover, it provides essential information for comprehending operators' behavior patterns and their interaction with construction equipment. However, limited research has been conducted on recognizing excavator operators' sitting activities. This paper presents a method for recognizing excavator operators' sitting activities by leveraging multi-sensor data and employing machine learning and deep learning algorithms. A multi-sensor system integrating interface pressure sensor arrays and inertial measurement units was developed to capture excavator operators' sitting activity information at a real construction site. Results suggest that the gated recurrent unit achieved outstanding performance, with 98.50% accuracy for static sitting postures and 94.25% accuracy for compound sitting actions. Moreover, several multi-sensor combination schemes were proposed to strike a balance between practicability and recognition accuracy. These findings demonstrate the feasibility and potential of the proposed approach for recognizing operators' sitting activities on construction sites.

Publication DOI: https://doi.org/10.1016/j.autcon.2024.105554
Divisions: College of Engineering & Physical Sciences > Smart and Sustainable Manufacturing
College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Civil Engineering
College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Engineering Systems and Supply Chain Management
Funding Information: This work was partially supported by the National Natural Science Foundation of China (No. 72201254), the Project funded by China Postdoctoral Science Foundation (No. 2021M692990), the Fundamental Research Funds for the Central Universities, China Univers
Additional Information: Publisher Copyright: © 2023
Uncontrolled Keywords: Deep learning,Excavator operator,Interface pressure,Machine learning,Multi-sensor fusion,Sitting activity recognition,Control and Systems Engineering,Civil and Structural Engineering,Building and Construction
Publication ISSN: 0926-5805
Last Modified: 06 Dec 2024 08:31
Date Deposited: 14 Aug 2024 15:22
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.sci ... 2905?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2024-09-01
Published Online Date: 2024-06-17
Accepted Date: 2024-06-10
Authors: Li, Jue
Chen, Gaotong
Antwi-Afari, Maxwell Fordjour (ORCID Profile 0000-0002-6812-7839)

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Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 17 June 2025.

License: Creative Commons Attribution Non-commercial No Derivatives


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