Posture Prediction of Individuals Using Agricultural Machinery Under Whole-Body Vibration in a Lab Environment

Brian Fiegel; Yash Kumar Dhabi; Salam Rahmatalla; Geb Thomas; Tyler Guzowski; Elizabeth Ritchie; David Wilder; Nathan B. Fethke

doi:10.3390/vibration9020025

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Posture Prediction of Individuals Using Agricultural Machinery Under Whole-Body Vibration in a Lab Environment

Journal article

Open access

Peer reviewed

Posture Prediction of Individuals Using Agricultural Machinery Under Whole-Body Vibration in a Lab Environment

Brian Fiegel, Yash Kumar Dhabi, Salam Rahmatalla, Geb Thomas, Tyler Guzowski, Elizabeth Ritchie, David Wilder and Nathan B. Fethke

Vibration, Vol.9(2), 25

04/09/2026

DOI: 10.3390/vibration9020025

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Abstract

Low back pain associated with exposure to whole-body vibration (WBV) is common among agricultural workers, and seated posture significantly affects health outcomes from WBV exposure. Current posture assessment methods rely on manual observation or body-worn sensors, which are labor-intensive and impractical for continuous monitoring. We developed a machine learning approach to classify seated posture using force sensors and accelerometers integrated into a vibration sensing seat pad for use in agricultural machinery, avoiding the need for body-worn sensors. Twenty-four participants were exposed to WBV in different upper body postures while seat pad force and acceleration data were recorded. We compared four machine learning architectures: Logistic Regression, Random Forest, Support Vector Machine, and Recurrent Neural Network with Gated Recurrent Unit (GRU). The GRU architecture substantially outperformed baseline models, achieving 89% accuracy (weighted F1 = 0.89) in classifying forward and backward leaning postures. To our knowledge, this study demonstrates the first application of machine learning to classify seated postures from seat pad force measurements during WBV exposure. Temporal modeling with an 18 s window proved essential for accurate classification, enabling non-invasive, continuous posture monitoring.

whole-body vibration

posture classification

recurrent neural networks

gated recurrent units

occupational ergonomics

Details

Title: Subtitle: Posture Prediction of Individuals Using Agricultural Machinery Under Whole-Body Vibration in a Lab Environment
Creators: Brian Fiegel - University of Iowa
Yash Kumar Dhabi - University of Iowa
Salam Rahmatalla - University of Iowa
Geb Thomas - University of Iowa
Tyler Guzowski - University of Iowa
Elizabeth Ritchie - University of Iowa
David Wilder - University of Iowa
Nathan B. Fethke - University of Iowa
Resource Type: Journal article
Publication Details: Vibration, Vol.9(2), 25
DOI: 10.3390/vibration9020025
ISSN: 2571-631X
eISSN: 2571-631X
Publisher: MDPI
Grant note: National Institute for Occupational Safety and Health: U54OH007548
This research was supported by the US Centers for Disease Control and Prevention (CDC)/National Institute for Occupational Safety and Health (NIOSH) through a cooperative agreement (CDC/NIOSH U54OH007548) with the Great Plains Center for Agricultural Health at the University of Iowa. Contents of the manuscript are solely the responsibility of the authors and do not necessarily represent the official views of the CDC or NIOSH.
Language: English
Date published: 04/09/2026
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Civil and Environmental Engineering; Occupational and Environmental Health; Iowa Technology Institute; Orthopedics and Rehabilitation; Industrial and Systems Engineering; Injury Prevention Research Center
Record Identifier: 9985153532802771

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