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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Many workers who engage in manual material handling (MMH) jobs experience high physical demands that are associated with work-related musculoskeletal disorders (WMSDs). Quantifying the physical demands of a job is important for identifying high risk jobs and is a legal requirement in the United States for hiring and return to work following injury. Currently, most physical demand analyses (PDAs) are performed by experts using observational and semi-quantitative methods. The lack of accuracy and reliability of these methods can be problematic, particularly when identifying restrictions during the return-to-work process. Further, when a worker does return-to-work on modified duty, there is no way to track compliance to work restrictions conflating the effectiveness of the work restrictions versus adherence to them. To address this, we applied a deep learning model to data from eight inertial measurement units (IMUs) to predict 15 occupational physical activities. Overall, a 95% accuracy was reached for predicting isolated occupational physical activities. However, when applied to more complex tasks that combined occupational physical activities (OPAs), accuracy varied widely (0–95%). More work is needed to accurately predict OPAs when combined into simulated work tasks.

Details

Title
Applying Wearable Technology and a Deep Learning Model to Predict Occupational Physical Activities
Author
Yishu Yan 1 ; Fan, Hao 2 ; Li, Yibin 3 ; Hoeglinger, Elias 4 ; Wiesinger, Alexander 4 ; Barr, Alan 5 ; Grace D O’Connell 6   VIAFID ORCID Logo  ; Harris-Adamson, Carisa 5 

 Department of Mechanical Engineering, University of California, Berkeley, CA 94720, USA; [email protected] (Y.Y.); [email protected] (G.D.O.) 
 Key Laboratory of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi’an 710072, China; [email protected] 
 Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720, USA; [email protected] 
 Department of Medical Engineering, University of Applied Sciences Upper Austria, 4020 Linz, Austria; [email protected] (E.H.); [email protected] (A.W.) 
 School of Public Health, University of California, Berkeley, CA 94704, USA; [email protected]; Department of Medicine, University of California, San Francisco, CA 94143, USA 
 Department of Mechanical Engineering, University of California, Berkeley, CA 94720, USA; [email protected] (Y.Y.); [email protected] (G.D.O.); Department of Orthopaedic Surgery, University of California, San Francisco, CA 94143, USA 
First page
9636
Publication year
2021
Publication date
2021
Publisher
MDPI AG
e-ISSN
20763417
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2584317124
Copyright
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.