Applying wearable technology and a deep learning model to predict occupational physical activities

Yishu Yan, Hao Fan, Yibin Li, simon Hoeglinger, Alexander Wiesinger, Alan Barr, Grace D. O’connell, Carisa Harris-Adamson

    Research output: Contribution to journalArticlepeer-review

    2 Citations (Scopus)

    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. How-ever, 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.

    Original languageEnglish
    Article number9636
    JournalApplied Sciences (Switzerland)
    Volume11
    Issue number20
    DOIs
    Publication statusPublished - 1 Oct 2021

    Keywords

    • Deep learning
    • Musculoskeletal disorders
    • Occupational physical demands
    • Wearable devices

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