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Automation of Workplace Lifting Hazard Assessment for Musculoskeletal Injury Prevention



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  • Personal Author:
  • Description:
    Objectives: Existing methods for practically evaluating musculoskeletal exposures such as posture and repetition in workplace settings have limitations. We aimed to automate the estimation of parameters in the revised United States National Institute for Occupational Safety and Health (NIOSH) lifting equation, a standard manual observational tool used to evaluate back injury risk related to lifting in workplace settings, using depth camera (Microsoft Kinect) and skeleton algorithm technology. Methods: A large dataset (approximately 22,000 frames, derived from six subjects) of simultaneous lifting and other motions recorded in a laboratory setting using the Kinect (Microsoft Corporation, Redmond, Washington, United States) and a standard optical motion capture system (Qualysis, Qualysis Motion Capture Systems, Qualysis AB, Sweden) was assembled. Error-correction regression models were developed to improve the accuracy of NIOSH lifting equation parameters estimated from the Kinect skeleton. Kinect-Qualysis errors were modelled using gradient boosted regression trees with a Huber loss function. Models were trained on data from all but one subject and tested on the excluded subject. Finally, models were tested on three lifting trials performed by subjects not involved in the generation of the model-building dataset. Results: Error-correction appears to produce estimates for NIOSH lifting equation parameters that are more accurate than those derived from the Microsoft Kinect algorithm alone. Our error-correction models substantially decreased the variance of parameter errors. In general, the Kinect underestimated parameters, and modelling reduced this bias, particularly for more biased estimates. Use of the raw Kinect skeleton model tended to result in falsely high safe recommended weight limits of loads, whereas error-corrected models gave more conservative, protective estimates. Conclusions: Our results suggest that it may be possible to produce reasonable estimates of posture and temporal elements of tasks such as task frequency in an automated fashion, although these findings should be confirmed in a larger study. Further work is needed to incorporate force assessments and address workplace feasibility challenges. We anticipate that this approach could ultimately be used to perform large-scale musculoskeletal exposure assessment not only for research but also to provide real-time feedback to workers and employers during work method improvement activities and employee training. [Description provided by NIOSH]
  • Subjects:
  • Keywords:
  • ISSN:
    2052-4374
  • Document Type:
    Text
  • Funding:
    Grant
  • Genre:
    Journal Article
  • Place as Subject:
  • CIO:
    National Institute for Occupational Safety and Health (NIOSH)
  • Topic:
    Workplace Safety & Health
  • Location:
    North America
  • Pages in Document:
    15
  • Volume:
    26
  • Issue:
    1
  • NIOSHTIC Number:
    nn:20057182
  • Citation:
    Ann Occup Environ Med 2014 Jun; 26(1):15
  • Contact Point Address:
    June T Spector, Department of Environmental & Occupational Health Sciences, University of Washington, 4225 Roosevelt Way NE, Suite 100, Seattle, WA 98105, USA
  • Email:
    spectj@uw.edu
  • Federal Fiscal Year:
    2014
  • Performing Organization:
    University of Washington
  • Peer Reviewed:
    True
  • Start Date:
    20050701
  • Source Full Name:
    Annals of Occupational and Environmental Medicine
  • End Date:
    20250630
  • Collection(s):
  • Main Document Checksum:
    urn:sha-512:56bce56479d2507a472e0d503f74de381f36f685ecd319fbe3834105129ee6012fb9bef3e870f0f6cc1129a54686f69c4cc696c3964f774aedc729ac9ed5ef5d
  • Download URL:
  • File Type:
    Filetype[PDF - 582.41 KB ]
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