Skip To Main Content University of Washington Department of Rehabilitation Medicine
  Department of Rehabilitation Medicine 
  maximizing potential across the lifespan

Title:

Automation of workplace lifting hazard assessment for musculoskeletal injury prevention

Author(s):

Spector JT, Lieblich M, Bao S, McQuade K, Hughes M

Year:

2014

Publication Info:

Annals of Occupational and Environmental Medicine, 26(15):

Abstract:

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.

Link to Article:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4076760/

Featured Research Articles

Chronic Pain

View the latest research articles on Chronic Pain written by faculty from the Department of Rehabilitation Medicine.

Volunteer to Participate in our Research Studies

The Department of Rehabilitation Medicine is looking for volunteers to participate in research studies on Multiple Sclerosis & Pain Management, and Traumatic Brain Injury.

Level A conformance icon, 
          W3C-WAI Web Content Accessibility Guidelines 1.0
Copyright © 2000-2017 University of Washington