Accuracy of video-based moment arm measurements and use with occupational low back disorder assessment methods
type: CreativeWork
description: A large portion of occupational low back disorders (OLBDs) are due to industrial lifting activities. Prior research has clearly demonstrated the association between the external loadings and the risk of OLBD. Low back moments generated internally to counteract external loadings are believed to be the most significant risk factor for OLBD. The increase of both load weight and the horizontal distance between the load and the body (moment arm) increase the external loadings. To accurately assess the risk of OLBD, practitioners need to accurately estimate the external moment using an accurate measurement of the moment arm. The first study assessed the accuracy of manual moment arm measurements during various lifting conditions as compared to a motion capturing system measurements. The second study examined the accuracy of a video-based analysis software (VAS) in measuring the moment arm distance as compared to a motion capturing system. The third study examined the effect of different camera vertical heights on the accuracy of the measured moment arms. Manual moment arm measurements resulted in an average absolute error (AAE) of 6.21cm for lift origin moment arms. VAS moment arm measurements obtained from video recordings via 90° webcam showed a lower AAE (4.25 cm) as compared to measurement errors from 30° and 60° webcams (17.29 cm and 6.07 cm, respectively). The lower webcam height provided measurements with a smaller error (AAE = 3.90 cm) as compared to measurements from the upper webcam (AAE = 11.84 cm). Manual moment arm measurements encountered greater errors as compared to VAS measurements using 90° webcam. This error could further increase when assessing lifting jobs in the field, where practitioners may not get the chance to manually measure the moment arm from a close distance. Videos recorded from webcams with a line of sight almost perpendicular to the lifter’s forearm during lifting resulted in more accurate measurements via the VAS approach.
abstract: A large portion of occupational low back disorders (OLBDs) are due to industrial lifting activities. Prior research has clearly demonstrated the association between the external loadings and the risk of OLBD. Low back moments generated internally to counteract external loadings are believed to be the most significant risk factor for OLBD. The increase of both load weight and the horizontal distance between the load and the body (moment arm) increase the external loadings. To accurately assess the risk of OLBD, practitioners need to accurately estimate the external moment using an accurate measurement of the moment arm. The first study assessed the accuracy of manual moment arm measurements during various lifting conditions as compared to a motion capturing system measurements. The second study examined the accuracy of a video-based analysis software (VAS) in measuring the moment arm distance as compared to a motion capturing system. The third study examined the effect of different camera vertical heights on the accuracy of the measured moment arms. Manual moment arm measurements resulted in an average absolute error (AAE) of 6.21cm for lift origin moment arms. VAS moment arm measurements obtained from video recordings via 90° webcam showed a lower AAE (4.25 cm) as compared to measurement errors from 30° and 60° webcams (17.29 cm and 6.07 cm, respectively). The lower webcam height provided measurements with a smaller error (AAE = 3.90 cm) as compared to measurements from the upper webcam (AAE = 11.84 cm). Manual moment arm measurements encountered greater errors as compared to VAS measurements using 90° webcam. This error could further increase when assessing lifting jobs in the field, where practitioners may not get the chance to manually measure the moment arm from a close distance. Videos recorded from webcams with a line of sight almost perpendicular to the lifter’s forearm during lifting resulted in more accurate measurements via the VAS approach.
author: Hafez, Khaled A.
date published: 2020-05
date modified: 2026-07-24T06:06:34Z
provider: Wichita State University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Wichita State University