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Updated: May 29, 2026

An Instrumented Pull Test to Characterize Postural Responses
Published on: April 6, 2019
The validity and interrater reliability of video-based posture observation during asymmetric lifting tasks
Xu Xu1, Chien-Chi Chang, Gert S Faber
1Liberty Mutual Research Institute for Safety, 71 Frankland Rd., Hopkinton, MA 01748, USA.
Objective:
The objective was to evaluate the validity and interrater reliability of a video-based posture observation method for the major body segment angles during asymmetric lifting tasks.
Background:
Observational methods have been widely used as an awkward-posture assessment tool for ergonomics studies. Previous research proposed a video-based posture observation method with estimation of major segment angles during lifting tasks. However, it was limited to symmetric lifting tasks. The current study extended this method to asymmetric lifting tasks and investigated the validity and the interrater reliability.
Method:
Various asymmetric lifting tasks were performed in a laboratory while a side-view video camera recorded the lift, and the body segment angles were measured directly by a motion tracking system. For this study, 10 raters estimated seven major segment angles using a customized program that played back the video recording, thus allowing users to enter segment angles. The validity of estimated segment angles was evaluated in relation to measured segment angles. Interrater reliability was assessed among the raters.
Results:
For all the segment angles except trunk lateral bending, the estimated segment angles were strongly correlated with the measured segment angles (r > .8), and the intraclass correlation coefficient was greater than 0.75.
Conclusion:
The proposed observational method was able to provide a robust estimation of major segment angles for asymmetric lifting tasks based on side-view video clips. The estimated segment angles were consistent among raters.
Application:
This method can be used for assessing posture during asymmetric lifting tasks. It also supports developing a video-based rapid joint loading estimation method.
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