Determining the optimal size for posture categories used in video-based posture assessment methods

Abstract

Currently, there are no standards for the development of posture classification systems used in observation-based ergonomic posture assessment methods. This study was conducted to determine if an optimal posture category size for different body segments and posture views could be established by examining the trade-off between magnitude of error and the number of posture category misclassification errors made. Three groups (trunk flexion/extension and lateral bend; shoulder flexion/extension and adduction/abduction; elbow flexion/extension) of 30 participants each selected postures they perceived to correctly represent the video image shown on a computer screen. For each view, 10 images were presented for five different posture category sizes, three times each. The optimal posture category sizes established were 30° for trunk, shoulder and elbow flexion/extension, 30° for shoulder adduction/abduction and 15° for trunk lateral bend, suggesting that posture category size should be based on the body segment and view of the image being assessed. Across all conditions, the posture category sizes were comparable to those used in published ergonomic tools.

Keywords:

• posture assessment
• posture category
• decision time
• posture misclassification errors
• posture category size
• video

Acknowledgements

This study was funded by the AUTO21 Network Centres of Excellence, whose funding is provided by the Canadian Federal Government. Dr Jack P. Callaghan is supported by a Canada Research Chair in Spine Biomechanics and Injury Prevention. Thanks to Genie Griggs for her help with data collection.