The Influence of Lifting Horizontal Distance Measurement Error on NIOSH Lifting Equation Assessment Outcomes
Abstract
Low back injuries are becoming increasingly costly due to the compensation costs and lost days of work. Most of these injuries are linked to manual material handling (MMH) activities. Several ergonomic assessment methods are available to assess the risk factors and determine the risk level for a given MMH job. The National Institute for Occupational Safety and Health (NIOSH) Lifting Equation is the most popular and frequently used ergonomic assessment method to assess MMH jobs. The load weight and horizontal distance are the most significant low back pain risk factors in such jobs. Errors in the measurements of load horizontal distance may influence the risk level obtained from the NIOSH Lifting Equation assessment method depending on the weight of the load being handled. Measurements of the horizontal distance variable measured by novice college students were used to examine NIOSH Lifting Equation sensitivity to the horizontal distance measurement errors with respect to the load weight. The results showed that even though errors in the horizontal distance measurements influenced the resulted lifting index values, that did not influence the resulting NIOSH Lifting Equation risk assessment outcomes for almost all lifting conditions.
Keywords: Ergonomic assessment, horizontal distance, NIOSH sensitivity, measurement error
DOI: 10.54941/ahfe1002610
Cite this paper
More from this volume
- Ergonomics Evaluation Methods for Civil Aircraft Cockpit Layout
- Effect of the backpack load on students’ discomfort
- Effect of the shoe heel height on lower-limb muscle activity
- Effects of carbon fiber insole on lower-extremity muscle activation and wearing comfort during treadmill running
- Intervention of arch support: A quantitative study
- Cycling Stability and Symmetry using a Corrective Bib Short
- Smart Detective Gloves (PROSAFE) for Reducing Carpal Tunnel Syndrome Injuries
- Comparing semiautomatic Rapid Upper Limb Assessments (RULA): Azure Kinect versus RGB-based machine vision algorithm
- Prediction of Muscle Fatigue During Dynamic Exercises based on Surface Electromyography Signals Using Gaussian Classifier
- Integrating sEMG into NIOSH protocol: a manual material handling risk assessment in the fruit and vegetable department of a supermarket
- Anxiety level among industrial engineering students in virtual learning
- Promoting Physical Wellbeing in the Workplace: Providing Working Adults with a Tool to Reduce their Sedentary Behavior


AHFE Open Access