Using Digital Human Modelling to Evaluate the Risk of Musculoskeletal Injury for Workers in the Healthcare Industry

Area of Science:

• Occupational Health
• Ergonomics
• Biomechanical Engineering

Background:

• Healthcare workers face high injury rates, leading to absenteeism and costs.
• Existing methods for assessing injury risk are limited.
• A novel technique combines wearable devices and digital human technology for continuous monitoring.

Purpose of the Study:

• To develop and evaluate a new method for assessing injury risk in healthcare workers.
• To identify biomechanical factors contributing to lower back injuries during patient transfers.
• To enable real-time monitoring and adjustment of potentially harmful postures.

Main Methods:

• Integration of JACK Siemens software and Xsens motion tracking system.
• Utilized 33 participants performing simulated patient transfer tasks (bed to sitting, bed to wheelchair).
• Analysis of awkward postures and spinal loads during repetitive tasks.

Main Results:

• Significant gender-based differences in spinal forces were observed at varying operational heights.
• Key anthropometric variables, including trunk and hip motion, were identified as major contributors to lower back injury risk.
• The study successfully identified specific movements associated with increased lumbar spine load.

Conclusions:

• The developed technique can identify high-risk movements and postures.
• Findings support the implementation of targeted training and environmental design improvements.
• Reducing lower back pain can decrease healthcare worker turnover, improve patient satisfaction, and lower costs.