Framework Using Multicriteria Analysis for Evaluating the Risk of Musculoskeletal Disorders

Area of Science:

• Occupational Health
• Biomedical Engineering
• Ergonomics

Background:

• Musculoskeletal disorders (MSDs) are a significant concern for healthcare professionals, particularly during patient-lifting tasks.
• Existing risk assessment methods may not fully capture the dynamic nature of these movements.
• Previous research has laid the groundwork for novel statistical approaches to MSD risk evaluation.

Purpose of the Study:

• To evaluate musculoskeletal disorder (MSD) risk using IMU sensor data from healthcare specialists performing patient lifts.
• To develop and test a novel multicriteria statistical model for predicting long-term MSD probabilities.
• To enable individual risk profiling and identify dynamic parameters for risk reduction.

Main Methods:

• Utilized Inertial Measurement Unit (IMU) sensor data collected during patient-lifting movements.
• Developed a novel multicriteria statistical model integrating experimental and large-scale statistical datasets.
• Estimated MSD probabilities for the neck, shoulder, and elbows over 5, 10, and 15-year periods.

Main Results:

• The model estimated 5, 10, and 15-year MSD probabilities for the neck (0.537 ± 0.156), shoulder (0.449 ± 0.084), and elbows (0.277 ± 0.221).
• Individual risk profiling is enabled, with dynamic parameters shown to reduce long-term risk by up to 70.49%.
• This proof-of-concept study presents a new approach combining motion tracking and statistical analysis for MSD risk assessment.

Conclusions:

• The developed statistical model offers a novel approach to assessing occupational MSD risk using motion tracking data.
• Dynamic parameters can significantly influence and reduce long-term MSD risk for healthcare workers.
• Further research with larger sample sizes and validated criterion weights is necessary to refine and validate the model.