Improving dynamic endurance time predictions for shoulder fatigue: A comparative evaluation.

Area of Science:

• Biomechanics
• Ergonomics
• Occupational Health

Background:

• Work-related musculoskeletal disorders (WMSDs) are a significant issue in various industries.
• Existing methods for assessing and mitigating WMSDs include wearable sensors and biomechanical endurance models.
• There is a need for more accurate and individualized approaches to predict physical strain and prevent injuries.

Purpose of the Study:

• To compare the performance of six different biomechanical endurance models.
• To evaluate four methods for quantifying maximum torque (Torque_max), including a novel approach.
• To develop an optimized and individualized endurance prediction method for loaded dynamic movements.

Main Methods:

• Utilized wearable sensors and biomechanical endurance models.
• Compared six endurance models: consumed endurance, new improved consumed endurance, Frey Law (exponential and power), and Avin general and shoulder models.
• Introduced and tested a novel approach for Torque_max quantification.

Main Results:

• The novel Torque_max quantification method, combined with the new improved consumed endurance model, yielded the lowest root mean square errors (RMSE).
• Mean RMSE decreased from 41.08s to 19.11s overall, 26.13s to 12.16s for males, and 51.28s to 24.45s for females.
• Achieved R-squared values of .459 and .314 for 25% and 45% standardized intensity dynamic tasks, respectively (P < .01).

Conclusions:

• The proposed method offers an optimized and individualized approach for predicting endurance in dynamic loaded movements.
• This approach has direct applicability to industrial tasks, potentially reducing upper-limb strains.
• The findings suggest a pathway to significantly decrease the incidence of WMSDs in the workplace.