Repetitive Lifting Motion Predictions Considering Muscle Fatigue.

Area of Science:

• Biomechanics
• Ergonomics
• Human Factors Engineering

Background:

• Repetitive lifting tasks pose risks due to muscle fatigue.
• Accurate prediction of task duration is crucial for preventing injuries.
• Existing muscle fatigue models have limitations in dynamic scenarios.

Purpose of the Study:

• To predict optimal motion for repetitive lifting tasks considering muscle fatigue.
• To compare the accuracy of two muscle fatigue models (3CC and 4CCr) in predicting lifting task duration.
• To formulate the lifting problem as an optimization task minimizing dynamic effort and joint acceleration.

Main Methods:

• Utilized a 2D digital human model with 10 degrees-of-freedom (DOFs) using Denavit-Hartenberg (DH) representation.
• Employed joint-based muscle fatigue models: three-compartment controller (3CC) and four-compartment controller with augmented recovery (4CCr).
• Formulated the lifting task as an optimization problem with joint angle profiles (quartic B-splines) and fatigue compartment control points as design variables.

Main Results:

• Simulations generated profiles for joint angles, torques, and joint fatigue, showing distinct periodic patterns.
• Numerical simulations predicted 11 lifting cycles with the 3CC model and 13 cycles with the 4CCr model for a 20kg box.
• Experimental results matched the 4CCr model's prediction of 13 lifting cycles.

Conclusions:

• The 4CCr muscle fatigue model demonstrates superior accuracy over the 3CC model for predicting the duration of repetitive lifting tasks.
• Optimizing motion based on fatigue prediction can enhance safety and efficiency in lifting tasks.
• The study provides a robust framework for analyzing and predicting human performance in dynamic, repetitive tasks.