Upper limb muscle forces during a simple reach-to-grasp movement: a comparative study.

Area of Science:

• Biomechanics
• Neuroscience
• Human Movement Science

Background:

• Accurate muscle force estimation is vital for understanding reaching movements.
• Developing reliable tools for measuring muscle forces is challenging, particularly for clinical applications.
• Current methods for muscle force estimation require validation for dynamic tasks.

Purpose of the Study:

• To compare muscle force estimations between a Hill-type model and the EMG-to-Force Processing (EFP) method.
• To evaluate the efficacy of the EFP method for estimating muscle forces during reach-to-grasp movements.
• To provide a validated method for muscle force estimation applicable to robotics, rehabilitation, and ergonomics.

Main Methods:

• Ten healthy male volunteers performed simple reach-to-grasp movements.
• Surface electromyography (EMG) was recorded from key upper limb muscles (deltoid, triceps, biceps).
• Kinematic data of the right upper limb were recorded across three reaching distances (20, 30, 40 cm).
• Muscle forces were estimated using both a Hill-type model and the EMG-to-Force Processing (EFP) method.

Main Results:

• High correlations and low Root Mean Square (RMS) errors were observed between the two muscle force estimation methods.
• The EMG-to-Force Processing (EFP) method demonstrated good accuracy in estimating muscle forces during the tested movements.
• Muscle activations were calculated and compared between the methods.

Conclusions:

• The EMG-to-Force Processing (EFP) method is a reliable approach for estimating muscle forces during reach-to-grasp movements.
• This validated method can be integrated into advanced models for robotics, rehabilitation, and ergonomics.
• Accurate muscle force data enhances the development of assistive technologies and therapeutic interventions.