A Method for Improving Human Joint Moment Estimation during Lower Limb Rehabilitation Training Based on sEMG Signals

Area of Science:

• Biomechanics
• Rehabilitation Engineering
• Human Movement Science

Background:

• Accurate knee joint moment estimation is crucial for quantitative assessment in lower limb rehabilitation.
• Complex musculoskeletal dynamics present challenges for precise joint moment calculation.
• Existing methods struggle to adapt to dynamic changes in muscle activation states.

Purpose of the Study:

• To develop and validate a novel joint moment estimation method adaptable to varying muscle activation states.
• To improve the accuracy of knee joint moment estimation for enhanced lower limb rehabilitation.
• To leverage surface electromyography (sEMG) for more precise biomechanical analysis.

Main Methods:

• Collected lower limb muscle sEMG signals to analyze muscle synergy features.
• Developed an adaptive muscle activation state model using personalized parameters (e.g., segment lengths).
• Calculated muscle forces and paths, then computed joint muscle moments based on muscle actions.

Main Results:

• The proposed method demonstrated superior performance compared to traditional models in both static and dynamic experiments.
• Achieved root mean square errors (RMSEs) of 2.1 Nm (static) and 1.1 Nm (dynamic).
• Resulted in a 38.2% increase in static and 15.4% increase in dynamic joint moment estimation accuracy.

Conclusions:

• The novel method effectively adapts to muscle activation state changes, significantly improving joint moment estimation accuracy.
• This advancement holds potential for enhancing human-machine interaction in lower limb rehabilitation applications.
• The findings support the use of adaptive sEMG-based models for precise biomechanical assessments.