Enhanced Gait Phase Estimation for Lower Limb Exoskeletons by Integrating Contralateral Leg Information

Area of Science:

• Biomechanics
• Robotics
• Rehabilitation Engineering

Background:

• Human gait phase estimation is crucial for controlling lower limb exoskeletons using biological signals.
• Accurate gait phase estimation is essential to prevent device-user desynchronization and ensure proper torque application.
• Current methods face challenges in achieving the high accuracy required for effective exoskeleton control.

Purpose of the Study:

• To develop a novel method for improving human gait phase estimation accuracy.
• To leverage contralateral leg information to enhance gait phase estimation.
• To validate the proposed method's effectiveness in both able-bodied and hemiparetic individuals.

Main Methods:

• A new algorithm was developed to incorporate contralateral leg data for gait phase estimation.
• The method was tested using simulation data for able-bodied individuals.
• Clinical data from three hemiparetic stroke patients were used to evaluate the method's performance.

Main Results:

• A 75.2% reduction in estimation error was observed compared to state-of-the-art methods in able-bodied simulations.
• An average of 47.6% reduction in estimation error was achieved with hemiparetic stroke patient data.
• An overall Root Mean Square Error (RMSE) of 2.28% was demonstrated across all subjects.

Conclusions:

• The proposed contralateral leg-based method significantly improves gait phase estimation accuracy.
• This technique shows promise for enhancing the control of rehabilitative robotics, particularly lower limb exoskeletons.
• Improved exoskeleton control can lead to greater user participation in robot-assisted gait training for stroke recovery.