Design and Performance Evaluation of a Wearable Sensing System for Lower-Limb Exoskeleton.

Area of Science:

• Robotics and Human-Computer Interaction
• Biomedical Engineering
• Wearable Sensor Technology

Background:

• Assistive lower extremity exoskeletons (ASLEEs) use customized gait for users with lower limb disabilities.
• Current customized gaits lack stability on varied terrains like slopes and soft ground.
• Adaptive control is crucial for enhancing ASLEE functionality and user mobility.

Purpose of the Study:

• To develop a novel wearable sensing system for gait detection and environment feature recognition in ASLEEs.
• To enable adaptive control strategies for ASLEEs operating in diverse environmental conditions.
• To enhance the stability and performance of ASLEEs on challenging terrains.

Main Methods:

• A wearable sensing system integrating 7 force sensors and 1 IMU sensor was developed.
• The system captures ground reaction forces and angular velocity data during locomotion.
• Sensor fusion techniques were employed to process the collected data.

Main Results:

• The system achieved high accuracy in detecting ground reaction forces.
• Fusion of force and angular velocity data enabled recognition of four typical terrain features.
• A terrain recognition rate of up to 93% was successfully achieved.

Conclusions:

• The developed wearable sensing system is effective for gait detection and environment feature recognition.
• This technology can significantly improve the stability and adaptability of ASLEEs on variable terrain.
• The findings pave the way for more robust and responsive assistive exoskeleton control systems.