Development and Validation of a Modular Sensor-Based System for Gait Analysis and Control in Lower-Limb Exoskeletons

Area of Science:

• Wearable Robotics
• Biomechanics
• Control Systems

Background:

• Accurate user biomechanics assessment and reliable exoskeleton control remain challenges despite hardware advancements.
• Traditional methods are often lab-bound, expensive, and lack real-world applicability.
• Existing systems struggle with real-time data processing for adaptive control.

Purpose of the Study:

• To develop a modular, sensor-based system for enhanced biomechanical evaluation and control of lower-limb exoskeletons.
• To provide a cost-effective and open-source alternative to traditional lab-bound assessment methods.
• To integrate advanced sensors and fuzzy logic for real-time gait phase estimation and exoskeleton control.

Main Methods:

• Integration of inertial measurement units, force-sensitive resistors, and load cells into instrumented crutches and 3D-printed insoles.
• Real-time data processing using fuzzy logic algorithms for gait phase estimation and exoskeleton control.
• Validation experiments comparing the system against gold-standard motion capture and force plate systems.

Main Results:

• The system reliably detects gait phases with high accuracy.
• Accurate measurement of key biomechanical parameters, including center of pressure and crutch ground reaction forces.
• Demonstrated feasibility of real-time data processing for adaptive exoskeleton control.

Conclusions:

• The developed modular sensor system offers a reliable and cost-effective solution for lower-limb exoskeleton research.
• The open-source nature promotes accessibility and further innovation in wearable robotics.
• This system advances the potential for real-world application of lower-limb exoskeletons through improved control and assessment.