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

TL;DR

This paper presents a modular, sensor-based system that improves gait analysis and control in lower-limb exoskeletons using advanced sensors and fuzzy logic, validated against gold-standard methods.

Contribution

Introduces an open-source, cost-effective modular sensor system with fuzzy logic for real-time gait analysis and exoskeleton control, surpassing laboratory limitations.

Findings

Reliable gait phase detection demonstrated in validation experiments.

Accurate biomechanical measurements comparable to gold-standard systems.

Open-source design promotes broader adoption and innovation.

Abstract

With rapid advancements in exoskeleton hardware technologies, successful assessment and accurate control remain challenging. This study introduces a modular sensor-based system to enhance biomechanical evaluation and control in lower-limb exoskeletons, utilizing advanced sensor technologies and fuzzy logic. We aim to surpass the limitations of current biomechanical evaluation methods confined to laboratories and to address the high costs and complexity of exoskeleton control systems. The system integrates inertial measurement units, force-sensitive resistors, and load cells into instrumented crutches and 3D-printed insoles. These components function both independently and collectively to capture comprehensive biomechanical data, including the anteroposterior center of pressure and crutch ground reaction forces. This data is processed through a central unit using fuzzy logic algorithms for real-time gait phase estimation and exoskeleton control. Validation experiments with three participants, benchmarked against gold-standard motion capture and force plate technologies, demonstrate our system's capability for reliable gait phase detection and precise biomechanical measurements. By offering our designs open-source and integrating cost-effective technologies, this study advances wearable robotics and promotes broader innovation and adoption in exoskeleton research.