Gait Phase Estimation Based on Noncontact Capacitive Sensing and Adaptive Oscillators.

Area of Science:

• Biomedical Engineering
• Wearable Technology
• Human Motion Analysis

Background:

• Accurate gait phase estimation is crucial for controlling assistive devices like exoskeletons.
• Existing methods often require skin contact or are sensitive to clothing, limiting practical application.
• Developing noncontact, robust gait analysis systems is an ongoing research challenge.

Purpose of the Study:

• To present a novel strategy for accurate, walking-speed-adaptive gait phase estimation.
• To utilize noncontact capacitive sensing and adaptive oscillators (AOs) for gait analysis.
• To evaluate the system's performance in both offline and real-time scenarios.

Main Methods:

• A noncontact capacitive sensing system with two leg cuffs to measure muscle shape changes during walking.
• An adaptive oscillator (AO) dynamic system designed for tracking quasi-periodic capacitance signals.
• Offline and real-time experiments with healthy subjects walking on a treadmill at various speeds.

Main Results:

• The strategy achieved accurate and consistent gait phase estimation using a single channel of capacitance signal.
• Average root-mean-square errors were 0.19 rad (3.0%) for constant speeds and 0.31 rad (4.9%) for speed transitions.
• Successful validation in real-time gait phase estimation tasks with changing walking speeds.

Conclusions:

• The proposed strategy based on capacitive sensing and AOs is a viable noncontact method for gait phase estimation.
• The system demonstrates robustness to clothing and adaptability to varying walking speeds.
• This approach shows significant potential as an alternative for controlling exoskeleton and orthosis devices.