Real-Time Gait Event Detection with Adaptive Frequency Oscillators from a Single Head-Mounted IMU

Area of Science:

• Biomechanics
• Wearable Technology
• Rehabilitation Engineering

Background:

• Accurate real-time gait event detection is crucial for advancing gait rehabilitation, especially with robotics and virtual reality (VR).
• Affordable wearable inertial measurement units (IMUs) have enabled new gait analysis methods.
• Traditional gait event detection algorithms have limitations.

Purpose of the Study:

• To highlight the advantages of adaptive frequency oscillators (AFOs) for gait event detection.
• To implement and validate a real-time AFO-based algorithm using a single head-mounted IMU for gait phase estimation.
• To assess the method's utility in VR applications.

Main Methods:

• Developed a real-time gait phase estimation algorithm based on adaptive frequency oscillators (AFOs).
• Utilized a single head-mounted inertial measurement unit (IMU) for data acquisition.
• Validated the algorithm on healthy subjects at different walking speeds.

Main Results:

• The AFO-based gait event detection was accurate at two different walking speeds.
• The method demonstrated reliability for symmetric gait patterns.
• The algorithm's performance was not reliable for asymmetric gait patterns.

Conclusions:

• The AFO-based method offers an accurate and efficient approach to real-time gait event detection.
• This technique is particularly promising for integration into virtual reality (VR) systems due to the common use of head-mounted IMUs.
• Further research may be needed to address reliability in asymmetric gait patterns.