Quasi real-time gait event detection using shank-attached gyroscopes

Area of Science:

• Biomedical Engineering
• Biomechanics
• Wearable Technology

Background:

• Long-term monitoring of walking relies on accurate gait event detection.
• Existing gyroscope-based algorithms for gait event detection are often post-processed, limiting real-time application.
• There is a need for robust, quasi real-time gait event detection methods for ambulatory monitoring.

Purpose of the Study:

• To develop and validate a modified algorithm for quasi real-time detection of foot initial and end contact timings.
• To improve upon post-processed gait event detection algorithms using shank-attached gyroscope signals.
• To ensure the robustness of gait event detection across various walking speeds for practical ambulatory monitoring.

Main Methods:

• A modified algorithm utilizing shank-attached gyroscope signals for gait event detection.
• Leveraging knowledge of gait sequence and peak angular acceleration for quasi real-time detection.
• Validation against a footswitch-based method to assess accuracy and timing differences.

Main Results:

• The proposed algorithm achieves quasi real-time detection of foot initial and end contact timings.
• The algorithm demonstrates robustness in detecting gait events across different walking speeds.
• Validation showed the algorithm detected end contacts 8 ms earlier and initial contacts 19 ms later than footswitches.

Conclusions:

• The developed algorithm offers a practical, quasi real-time solution for ambulatory gait event detection.
• This method enhances long-term walking monitoring by providing more immediate gait event data.
• The algorithm's robustness and accuracy make it suitable for diverse clinical and research applications.