Detecting Toe-Off and Initial Contact in Real-Time With Self-Adapting Thresholds

Area of Science:

• Biomechanics
• Wearable Technology
• Robotics

Background:

• Accurate real-time gait phase detection is crucial for advanced prosthetic control and gait analysis.
• Existing methods often struggle with adaptability across different walking speeds and conditions.

Purpose of the Study:

• To develop and validate an adaptive control algorithm for precise, real-time gait event detection (initial contact and toe-off).
• To assess the algorithm's performance across a range of walking speeds using inertial measurement units (IMUs).

Main Methods:

• An adaptive algorithm was developed to determine gait phase using an IMU on the shank.
• Dynamic thresholds and ratios, along with safety checks, were employed for adaptive event determination.
• The algorithm was tested on eight participants walking at various speeds.

Main Results:

• The algorithm achieved high accuracy in detecting initial contact (100% success, 8.95 ms lead) and toe-off (99.72% success, 4.42 ms lead).
• Performance was consistent with established gait event detection algorithms.
• The adaptive nature proved effective across different walking speeds.

Conclusions:

• The developed adaptive algorithm offers a robust and precise solution for real-time gait phase detection.
• Its self-adaptive capabilities make it suitable for diverse movement scenarios, enhancing applications in prosthetics and rehabilitation.
• This technology holds promise for improving human-machine interaction in locomotion-related fields.