Foot Pose Estimation Using an Inertial Sensor Unit and Two Distance Sensors

Area of Science:

• Biomechanics
• Sensor Fusion
• Robotics

Background:

• Inertial sensor-based foot pose estimation is widely used.
• Existing methods face accuracy limitations, especially in vertical position.

Purpose of the Study:

• To improve foot pose estimation accuracy.
• To integrate low-cost distance sensors with inertial measurement units.

Main Methods:

• Utilized two VL6180 time-of-flight distance sensors and an inertial sensor unit.
• Developed a 21-state Kalman filter for initial calibration and foot pose estimation.
• Employed a 9-state Kalman filter for subsequent foot pose estimation after calibration.

Main Results:

• The proposed algorithm significantly enhances vertical position estimation accuracy.
• Demonstrated improved performance across diverse activities: walking, dancing, ball kicking, and jumping.

Conclusions:

• Combining inertial sensors with low-cost distance sensors offers a viable method for accurate foot pose estimation.
• The Kalman filter approach effectively handles sensor fusion and calibration for improved kinematic tracking.