Estimating Lower Limb Kinematics Using a Reduced Wearable Sensor Count.

Area of Science:

• Biomechanics
• Wearable Technology
• Robotics

Background:

• Accurate estimation of human lower limb kinematics is crucial for gait analysis, rehabilitation, and assistive device development.
• Traditional motion capture systems are often cumbersome and limited to laboratory settings.
• Wearable inertial sensors offer a portable and convenient alternative, but achieving high accuracy remains a challenge.

Purpose of the Study:

• To develop and validate a novel algorithm for accurate estimation of pelvis, thigh, and shank kinematics during walking using only three wearable inertial sensors.
• To leverage a constrained Kalman filter (CKF) for improved kinematic tracking accuracy.
• To assess the algorithm's performance against optical motion capture in terms of position, orientation, and joint angle accuracy.

Main Methods:

• Development of a constrained Kalman filter (CKF) algorithm incorporating kinematic equations, pelvis position pseudo-measurements, zero velocity updates, flat-floor assumption, and covariance limitation.
• Formulation of hinged knee and ball-and-socket hip joint constraints within the CKF framework.
• Validation using optical motion capture on nine participants, employing sensor-to-segment calibration.

Main Results:

• The CKF-based algorithm achieved mean root-mean-square error (RMSE) of [Formula: see text] cm for position and [Formula: see text] for orientation relative to the mid-pelvis origin.
• Sagittal knee and hip joint angle RMSEs were [Formula: see text] and [Formula: see text], respectively, with high correlation coefficients (CC) of [Formula: see text] and [Formula: see text].
• The algorithm successfully tracked the 3D pose of the pelvis, thigh, and shanks using only three inertial sensors.

Conclusions:

• The proposed CKF-based algorithm accurately estimates lower limb kinematics during walking using a minimal sensor setup.
• The algorithm's low computational cost and convenience facilitate real-time, remote gait monitoring and inform the development of gait assistive devices.