Estimating Lower Limb Kinematics using a Reduced Wearable Sensor Count

by   Luke Sy, et al.

Goal: This paper presents an algorithm for accurately estimating pelvis, thigh, and shank kinematics during walking using only three wearable inertial sensors. Methods: The algorithm makes novel use of a constrained Kalman filter (CKF). The algorithm iterates through the prediction update (kinematic equation), measurement update (soft pelvis constraint, zero velocity update, flat-floor assumption, and covariance limiter), and constraint update (formulation of hinged knee joints and ball-and-socket hip joints). Results: Evaluation of the algorithm given Vicon-based sensor-to-segment calibration on nine healthy subjects who walked freely within a 4 x 4 m^3 room shows that it can track motion relative to the mid-pelvis origin with mean position and orientation root-mean-square error (RMSE) of 5.21 ± 1.39 cm and 16.1 ± 3.2^∘, respectively. The sagittal knee and hip joint angle RMSEs were 10.0 ± 2.9^∘ and 9.9 ± 3.2^∘, respectively, while the corresponding correlation coefficient (CC) values were 0.87 ± 0.09 and 0.74 ± 0.14. Conclusion: The CKF-based algorithm was able to track the 3D pose of the pelvis, thigh, and shanks using only three inertial sensors worn on the pelvis and shanks. Significance: Due to the Kalman filter based algorithm's low computation cost and the systems' ease of attachment, gait parameters can be computed in real-time and remotely for long term gait monitoring. Furthermore, the system can be used to inform gait assistive devices.


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