Human movement tracking based on Kalman filter

2006 ◽  
Author(s):  
Yi Zhang ◽  
Yuan Luo
Keyword(s):  
Kalman Filter ◽  
Human Movement ◽  
Movement Tracking

scholarly journals Error-state Kalman filter for lower-limb kinematic estimation: Evaluation on a 3-body model

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249577
Author(s):  
Michael V. Potter ◽  
Stephen M. Cain ◽  
Lauro V. Ojeda ◽  
Reed D. Gurchiek ◽  
Ryan S. McGinnis ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Lower Limb ◽  
Injury Risk ◽  
Human Subjects ◽  
Human Movement ◽  
Lower Limbs ◽  
Direct Evaluation ◽  
Body Model ◽  
Optical Motion ◽  
Error State

Human lower-limb kinematic measurements are critical for many applications including gait analysis, enhancing athletic performance, reducing or monitoring injury risk, augmenting warfighter performance, and monitoring elderly fall risk, among others. We present a new method to estimate lower-limb kinematics using an error-state Kalman filter that utilizes an array of body-worn inertial measurement units (IMUs) and four kinematic constraints. We evaluate the method on a simplified 3-body model of the lower limbs (pelvis and two legs) during walking using data from simulation and experiment. Evaluation on this 3-body model permits direct evaluation of the ErKF method without several confounding error sources from human subjects (e.g., soft tissue artefacts and determination of anatomical frames). RMS differences for the three estimated hip joint angles all remain below 0.2 degrees compared to simulation and 1.4 degrees compared to experimental optical motion capture (MOCAP). RMS differences for stride length and step width remain within 1% and 4%, respectively compared to simulation and 7% and 5%, respectively compared to experiment (MOCAP). The results are particularly important because they foretell future success in advancing this approach to more complex models for human movement. In particular, our future work aims to extend this approach to a 7-body model of the human lower limbs composed of the pelvis, thighs, shanks, and feet.

Predictive head movement tracking using a Kalman filter

1997 ◽  
Vol 27 (2) ◽  
pp. 326-331 ◽  
Author(s):  
A. Kiruluta ◽  
M. Eizenman ◽  
S. Pasupathy
Keyword(s):  
Kalman Filter ◽  
Head Movement ◽  
Movement Tracking
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