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

Abstract

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.

Estimation of Steering and Throttle Angles of a Motorized Mobility Scooter with Inertial Measurement Units for Continuous Quantification of Driving Operation

“…The required accuracy, however, is dependent on the objectives of the operational measurement and analysis. If necessary, it will be possible to improve the estimation accuracy by introducing a complementary or Kalman filter to fuse the accelerations and angular velocities into the operating angles [37,44,50]. The proposed method achieved a practical accuracy using only the accelerations from IMUs and did not require high hardware configurations for the use of the filter processing.…”

“…An equation with rotation matrices that represents the geometrical relationship between the IMU and the two rotational axes of the operating interfaces can be solved to estimate the throttle and steering angles. Previous studies on IMU-based estimations of human motion and joint angles during ambulation also used a similar calculation approach and reported various sources of estimation errors, including vibration and misalignment of the IMU axes [41][42][43][44]. We also evaluated the estimation accuracy of the proposed method by implementing the IMU system on a test MMS.…”

Evaluation of Error-State Kalman Filter Method for Estimating Human Lower-Limb Kinematics during Various Walking Gaits

“…This paper extends the ErKF method in [ 37 ] for a full seven-body model, representing the feet, shanks, thighs, and pelvis. Further, advancements are made to the method to handle additional uncertainties and errors due to the complexities of biological joints and tissue (e.g., increased uncertainty in the sensor to segment alignment) that manifest in a seven-body model of human subjects (as opposed to the simplified constructed model in [ 37 ]). As noted above, this study focuses on IMU kinematic estimation and not the separate step of sensor to segment alignment which is an open research challenge in its own right [ 38 , 39 , 40 ].…”

“…Motivated by the limitations summarized above, the present study contributes a new error-state Kalman filter (ErKF) method to estimate the kinematics of the lower-limbs across a wide variety of walking gaits. In [ 37 ], we presented an ErKF method for a simplified three-body constructed model of the human lower limbs and demonstrated its success in accurately estimating joint angles, stride length, and step width over a long (ten-minute) trial. This paper extends the ErKF method in [ 37 ] for a full seven-body model, representing the feet, shanks, thighs, and pelvis.…”

“…In [ 37 ], we presented an ErKF method for a simplified three-body constructed model of the human lower limbs and demonstrated its success in accurately estimating joint angles, stride length, and step width over a long (ten-minute) trial. This paper extends the ErKF method in [ 37 ] for a full seven-body model, representing the feet, shanks, thighs, and pelvis. Further, advancements are made to the method to handle additional uncertainties and errors due to the complexities of biological joints and tissue (e.g., increased uncertainty in the sensor to segment alignment) that manifest in a seven-body model of human subjects (as opposed to the simplified constructed model in [ 37 ]).…”