Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Estimating Lower Limb Kinematics using a Lie Group Constrained Extended Kalman Filter with a Reduced Wearable IMU Count and Distance Measurements

Version 1 : Received: 22 October 2020 / Approved: 3 November 2020 / Online: 3 November 2020 (15:24:43 CET)

How to cite: Sy, L.W.F.; Lovell, N.H.; Redmond, S.J. Estimating Lower Limb Kinematics using a Lie Group Constrained Extended Kalman Filter with a Reduced Wearable IMU Count and Distance Measurements. Preprints 2020, 2020110166 (doi: 10.20944/preprints202011.0166.v1). Sy, L.W.F.; Lovell, N.H.; Redmond, S.J. Estimating Lower Limb Kinematics using a Lie Group Constrained Extended Kalman Filter with a Reduced Wearable IMU Count and Distance Measurements. Preprints 2020, 2020110166 (doi: 10.20944/preprints202011.0166.v1).

Abstract

Tracking the kinematics of human movement usually requires the use of equipment that constrains the user within a room (e.g., optical motion capture systems), or requires the use of a conspicuous body-worn measurement system (e.g., inertial measurement units (IMUs) attached to each body segment). This paper presents a novel Lie group constrained extended Kalman filter to estimate lower limb kinematics using IMU and inter-IMU distance measurements in a reduced sensor count configuration. The algorithm iterates through the prediction (kinematic equations), measurement (pelvis height assumption/inter-IMU distance measurements, zero velocity update for feet/ankles, flat-floor assumption for feet/ankles, and covariance limiter), and constraint update (formulation of hinged knee joints and ball-and-socket hip joints). The knee and hip joint angle root-mean-square errors in the sagittal plane for straight walking were 7.6±2.6∘ and 6.6±2.7∘, respectively, while the correlation coefficients were 0.95±0.03 and 0.87±0.16, respectively. Furthermore, experiments using simulated inter-IMU distance measurements show that performance improved substantially for dynamic movements, even at large noise levels (σ=0.2 m). However, further validation is recommended with actual distance measurement sensors, such as ultra-wideband ranging sensors.

Subject Areas

Lie group; Constrained extended Kalman filter; Gait analysis; Motion capture; Pose estimation; Wearable devices; IMU; Distance measurement

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