Abstract
INTRODUCTION: Recently, low cost RGB-D depth sensors have emerged as a promising alternative for motion capture for clinical gait assessment. However, depth sensor based Mocap (D-Mocap) suffers from low accuracy and poor stability for 3D joint estimation due to noise, self-occlusion, interference, a
Highlights
Low cost RGB-D depth sensors have emerged as a promising alternative for motion capture for clinical gait assessment
The second dataset is OSU depth sensor based motion capture (Mocap) (D-Mocap) data which was collected by the Visual Computing and Image Processing Lab (VCIPL) and Biomechanical Analysis and Musculoskeletal Modeling (BAMM) Lab at Oklahoma State University (OSU), where the Orbbec depth sensor and the Nuitrack SDK were use in the data acquisition
We confirm that the extended Kalman filters (KF) (EKF) and unscented KF (UKF) significantly improve the accuracy of joint positions and joint angles for both simulated and real-world motion data
Summary
Low cost RGB-D depth sensors have emerged as a promising alternative for motion capture for clinical gait assessment. Depth sensor based Mocap (D-Mocap) suffers from low accuracy and poor stability for 3D joint estimation due to noise, self-occlusion, interference, and other technical limitations, which prevents it from being widely used in health-related applications. OBJECTIVES: The primary objective of this study is to integrate non-linear Kalman filters (KFs) and evolutionary algorithms to enhance the quality of D-Mocap data by jointly considering kinematic or anthropometric constraints at the joint-level and skeleton-level, respectively. The experimental results on two D-Mocap datasets show that the proposed TKF-DE method significantly improves the quality of D-Mocap data in terms of joint positions, bone lengths, and joint angles. D-Mocap suffers from low accuracy due to several technical limitations: the limited imaging range [9], self-occlusions during motion [10], and possible interference [11]
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