scholarly journals Human Motion Tracking Based on Unscented Kalman Filter in Sports Domain

Kalman Filter ◽  
10.5772/9579 ◽  
2010 ◽  
Author(s):  
GuoJun Liu ◽  
XiangLong Tang
Keyword(s):  
Kalman Filter ◽  
Motion Tracking ◽  
Unscented Kalman Filter ◽  
Human Motion ◽  
Human Motion Tracking

Hierarchical Model-Based Human Motion Tracking Via Unscented Kalman Filter

2007 ◽  
Author(s):  
GuoJun Liu ◽  
XiangLong Tang ◽  
JianHua Huang ◽  
JiaFeng Liu ◽  
Da Sun
Keyword(s):  
Kalman Filter ◽  
Hierarchical Model ◽  
Motion Tracking ◽  
Unscented Kalman Filter ◽  
Human Motion ◽  
Human Motion Tracking ◽  
Model Based

A Novel Kalman Filter for Human Motion Tracking With an Inertial-Based Dynamic Inclinometer

2015 ◽  
Vol 62 (8) ◽  
pp. 2033-2043 ◽  
Author(s):  
Gabriele Ligorio ◽  
Angelo M. Sabatini
Keyword(s):  
Kalman Filter ◽  
Motion Tracking ◽  
Human Motion ◽  
Human Motion Tracking

Human Arm Motion Tracking by Orientation-Based Fusion of Inertial Sensors and Kinect Using Unscented Kalman Filter

2016 ◽  
Vol 138 (9) ◽  
Author(s):  
Arash Atrsaei ◽  
Hassan Salarieh ◽  
Aria Alasty
Keyword(s):  
Kalman Filter ◽  
Motion Capture ◽  
Motion Tracking ◽  
Inertial Sensors ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Human Motion ◽  
Home Based ◽  
Human Arm ◽  
Arm Motion

Due to various applications of human motion capture techniques, developing low-cost methods that would be applicable in nonlaboratory environments is under consideration. MEMS inertial sensors and Kinect are two low-cost devices that can be utilized in home-based motion capture systems, e.g., home-based rehabilitation. In this work, an unscented Kalman filter approach was developed based on the complementary properties of Kinect and the inertial sensors to fuse the orientation data of these two devices for human arm motion tracking during both stationary shoulder joint position and human body movement. A new measurement model of the fusion algorithm was obtained that can compensate for the inertial sensors drift problem in high dynamic motions and also joints occlusion in Kinect. The efficiency of the proposed algorithm was evaluated by an optical motion tracker system. The errors were reduced by almost 50% compared to cases when either inertial sensor or Kinect measurements were utilized.

scholarly journals Human motion tracking based on complementary Kalman filter

2017 ◽  
Author(s):  
Zhi-Bo Wang ◽  
Lin Yang ◽  
Zhi-Pei Huang ◽  
Jian-Kang Wu ◽  
Zhi-Qiang Zhang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Motion Tracking ◽  
Human Motion ◽  
Human Motion Tracking

HISTORY: The Use of the Kalman Filter for Human Motion Tracking in Virtual Reality

2009 ◽  
Vol 18 (1) ◽  
pp. 72-91 ◽  
Author(s):  
Gregory F Welch
Keyword(s):  
Virtual Reality ◽  
Kalman Filter ◽  
Motion Tracking ◽  
50Th Anniversary ◽  
Human Motion ◽  
Linear Filtering ◽  
Human Motion Tracking ◽  
Assisted Navigation ◽  
Comprehensive Survey ◽  
Prediction Problems

In 1960 Rudolph E. Kalman published his now famous article describing a recursive solution to the discrete-data linear filtering problem (Kalman, “A new approach to linear filtering and prediction problems,” Transactions of the ASME—Journal of Basic Engineering, 82 (D), 35–45, 1960). Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and applications, particularly in the area of autonomous or assisted navigation. The purpose of this paper is to acknowledge the approaching 50th anniversary of the Kalman filter with a look back at the use of the filter for human motion tracking in virtual reality (VR) and augmented reality (AR). In recent years there has been an explosion in the use of the Kalman filter in VR/AR. In fact, at technical conferences related to VR these days, it would be unusual to see a paper on tracking that did not use some form of a Kalman filter, or draw comparisons to those that do. As such, rather than attempt a comprehensive survey of all uses of the Kalman filter to date, what follows focuses primarily on the early discovery and subsequent period of evolution of the Kalman filter in VR, along with a few examples of modern commercial systems that use the Kalman filter. This paper begins with a very brief introduction to the Kalman filter, a brief look at the origins of VR, a little about tracking in VR—in particular the work and conditions that gave rise to the use of the filter, and then the evolution of the use of the filter in VR.

Probabilistic graphicalmodels for human motion tracking

2009 ◽  
Author(s):  
Jose I. Gomez ◽  
M.J. Marin-Jimenez ◽  
N. Perez de la Blanca
Keyword(s):  
Motion Tracking ◽  
Human Motion ◽  
Human Motion Tracking

scholarly journals Markerless Multi-view Human Motion Tracking Using Manifold Model Learning by Charting

2012 ◽  
Vol 41 ◽  
pp. 664-670 ◽  
Author(s):  
Sanjay Saini ◽  
Dayang Rohaya Bt Awang Rambli ◽  
Suziah Bt Sulaiman ◽  
M Nordin B Zakaria ◽  
Siti Rohkmah
Keyword(s):  
Motion Tracking ◽  
Human Motion ◽  
Model Learning ◽  
Human Motion Tracking
Sign in / Sign up

Export Citation Format

Share Document