scholarly journals Estimating Three-Dimensional Body Orientation Based on an Improved Complementary Filter for Human Motion Tracking

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3765 ◽  
Author(s):  
Chunzhi Yi ◽  
Jiantao Ma ◽  
Hao Guo ◽  
Jiahong Han ◽  
Hefu Gao ◽  
...  
Keyword(s):  
Motion Tracking ◽  
Filter Design ◽  
Angular Rate ◽  
Human Motion ◽  
Body Orientation ◽  
Measurement Unit ◽  
Test Machine ◽  
Gradient Descent Method ◽  
Human Motion Tracking ◽  
Complementary Filter

Rigid body orientation determined by IMU (Inertial Measurement Unit) is widely applied in robotics, navigation, rehabilitation, and human-computer interaction. In this paper, aiming at dynamically fusing quaternions computed from angular rate integration and FQA algorithm, a quaternion-based complementary filter algorithm is proposed to support a computationally efficient, wearable motion-tracking system. Firstly, a gradient descent method is used to determine a function from several sample points. Secondly, this function is used to dynamically estimate the fusion coefficient based on the deviation between measured magnetic field, gravity vectors and their references in Earth-fixed frame. Thirdly, a test machine is designed to evaluate the performance of designed filter. Experimental results validate the filter design and show its potential of real-time human motion tracking.

scholarly journals Towards Human Motion Tracking: Multi-Sensory IMU/TOA Fusion Method and Fundamental Limits

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 142 ◽  
Author(s):  
Cheng Xu ◽  
Jie He ◽  
Xiaotong Zhang ◽  
Xinghang Zhou ◽  
Shihong Duan
Keyword(s):  
Inertial Measurement Unit ◽  
Motion Tracking ◽  
Human Motion ◽  
Measurement Unit ◽  
Time Of Arrival ◽  
Fusion Method ◽  
Tracking Accuracy ◽  
Fundamental Limits ◽  
Inertial Measurement ◽  
Human Motion Tracking

Human motion tracking could be viewed as a multi-target tracking problem towards numerous body joints. Inertial-measurement-unit-based human motion tracking technique stands out and has been widely used in body are network applications. However, it has been facing the tough problem of accumulative errors and drift. In this paper, we propose a multi-sensor hybrid method to solve this problem. Firstly, an inertial-measurement-unit and time-of-arrival fusion-based method is proposed to compensate the drift and accumulative errors caused by inertial sensors. Secondly, Cramér–Rao lower bound is derived in detail with consideration of both spatial and temporal related factors. Simulation results show that the proposed method in this paper has both spatial and temporal advantages, compared with traditional sole inertial or time-of-arrival-based tracking methods. Furthermore, proposed method is verified in 3D practical application scenarios. Compared with state-of-the-art algorithms, proposed fusion method shows better consistency and higher tracking accuracy, especially when moving direction changes. The proposed fusion method and comprehensive fundamental limits analysis conducted in this paper can provide a theoretical basis for further system design and algorithm analysis. Without the requirements of external anchors, the proposed method has good stability and high tracking accuracy, thus it is more suitable for wearable motion tracking applications.

scholarly journals Analysis of the Accuracy of Ten Algorithms for Orientation Estimation Using Inertial and Magnetic Sensing Under Optimal Conditions: One Size Does Not Fit All

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2543
Author(s):  
Marco Caruso ◽  
Angelo Maria Sabatini ◽  
Daniel Laidig ◽  
Thomas Seel ◽  
Marco Knaflitz ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Motion Tracking ◽  
Recent Literature ◽  
Human Motion ◽  
Measurement Unit ◽  
Optimal Conditions ◽  
Inertial Measurement ◽  
Rotation Rates ◽  
Significant Performance ◽  
Parameter Values

The orientation of a magneto and inertial measurement unit (MIMU) is estimated by means of sensor fusion algorithms (SFAs) thus enabling human motion tracking. However, despite several SFAs implementations proposed over the last decades, there is still a lack of consensus about the best performing SFAs and their accuracy. As suggested by recent literature, the filter parameters play a central role in determining the orientation errors. The aim of this work is to analyze the accuracy of ten SFAs while running under the best possible conditions (i.e., their parameter values are set using the orientation reference) in nine experimental scenarios including three rotation rates and three commercial products. The main finding is that parameter values must be specific for each SFA according to the experimental scenario to avoid errors comparable to those obtained when the default parameter values are used. Overall, when optimally tuned, no statistically significant differences are observed among the different SFAs in all tested experimental scenarios and the absolute errors are included between 3.8 deg and 7.1 deg. Increasing the rotation rate generally leads to a significant performance worsening. Errors are also influenced by the MIMU commercial model. SFA MATLAB implementations have been made available online.

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
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