Interactive Full-Body Motion Capture Using Infrared Sensor Network

2013 ◽  
Vol 3 (4) ◽  
pp. 41-56 ◽  
Author(s):  
Son Duong ◽  
Min-Hyung Choi
Sensors ◽  
2015 ◽  
Vol 15 (9) ◽  
pp. 24297-24317 ◽  
Author(s):  
Zhiquan Gao ◽  
Yao Yu ◽  
Yu Zhou ◽  
Sidan Du
Keyword(s):  

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8034
Author(s):  
Susanne M. van der Veen ◽  
James S. Thomas

Fall rates are increasing among the aging population and even higher falls rates have been reported in populations with neurological impairments. The Berg Balance Scale is often used to assess balance in older adults and has been validated for use in people with stroke, traumatic brain injury, and Parkinson’s disease. While the Berg Balance Scale (BBS) has been found to be predictive of the length of rehabilitation stay following stroke, a recent review concluded the BBS lacked predictive validity for fall risk. Conversely, sophisticated measures assessing center of mass (COM) displacement have shown to be predictive of falls risk. However, calculating COM displacement is difficult to measure outside a laboratory. Accordingly, we sought to validate COM displacement measurements derived from an HTC Vive tracker secured to the pelvis by comparing it to COM derived from ‘gold’ standard laboratory-based full-body motion capture. Results showed that RMS between the COM calculated from HTC Vive tracker and full body motion capture agree with an average error rate of 2.1 ± 2.6 cm. Therefore, we conclude measurement of COM displacement using an HTC Vive tracker placed on the pelvis is reasonably representative of laboratory-based measurement of COM displacement.


2013 ◽  
Vol 722 ◽  
pp. 454-458
Author(s):  
Shu Ai Li ◽  
Yong Sheng Wang ◽  
Rui Pai Xiang

To solve the bottleneck problem of defining motion trajectory of virtual role in animation creation process, this paper presents a solution of mechanical human body motion capture technology, mainly involving inertia sensing technology, Bluetooth, the design of sensor network nodes and the development of reconstruction software of human body motion model. The system uses sensor network to collect motion data of the body key joints, and the data are delivered to workstation through Bluetooth, the software on workstation uses analytical inverse kinematics algorithm to analyze the motion data. So the system has advantages of lower cost and high precision. Meanwhile, the paper also provides a solid foundation for the research of multiplayer real-time motion capture technology.


Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4280 ◽  
Author(s):  
Matthew P. Mavor ◽  
Gwyneth B. Ross ◽  
Allison L. Clouthier ◽  
Thomas Karakolis ◽  
Ryan B. Graham

Investigating the effects of load carriage on military soldiers using optical motion capture is challenging. However, inertial measurement units (IMUs) provide a promising alternative. Our purpose was to compare optical motion capture with an Xsens IMU system in terms of movement reconstruction using principal component analysis (PCA) using correlation coefficients and joint kinematics using root mean squared error (RMSE). Eighteen civilians performed military-type movements while their motion was recorded using both optical and IMU-based systems. Tasks included walking, running, and transitioning between running, kneeling, and prone positions. PCA was applied to both the optical and virtual IMU markers, and the correlations between the principal component (PC) scores were assessed. Full-body joint angles were calculated and compared using RMSE between optical markers, IMU data, and virtual markers generated from IMU data with and without coordinate system alignment. There was good agreement in movement reconstruction using PCA; the average correlation coefficient was 0.81 ± 0.14. RMSE values between the optical markers and IMU data for flexion-extension were less than 9°, and 15° for the lower and upper limbs, respectively, across all tasks. The underlying biomechanical model and associated coordinate systems appear to influence RMSE values the most. The IMU system appears appropriate for capturing and reconstructing full-body motion variability for military-based movements.


Author(s):  
C. Einsmann ◽  
M. Quirk ◽  
B. Muzal ◽  
B. Venkatramani ◽  
T. Martin ◽  
...  

2016 ◽  
Vol 35 (6) ◽  
pp. 1-11 ◽  
Author(s):  
Tomislav Pejsa ◽  
Daniel Rakita ◽  
Bilge Mutlu ◽  
Michael Gleicher
Keyword(s):  

Sign in / Sign up

Export Citation Format

Share Document