Development of motion correction technique for cardiac 15O-water PET study using an optical motion tracking system

2009 ◽  
Vol 24 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Kazuhiro Koshino ◽  
Hiroshi Watabe ◽  
Shinji Hasegawa ◽  
Takuya Hayashi ◽  
Jun Hatazawa ◽  
...  
Keyword(s):  
Motion Correction ◽  
Motion Tracking ◽  
Tracking System ◽  
Correction Technique ◽  
Optical Motion ◽  
Optical Motion Tracking ◽  
Motion Tracking System

Sinogram-based motion correction of PET images using optical motion tracking system and list-mode data acquisition

2004 ◽  
Vol 51 (3) ◽  
pp. 782-788 ◽  
Author(s):  
Sang-Keun Woo ◽  
H. Watabe ◽  
Yong Choi ◽  
Kyeong Min Kim ◽  
Chang Choon Park ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Motion Correction ◽  
Motion Tracking ◽  
Tracking System ◽  
List Mode ◽  
Optical Motion ◽  
Optical Motion Tracking ◽  
Motion Tracking System

scholarly journals A Low-Cost Motion Capture System for Small-Scale Wave Energy Device Tank Testing

2018 ◽  
Vol 141 (3) ◽  
Author(s):  
Francesco Paparella ◽  
Satja Sivcev ◽  
Daniel Toal ◽  
John V. Ringwood
Keyword(s):  
Wave Energy ◽  
Motion Tracking ◽  
Tracking System ◽  
Low Cost ◽  
Measurement Unit ◽  
Small Scale ◽  
Energy Device ◽  
Optical Motion ◽  
Optical Motion Tracking ◽  
Motion Tracking System

The measurement of the motion of a small-scale wave energy device during wave tank tests is important for the evaluation of its response to waves and the assessment of power production. Usually, the motion of a small-scale wave energy converter (WEC) is measured using an optical motion tracking system with high precision and sampling rate. However, the cost for an optical motion tracking system can be considerably high and, therefore, the overall cost for tank testing is increased. This paper proposes a low-cost capture system composed of an inertial measurement unit and ultrasound sensors. The measurements from the ultrasound sensors are combined optimally with the measurements from the inertial measurement unit through an extended Kalman filter (EKF) in order to obtain an accurate estimation of the motion of a WEC.

Implementation and performance of an optical motion tracking system for high resolution brain PET imaging

1999 ◽  
Vol 46 (6) ◽  
pp. 2059-2067 ◽  
Author(s):  
B.J. Lopresti ◽  
A. Russo ◽  
W.F. Jones ◽  
T. Fisher ◽  
D.G. Crouch ◽  
...  
Keyword(s):  
High Resolution ◽  
Pet Imaging ◽  
Motion Tracking ◽  
Tracking System ◽  
Brain Pet ◽  
Optical Motion ◽  
Optical Motion Tracking ◽  
And Performance ◽  
Motion Tracking System

Ballistocardiographic artifact removal from simultaneous EEG-fMRI using an optical motion-tracking system

NeuroImage ◽  
2013 ◽  
Vol 75 ◽  
pp. 1-11 ◽  
Author(s):  
Pierre LeVan ◽  
Julian Maclaren ◽  
Michael Herbst ◽  
Rebecca Sostheim ◽  
Maxim Zaitsev ◽  
...  
Keyword(s):  
Motion Tracking ◽  
Tracking System ◽  
Artifact Removal ◽  
Optical Motion ◽  
Optical Motion Tracking ◽  
Motion Tracking System

Characterizing Suitability of Wearable Sensors for Movement Analysis Using a Programmed Robotic Motion

2013 ◽  
Author(s):  
Amanda L. Martori ◽  
Stephanie L. Carey ◽  
Redwan Alqasemi ◽  
Daniel Ashley ◽  
Rajiv V. Dubey
Keyword(s):  
Motion Tracking ◽  
Tracking System ◽  
Wearable Sensors ◽  
Robotic Arm ◽  
Joint Angles ◽  
Optical Motion ◽  
Optical Motion Tracking ◽  
Motion Tracking System ◽  
Insight Into

Wearable sensor systems have the potential to offer advancements in the study of motion disorders, particularly outside of a laboratory setting during activities of daily living or on a football field. Advantages like portability and the capability to gather real-world data have resulted in the rapid adoption of these sensors in various studies for gait analysis, balance control evaluation, physical activity recognition and fall prevention. However, before using wearable sensors in long-term acquisition studies, it is necessary to quantify and analyze errors and determine their sources. In this study, the accuracy of joint angles and velocities measured with the wearable inertial measurement unit (IMU) sensors were compared to both measurements from an optical motion-tracking system and from encoders on a robotic arm while it completed various predetermined paths. The robotic arm uses incremental encoders at each joint to measure and calculate its Cartesian motion relative to a reference frame using inverse kinematics. Motion profiles of the robotic arm were tracked using the onboard encoders, an eight-camera Vicon (Oxford, UK) motion-tracking system with passive retro-reflective markers, and four wearable IMUs by APDM (Portland, OR). In order to better isolate various types of contributing errors, linear, planar, and 3-dimensional robot motions were used. Data were collected from the sensors over several hours, which provided insight into time-based effects as well as management of large amounts of data for future long-term tracking applications. In addition, the authors have previously seen acquisition errors with high-speed gaits, thus robotic arm trajectories of varying velocities were used to provide further insight into these rate-based effects. Angular velocity and joint angles were compared for all three systems and used to investigate the hysteresis, drift and time-based effects on the IMUs as well as their accuracy during motion tracking. Effects on IMU performance due to the application of filtering algorithms were not investigated. The results show that the IMUs were able to calculate the joint angles within a clinically acceptable range of the gold standard optical motion-tracking system. The IMUs also provided accurate trajectory recognition and angular velocity measurements relative to the known motion input of the robotic arm. Future work will include the development of algorithms to detect gait abnormalities such as those seen in patients with mild traumatic brain injury (mTBI). To complement human subject testing with gait pathology, controlled introduction of gait deviations into this robotic testing framework will allow for well-characterized unit testing, providing more robust algorithm development.

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