A Normative Database of Thumb Circumduction in Vivo: Center of Rotation and Range of Motion

2002 ◽  
Vol 46 (13) ◽  
pp. 1167-1171 ◽  
Author(s):  
Peter Braido ◽  
Xudong Zhang ◽  
Robert Hefner ◽  
Mark Redden
Keyword(s):  
Range Of Motion ◽  
Motion Capture ◽  
Local Coordinate System ◽  
Hand Movement ◽  
Three Dimensional ◽  
Biomechanical Modeling ◽  
Motion Capture System ◽  
Normative Database ◽  
Center Of Rotation

This study aimed to explore the use of a contemporary motion capture system for measuring in vivo maximum thumb circumduction, and through biomechanical modeling and statistical analysis, to establish a normative database of three-dimensional functional thumb range of motion (ROM) and examine the effects of anthropometry and gender. Twenty-eight (14 males and 14 females) anthropometrically diversified subjects performed maximum voluntary thumb circumductions, as the trajectories of surface markers placed on their thumb landmarks were measured by an opto-electronic motion capture system. Aglobographic representation method determined the best fitting spheres, the center of rotation (COR) expressed in a local coordinate system, and reference axes of thumb circumduction. Thumb ROM was quantified using (1) the cone volume circumscribed by the thumb, and (2) the time-varying included angle with respect to a reference axis, expressed as the joint sinus. Statistical analyses suggest that gender is the most important factor ( p < 0.05) in determining the COR while anthropometry has the most significant effect on the cone volume ( p < 0.0001), but neither affects the joint sinus measures. The results provide valuable data as well as insights for biomechanical modeling of hand movement, ergonomic design of hand-operated controls or devices, and evaluation of thumb impairments or disorders.

In-vivo three-dimensional motion analysis of the wrist during dart-throwing motion after midcarpal fusion and radioscapholunate fusion

2020 ◽  
Vol 45 (5) ◽  
pp. 501-507
Author(s):  
Lisa Reissner ◽  
Olga Politikou ◽  
Gabriella Fischer ◽  
Maurizio Calcagni
Keyword(s):  
Range Of Motion ◽  
Motion Capture ◽  
Three Dimensional ◽  
Healthy Controls ◽  
Dart Throwing ◽  
Radioscapholunate Fusion ◽  
Infrared Cameras ◽  
Basic Motion ◽  
Throwing Motion

We recorded the dart-throwing motion and basic motion tasks in patients following radioscapholunate fusion and midcarpal fusion with a three-dimensional motion capture system in vivo, using digital infrared cameras to track the movement of reflective skin markers on the hand and forearm. During the dart-throwing motion, 20 healthy volunteers showed a median range of motion of 107°. As expected, patients had significantly reduced wrist range of motion during basic motion tasks and dart-throwing motion compared with the healthy controls, except for ulnar flexion occurring in the dart-throwing motion in patients treated by midcarpal fusion and radial deviation after midcarpal fusion or radioscapholunate fusion. In addition, patients who had undergone radioscapholunate fusion had significantly reduced range of motion during dart-throwing motion compared with patients after midcarpal fusion.

A Normative Database of Thumb Circumduction In Vivo: Center of Rotation and Range of Motion

2005 ◽  
Vol 47 (3) ◽  
pp. 550-561 ◽  
Author(s):  
Xudong Zhang ◽  
Peter Braido ◽  
Sang-Wook Lee ◽  
Robert Hefner ◽  
Mark Redden
Keyword(s):  
Range Of Motion ◽  
Normative Database ◽  
Center Of Rotation

scholarly journals An In Vivo 3D Micro-CT Evaluation of Tooth Movement After the Application of Different Force Magnitudes in Rat Molar

2009 ◽  
Vol 79 (4) ◽  
pp. 703-714 ◽  
Author(s):  
Carmen Gonzales ◽  
Hitoshi Hotokezaka ◽  
Yoshinori Arai ◽  
Tadashi Ninomiya ◽  
Junya Tominaga ◽  
...  
Keyword(s):  
Tooth Movement ◽  
Three Dimensional ◽  
Element Model ◽  
Longitudinal Change ◽  
Micro Ct ◽  
Linear Measurements ◽  
Center Of Rotation ◽  
Ct Evaluation ◽  
Space Width

Abstract Objective: To investigate the precise longitudinal change in the periodontal ligament (PDL) space width and three-dimensional tooth movement with continuous-force magnitudes in living rats. Materials and Methods: Using nickel-titanium closed-coil springs for 28 days, 10-, 25-, 50-, and 100-g mesial force was applied to the maxillary left first molars. Micro-CT was taken in the same rat at 0, 1, 2, 3, 10, 14, and 28 days. The width of the PDL was measured in the pressure and tension sides from 0 to 3 days. Angular and linear measurements were used to evaluate molar position at day 0, 10, 14, and 28. The finite element model (FEM) was constructed to evaluate the initial stress distribution, molar displacement, and center of rotation of the molar. Results: The initial evaluation of PDL width showed no statistical differences among different force magnitudes. Tooth movement was registered 1 hour after force application and gradually increased with time. From day 10, greater tooth movement was observed when 10 g of force was applied. The FEM showed that the center of rotation in the molar is located in the center of five roots at the apical third of the molar roots. Conclusion: The rat's molar movement mainly consists of mesial tipping, extrusion of distal roots, intrusion of mesial root, palatal inclination, and mesial rotation. Although the initial tooth movement after the application of different force magnitudes until day 3 was not remarkably different, 10 g of force produced more tooth movement compared with heavier forces at day 28.

A Low-Dimensional Dissimilarity Analysis of Unilateral and Bilateral Stroke-Impacted Hand Trajectories

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Jay Ryan U. Roldan ◽  
Dejan Milutinović ◽  
Zhi Li ◽  
Jacob Rosen
Keyword(s):  
Multidimensional Scaling ◽  
Healthy Subject ◽  
Motion Capture ◽  
Early Phase ◽  
Three Dimensional ◽  
High Rate ◽  
Motion Capture System ◽  
Trajectory Data ◽  
The Difference ◽  
Low Dimensional

In this paper, we propose a quantitative approach based on identifying hand trajectory dissimilarities through the use of a multidimensional scaling (MDS) analysis. A high-rate motion capture system is used to gather three-dimensional (3D) trajectory data of healthy and stroke-impacted hemiparetic subjects. The mutual dissimilarity between any two trajectories is measured by the area between them. This area is used as a dissimilarity variable to create an MDS map. The map reveals a structure for measuring the difference and variability of individual trajectories and their groups. The results suggest that the recovery of hemiparetic subjects can be quantified by comparing the difference and variability of their individual MDS map points to the points from the cluster of healthy subject trajectories. Within the MDS map, we can identify fully recovered patients, those who are only functionally recovered, and those who are either in an early phase of, or are nonresponsive to the therapy.

scholarly journals Examination of Kinect’s Torso PCA Model for Planar Activities Assessment after Stroke

2019 ◽  
Vol 8 (12S2) ◽  
pp. 476-482
Keyword(s):  
Motion Capture ◽  
Upper Limb ◽  
Daily Living ◽  
Hand Movement ◽  
Three Dimensional ◽  
Principal Component ◽  
Stroke Patients ◽  
Joint Coordination ◽  
Arm Transport ◽  
Statistical Results

Compensatory movement after stroke occurred when inter-joint coordination between arm and forearm for the purpose of arm transport becomes limited due to the weaknesses of the upper limb after stroke. This limitation causes an inefficiency of hand movement to perform the activity of daily living (ADL). Previous work has shown the possibility of using Kinect to assess torso compensation in typical assessment of upper limb movement in a stroke-simulated setting using a Torso Principal Component Analysis (PCA) Model. This research extends the study into evaluating Torso PCA Model in terms of orientation angles of the torso in three dimensional when performing planar activities namely circle tracing and point-topoint tracing. The orientation angles were compared to the outcome of the measurement from a standard motion capture system and Kinect’s intrinsic chest orientation angles. Based on the statistical results, Torso PCA model is concurrently valid with the clinically accepted measures of torso orientation and can be used further to analyze torso compensation in stroke patients.

A Mobile Motion Capture System Based on Inertial Sensors and Smart Shoes

2013 ◽  
Vol 136 (1) ◽  
Author(s):  
Pyeong-Gook Jung ◽  
Sehoon Oh ◽  
Gukchan Lim ◽  
Kyoungchul Kong
Keyword(s):  
Motion Capture ◽  
Inertial Sensors ◽  
Kinematic Model ◽  
Three Dimensional ◽  
Health Condition ◽  
Body Motion ◽  
Whole Body ◽  
Motion Capture System ◽  
Outdoor Sports ◽  
Motion Capture Systems

Motion capture systems play an important role in health-care and sport-training systems. In particular, there exists a great demand on a mobile motion capture system that enables people to monitor their health condition and to practice sport postures anywhere at any time. The motion capture systems with infrared or vision cameras, however, require a special setting, which hinders their application to a mobile system. In this paper, a mobile three-dimensional motion capture system is developed based on inertial sensors and smart shoes. Sensor signals are measured and processed by a mobile computer; thus, the proposed system enables the analysis and diagnosis of postures during outdoor sports, as well as indoor activities. The measured signals are transformed into quaternion to avoid the Gimbal lock effect. In order to improve the precision of the proposed motion capture system in an open and outdoor space, a frequency-adaptive sensor fusion method and a kinematic model are utilized to construct the whole body motion in real-time. The reference point is continuously updated by smart shoes that measure the ground reaction forces.

A Motion Capture System for Hand Movement Recognition

2021 ◽  
pp. 114-121
Author(s):  
Graciela Rodríguez-Vega ◽  
Dora Aydee Rodríguez-Vega ◽  
Xiomara Penelope Zaldívar-Colado ◽  
Ulises Zaldívar-Colado ◽  
Rafael Castillo-Ortega
Keyword(s):  
Motion Capture ◽  
Hand Movement ◽  
Motion Capture System ◽  
Movement Recognition
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