Performance analysis of a generalized motion capture system using microsoft kinect 2.0

2017 ◽  
Vol 38 ◽  
pp. 265-280 ◽  
Author(s):  
Alessandro Napoli ◽  
Stephen Glass ◽  
Christian Ward ◽  
Carole Tucker ◽  
Iyad Obeid
Keyword(s):  
Performance Analysis ◽  
Motion Capture ◽  
Microsoft Kinect ◽  
Motion Capture System

DEVELOPMENT OF A MOTION CAPTURE SYSTEM USING KINECT

2015 ◽  
Vol 76 (11) ◽  
Author(s):  
Katherina Bujang ◽  
Ahmad Faiz Ahmad Nazri ◽  
Ahmad Fidaudin Ahmad Azam ◽  
Jamaluddin Mahmud
Keyword(s):  
Motion Capture ◽  
Low Cost ◽  
Microsoft Kinect ◽  
Motion Capture Data ◽  
Motion Capture System ◽  
Interface Motion ◽  
Measurement Analysis ◽  
Potential Alternative ◽  
Repeatability And Reproducibility ◽  
Data Tracking

Microsoft Kinect has been identified as a potential alternative tool in the field of motion capture due to its simplicity and low cost. To date, the application and potential of Microsoft Kinect has been vigorously explored especially for entertainment and gaming purposes. However, its motion capture capability in terms of repeatability and reproducibility is still not well addressed. Therefore, this study aims to explore and develop a motion capture system using Microsoft Kinect; focusing on developing the interface, motion capture protocol as well as measurement analysis. The work is divided into several stages which include installation (Microsoft Kinect and MATLAB); parameters and experimental setup, interface development; protocols development; motion capture; data tracking and measurement analysis. The results are promising, where the variances are found to be less than 1% for both repeatability and reproducibility analysis. This proves that the current study is significant and the gained knowledge could contribute

scholarly journals Mejora de la ubicación del centro articular de un sistema de captura de movimiento sin marcadores a través de un método de calibración funcional: estudio piloto

AJEA ◽  
2020 ◽  
Author(s):  
Magalí Sganga ◽  
Lucas Eduardo Ritacco ◽  
Emiliano Pablo Ravera
Keyword(s):  
Motion Capture ◽  
Gold Standard ◽  
Estimation Method ◽  
Microsoft Kinect ◽  
Motion Capture System ◽  
Para Determinar ◽  
Centre Of Rotation ◽  
Common Shape ◽  
Rotation Estimation

El análisis biomecánico es una herramienta para evaluar movimientos patológicos y su rehabilitación. Esta investigación estudia la factibilidad de mejoras en la ubicación del centro articular de cadera en un Sistema de Captura de Movimiento sin Marcadores a través de la aplicación de un método de calibración funcional. Se analizó el movimiento de una joven mujer sin impedimentos físicos. Se adquirió simultáneamente información del sistema gold standard (Motion Capture System, MOCAP) y una alternativa de bajo costo (Microsoft Kinect). Se grabaron adquisiciones estáticas y dinámicas. A partir de la información obtenida con Kinect, se crearon cinco marcadores virtuales para cada pierna y se introdujeron en optimal common shape technique y en symmetrical centre of rotation estimation method para determinar los centros articulares de ambas caderas. Los resultados mostraron mejora en la ubicación del centro de rotación cuando fueron comparados con la información de MOCAP. En conclusión, este enfoque podría mejorar la información obtenida con Kinect y demostró ser un método factible deaplicación en el campo de la rehabilitación.

scholarly journals Influence of a Marker-Based Motion Capture System on the Performance of Microsoft Kinect v2 Skeleton Algorithm

2019 ◽  
Vol 19 (1) ◽  
pp. 171-179 ◽  
Author(s):  
Mreza Naeemabadi ◽  
Birthe Dinesen ◽  
Ole Kaeseler Andersen ◽  
John Hansen
Keyword(s):  
Motion Capture ◽  
Microsoft Kinect ◽  
Motion Capture System ◽  
Kinect V2

scholarly journals Investigating the impact of a motion capture system on Microsoft Kinect v2 recordings: A caution for using the technologies together

PLoS ONE ◽  
2018 ◽  
Vol 13 (9) ◽  
pp. e0204052 ◽  
Author(s):  
MReza Naeemabadi ◽  
Birthe Dinesen ◽  
Ole Kæseler Andersen ◽  
John Hansen
Keyword(s):  
Motion Capture ◽  
Microsoft Kinect ◽  
Motion Capture System ◽  
Kinect V2 ◽  
The Impact

Gender differences in the Achilles tendon load during the fencing lunge

2014 ◽  
Vol 6 (3) ◽  
Author(s):  
Jonathan Kenneth Sinclair ◽  
Lindsay Bottoms
Keyword(s):  
Gender Differences ◽  
Achilles Tendon ◽  
Motion Capture ◽  
Tendon Injury ◽  
Force Platform ◽  
Motion Capture System ◽  
3D Motion ◽  
Loading Rates ◽  
Tendon Pathology ◽  
3D Motion Capture

AbstractRecent epidemiological analyses in fencing have shown that injuries and pain linked specifically to fencing training/competition were evident in 92.8% of fencers. Specifically the prevalence of Achilles tendon pathology has increased substantially in recent years, and males have been identified as being at greater risk of Achilles tendon injury compared to their female counterparts. This study aimed to examine gender differences in Achilles tendon loading during the fencing lunge.Achilles tendon load was obtained from eight male and eight female club level epee fencers using a 3D motion capture system and force platform information as they completed simulated lunges. Independent t-tests were performed on the data to determine whether differences existed.The results show that males were associated with significantly greater Achilles tendon loading rates in comparison to females.This suggests that male fencers may be at greater risk from Achilles tendon pathology as a function of fencing training/ competition.

Magnetic motion capture system using LC resonant magnetic marker composed of Ni–Zn ferrite core

2006 ◽  
Vol 99 (8) ◽  
pp. 08B312 ◽  
Author(s):  
S. Hashi ◽  
M. Toyoda ◽  
M. Ohya ◽  
Y. Okazaki ◽  
S. Yabukami ◽  
...  
Keyword(s):  
Motion Capture ◽  
Motion Capture System ◽  
Ferrite Core ◽  
Zn Ferrite ◽  
Magnetic Marker

scholarly journals Modeling and evaluating beat gestures for social robots

2021 ◽  
Author(s):  
Unai Zabala ◽  
Igor Rodriguez ◽  
José María Martínez-Otzeta ◽  
Elena Lazkano
Keyword(s):  
Motion Capture ◽  
Motion Capture System ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Desirable Feature ◽  
Quantitative Results ◽  
Path Lengths ◽  
End Effectors ◽  
The One ◽  
Made In

AbstractNatural gestures are a desirable feature for a humanoid robot, as they are presumed to elicit a more comfortable interaction in people. With this aim in mind, we present in this paper a system to develop a natural talking gesture generation behavior. A Generative Adversarial Network (GAN) produces novel beat gestures from the data captured from recordings of human talking. The data is obtained without the need for any kind of wearable, as a motion capture system properly estimates the position of the limbs/joints involved in human expressive talking behavior. After testing in a Pepper robot, it is shown that the system is able to generate natural gestures during large talking periods without becoming repetitive. This approach is computationally more demanding than previous work, therefore a comparison is made in order to evaluate the improvements. This comparison is made by calculating some common measures about the end effectors’ trajectories (jerk and path lengths) and complemented by the Fréchet Gesture Distance (FGD) that aims to measure the fidelity of the generated gestures with respect to the provided ones. Results show that the described system is able to learn natural gestures just by observation and improves the one developed with a simpler motion capture system. The quantitative results are sustained by questionnaire based human evaluation.

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