scholarly journals Dance Pose Identification from Motion Capture Data: A Comparison of Classifiers

Technologies ◽  
2018 ◽  
Vol 6 (1) ◽  
pp. 31 ◽  
Author(s):  
Eftychios Protopapadakis ◽  
Athanasios Voulodimos ◽  
Anastasios Doulamis ◽  
Stephanos Camarinopoulos ◽  
Nikolaos Doulamis ◽  
...  
Keyword(s):  
Motion Capture ◽  
Motion Capture Data ◽  
Pose Identification

Correlational Methods for Analysis of Dance Movements

2011 ◽  
Vol 29 (supplement) ◽  
pp. 283-304 ◽  
Author(s):  
Timothy R. Brick ◽  
Steven M. Boker
Keyword(s):  
Motion Capture ◽  
Mirror Symmetry ◽  
Cross Correlation ◽  
The Other ◽  
Free Form ◽  
Motion Capture Data ◽  
Complex Interactions ◽  
Dance Movements ◽  
Time Lagged ◽  
Insight Into

Among the qualities that distinguish dance from other types of human behavior and interaction are the creation and breaking of synchrony and symmetry. The combination of symmetry and synchrony can provide complex interactions. For example, two dancers might make very different movements, slowing each time the other sped up: a mirror symmetry of velocity. Examining patterns of synchrony and symmetry can provide insight into both the artistic nature of the dance, and the nature of the perceptions and responses of the dancers. However, such complex symmetries are often difficult to quantify. This paper presents three methods – Generalized Local Linear Approximation, Time-lagged Autocorrelation, and Windowed Cross-correlation – for the exploration of symmetry and synchrony in motion-capture data as is it applied to dance and illustrate these with examples from a study of free-form dance. Combined, these techniques provide powerful tools for the examination of the structure of symmetry and synchrony in dance.

Perceptually motivated LSPIHT for motion capture data compression

2015 ◽  
Vol 51 ◽  
pp. 1-7 ◽  
Author(s):  
Irene Cheng ◽  
Amirhossein Firouzmanesh ◽  
Anup Basu
Keyword(s):  
Data Compression ◽  
Motion Capture ◽  
Motion Capture Data

Keyframe Extraction for Human Motion Capture Data Based on Joint Kernel Sparse Representation

2017 ◽  
Vol 64 (2) ◽  
pp. 1589-1599 ◽  
Author(s):  
Guiyu Xia ◽  
Huaijiang Sun ◽  
Xiaoqing Niu ◽  
Guoqing Zhang ◽  
Lei Feng
Keyword(s):  
Sparse Representation ◽  
Motion Capture ◽  
Human Motion ◽  
Motion Capture Data ◽  
Human Motion Capture ◽  
Keyframe Extraction

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

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