scholarly journals Methodology of 3D Scanning of Intangible Cultural Heritage—The Example of Lazgi Dance

2021 ◽  
Vol 11 (23) ◽  
pp. 11568
Author(s):  
Maria Skublewska-Paszkowska ◽  
Pawel Powroznik ◽  
Jakub Smolka ◽  
Marek Milosz ◽  
Edyta Lukasik ◽  
...  
Keyword(s):  
3D Models ◽  
Body Parts ◽  
Additional Element ◽  
Hand Model ◽  
3D Data ◽  
Scientific Papers ◽  
Culture Heritage ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Full Body

Traditional dance is one of the key elements of Intangible Culture Heritage (ICH). Many scientific papers concern analysis of dance sequences, classification and recognition of movements, making ICH data public, creating and visualising 3D models or software solutions for learning folklore dances. These works make it possible to preserve this disappearing art. The aim of this article is to propose a methodology for scanning folklore dances. The methodology was developed on the basis of capturing 3D data via an optical motion capture system with a full body Plug-in Gait model that allows for kinematic and kinetic analysis of motion sequences. An additional element of this research was the development of a hand model with which it is possible to precisely analyse the fingers, which play a significant role in many dances. The present methodology was verified on the basis of the Lazgi dance, included in the UNESCO ICH list. The obtained results of movement biomechanics for the dance sequence and the angles of the fingers indicate that it is universal and can be applied to dances that involve the upper and lower body parts, including hand movements.

scholarly journals Use of computer innovative technologies in improving the dartsmen training

2020 ◽  
Vol 26 ◽  
pp. 00061
Author(s):  
Elina Makarova ◽  
Vladislav Dubatovkin ◽  
Nataliya Berezinskaya ◽  
Lyudmila Barkhatova ◽  
Elena Oleynik
Keyword(s):  
Motion Capture ◽  
Body Parts ◽  
Visual Material ◽  
Video Cameras ◽  
Dart Throwing ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Effective Use ◽  
Near Future ◽  
Motion Capture Systems

The research is focused on studying the possibility of effective use of the dart grip system, the work of the athlete’s hand, to prepare the dartsman for competitions using the MOSAR complex. The experiment uses optical motion capture systems, a set of video cameras, led parameter sensors, and devices that allow to record the movement of body parts and a dart. This method of training and controlling dart throwing can serve as educational and visual material for training future athletes. The use of such motion capture systems in the near future may become one of the main aspects of training, both beginners and professionals, in many sports.

Embodiment in Electronic Dance Music: Effects of musical content and structure on body movement

2018 ◽  
Vol 24 (2) ◽  
pp. 186-205 ◽  
Author(s):  
Birgitta Burger ◽  
Petri Toiviainen
Keyword(s):  
Motion Capture ◽  
Body Movement ◽  
Personality Characteristics ◽  
Body Parts ◽  
Dance Music ◽  
Electronic Dance Music ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Hands And Feet ◽  
Music Effects

Electronic dance music (EDM) is music produced with the foremost aim to make people move. While research has revealed relationships between movement features and, for example, musical, emotional, or personality characteristics, systematic investigations of genre differences and specifically of EDM are rather rare. This article aims at offering insights into the embodiment of EDM from three different angles: first from a genre-comparison perspective, then by comparing different EDM stimuli with each other, and finally by investigating embodiments in one specific EDM stimulus. Sixty participants moved freely to 16 stimuli of four different genres (EDM, Latin, Funk, Jazz – four stimuli/genre) while being recorded with an optical motion capture system. Subsequently, a set of movement features was extracted from the motion capture data. Results indicate that participants moved with significantly higher acceleration of torso, head, hands, and feet and more overall movement to the EDM stimuli than to the other genres. Between EDM stimuli, several significant correlations were found, suggesting an increase in acceleration of different body parts with clearer and more percussive rhythmic structures and brighter sounds. Within one EDM stimulus, participants’ movements differed in several movement features distinguishing the break from surrounding sections, showing less acceleration, as well as less overall movement and rotational speed during the break. These analyses propose different ways of studying EDM and indicate distinctive characteristics of EDM embodiment.

scholarly journals Validation of an IMU Suit for Military-Based Tasks

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4280 ◽  
Author(s):  
Matthew P. Mavor ◽  
Gwyneth B. Ross ◽  
Allison L. Clouthier ◽  
Thomas Karakolis ◽  
Ryan B. Graham
Keyword(s):  
Motion Capture ◽  
Principal Component ◽  
Load Carriage ◽  
Body Motion ◽  
Promising Alternative ◽  
Flexion Extension ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Optical Markers ◽  
Full Body

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.

scholarly journals Modeling and Animating Virtual Humans for Real-Time Applications

2007 ◽  
Vol 6 (4) ◽  
pp. 11-20 ◽  
Author(s):  
Frank Hülsken ◽  
Christian Eckes ◽  
Roland Kuck ◽  
Jörg Unterberg ◽  
Sophie J�rg
Keyword(s):  
Real Time ◽  
Motion Capture ◽  
3D Models ◽  
Rigid Bodies ◽  
Whole Body ◽  
Virtual Character ◽  
Real People ◽  
Optical Motion ◽  
High Resolution Models ◽  
Optical Motion Capture

We report on the workflow for the creation of realistic virtual anthropomorphic characters. 3D-models of human heads have been reconstructed from real people by following a structured light approach to 3D-reconstruction. We describe how these high-resolution models have been simplified and articulated with blend shape and mesh skinning techniques to ensure real-time animation. The full-body models have been created manually based on photographs. We present a system for capturing whole body motions, including the fingers, based on an optical motion capture system with 6 DOF rigid bodies and cybergloves. The motion capture data was processed in one system, mapped to a virtual character and visualized in real-time. We developed tools and methods for quick post processing. To demonstrate the viability of our system, we captured a library consisting of more than 90 gestures.

scholarly journals Determination of Body Parts in Holstein Friesian Cows Comparing Neural Networks and k Nearest Neighbour Classification

Animals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 50
Author(s):  
Jennifer Salau ◽  
Jan Henning Haas ◽  
Wolfgang Junge ◽  
Georg Thaller
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Body Parts ◽  
Nearest Neighbour ◽  
Data Set ◽  
3D Data ◽  
Holstein Friesian ◽  
Knn Classification ◽  
Friesian Cows

Machine learning methods have become increasingly important in animal science, and the success of an automated application using machine learning often depends on the right choice of method for the respective problem and data set. The recognition of objects in 3D data is still a widely studied topic and especially challenging when it comes to the partition of objects into predefined segments. In this study, two machine learning approaches were utilized for the recognition of body parts of dairy cows from 3D point clouds, i.e., sets of data points in space. The low cost off-the-shelf depth sensor Microsoft Kinect V1 has been used in various studies related to dairy cows. The 3D data were gathered from a multi-Kinect recording unit which was designed to record Holstein Friesian cows from both sides in free walking from three different camera positions. For the determination of the body parts head, rump, back, legs and udder, five properties of the pixels in the depth maps (row index, column index, depth value, variance, mean curvature) were used as features in the training data set. For each camera positions, a k nearest neighbour classifier and a neural network were trained and compared afterwards. Both methods showed small Hamming losses (between 0.007 and 0.027 for k nearest neighbour (kNN) classification and between 0.045 and 0.079 for neural networks) and could be considered successful regarding the classification of pixel to body parts. However, the kNN classifier was superior, reaching overall accuracies 0.888 to 0.976 varying with the camera position. Precision and recall values associated with individual body parts ranged from 0.84 to 1 and from 0.83 to 1, respectively. Once trained, kNN classification is at runtime prone to higher costs in terms of computational time and memory compared to the neural networks. The cost vs. accuracy ratio for each methodology needs to be taken into account in the decision of which method should be implemented in the application.

scholarly journals An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2869
Author(s):  
Jiaen Wu ◽  
Kiran Kuruvithadam ◽  
Alessandro Schaer ◽  
Richie Stoneham ◽  
George Chatzipirpiridis ◽  
...  
Keyword(s):  
Real Time ◽  
Inertial Sensor ◽  
Neurological Diseases ◽  
Principal Component ◽  
Sampling Frequency ◽  
Powerful Analytical Tool ◽  
Optical Motion ◽  
Analysis System ◽  
Optical Motion Capture ◽  
Gait Monitoring

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on technologies for the assessment of gait characteristics, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigenanalysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics.

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