scholarly journals Improvement of the Classification Algorithms of Postures for Non-Marker Systems of Human Motion Capture

2018 ◽  
Author(s):  
Tatiana Klishkovskaia ◽  
Andrey Aksenov
Keyword(s):  
Motion Capture ◽  
Remote Monitoring ◽  
Total Error ◽  
Rapid Development ◽  
Human Motion ◽  
Classification Algorithms ◽  
Human Motion Capture ◽  
Two Parameters ◽  
Subject Performance

The rapid development of algorithms for skeleton detection with relatively inexpensive contactless systems and cameras opens the possibility of virtual exercise therapy for patients with different complications. However, evaluation and confirmation of posture classifications is still needed. The purpose of this study was therefore to find the most accurate algorithm for automatic classification of human exercise movement. A Kinect V2 with 25 joints identification was used to record movements for data analysis. A total of 10 subjects volunteered for this study. Four algorithms were tested for the classification of different postures in Matlab. These were based on: total error of vector lengths, total error of angles, multiplication of these two parameters and simultaneous analysis of the first and second parameters. A base of 13 exercises was then created to test the recognition of postures by the algorithm, and to analyse subject performance. The best results for posture classification was shown by the second algorithm with an accuracy of 94.9%. The average correctness of exercises among the 10 participants was 94.2% (SD1.8%). The algorithms tested in this study therefore proved to be effective and could potentially form the basis for developing a system for remote monitoring of rehabilitation involving exercise.

scholarly journals Development of Classification Algorithms for the Detection of Postures Using Non-Marker-Based Motion Capture Systems

2020 ◽  
Vol 10 (11) ◽  
pp. 4028
Author(s):  
Tatiana Klishkovskaia ◽  
Andrey Aksenov ◽  
Aleksandr Sinitca ◽  
Anna Zamansky ◽  
Oleg A. Markelov ◽  
...  
Keyword(s):  
Low Cost ◽  
Total Error ◽  
Rapid Development ◽  
Simple Algorithm ◽  
Classification Algorithms ◽  
Health And Wellbeing ◽  
Cost System ◽  
Two Parameters ◽  
Subject Performance

The rapid development of algorithms for skeletal postural detection with relatively inexpensive contactless systems and cameras opens up the possibility of monitoring and assessing the health and wellbeing of humans. However, the evaluation and confirmation of posture classifications are still needed. The purpose of this study was therefore to develop a simple algorithm for the automatic classification of human posture detection. The most affordable solution for this project was through using a Kinect V2, enabling the identification of 25 joints, so as to record movements and postures for data analysis. A total of 10 subjects volunteered for this study. Three algorithms were developed for the classification of different postures in Matlab. These were based on a total error of vector lengths, a total error of angles, multiplication of these two parameters and the simultaneous analysis of the first and second parameters. A base of 13 exercises was then created to test the recognition of postures by the algorithm and analyze subject performance. The best results for posture classification were shown by the second algorithm, with an accuracy of 94.9%. The average degree of correctness of the exercises among the 10 participants was 94.2% (SD1.8%). It was shown that the proposed algorithms provide the same accuracy as that obtained from machine learning-based algorithms and algorithms with neural networks, but have less computational complexity and do not need resources for training. The algorithms developed and evaluated in this study have demonstrated a reasonable level of accuracy, and could potentially form the basis for developing a low-cost system for the remote monitoring of humans.

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

Sensor network oriented human motion capture via wearable intelligent system

2021 ◽  
Author(s):  
Sen Qiu ◽  
Hongkai Zhao ◽  
Nan Jiang ◽  
Donghui Wu ◽  
Guangcai Song ◽  
...  
Keyword(s):  
Sensor Network ◽  
Motion Capture ◽  
Intelligent System ◽  
Human Motion ◽  
Human Motion Capture

Human Motion Capture Driven by Orientation Measurements

1999 ◽  
Vol 8 (2) ◽  
pp. 187-203 ◽  
Author(s):  
Tom Molet ◽  
Ronan Boulic ◽  
Daniel Thalmann
Keyword(s):  
Motion Capture ◽  
Human Motion ◽  
High Rate ◽  
Position Measurement ◽  
Human Motion Capture ◽  
Fast Tracking ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Tracking Object ◽  
Capture Techniques

Motion-capture techniques are rarely based on orientation measurements for two main reasons: (1) optical motion-capture systems are designed for tracking object position rather than their orientation (which can be deduced from several trackers), (2) known animation techniques, like inverse kinematics or geometric algorithms, require position targets constantly, but orientation inputs only occasionally. We propose a complete human motion-capture technique based essentially on orientation measurements. The position measurement is used only for recovering the global position of the performer. This method allows fast tracking of human gestures for interactive applications as well as high rate recording. Several motion-capture optimizations, including the multijoint technique, improve the posture realism. This work is well suited for magnetic-based systems that rely more on orientation registration (in our environment) than position measurements that necessitate difficult system calibration.

Sign in / Sign up

Export Citation Format

Share Document