Version 1
: Received: 20 November 2018 / Approved: 22 November 2018 / Online: 22 November 2018 (04:33:58 CET)
How to cite:
Klishkovskaia, T.; Aksenov, A. Improvement of the Classification Algorithms of Postures for Non-Marker Systems of Human Motion Capture. Preprints2018, 2018110533. https://doi.org/10.20944/preprints201811.0533.v1
Klishkovskaia, T.; Aksenov, A. Improvement of the Classification Algorithms of Postures for Non-Marker Systems of Human Motion Capture. Preprints 2018, 2018110533. https://doi.org/10.20944/preprints201811.0533.v1
Klishkovskaia, T.; Aksenov, A. Improvement of the Classification Algorithms of Postures for Non-Marker Systems of Human Motion Capture. Preprints2018, 2018110533. https://doi.org/10.20944/preprints201811.0533.v1
APA Style
Klishkovskaia, T., & Aksenov, A. (2018). Improvement of the Classification Algorithms of Postures for Non-Marker Systems of Human Motion Capture. Preprints. https://doi.org/10.20944/preprints201811.0533.v1
Chicago/Turabian Style
Klishkovskaia, T. and Andrey Aksenov. 2018 "Improvement of the Classification Algorithms of Postures for Non-Marker Systems of Human Motion Capture" Preprints. https://doi.org/10.20944/preprints201811.0533.v1
Abstract
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.
Biology and Life Sciences, Biology and Biotechnology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
