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

Applied Sciences

Oleg A. Markelov¹  Dmitry Kaplun²  Aleksandr Sinitca²  Andrey Aksenov³  Tatiana Klishkovskaia³  Anna Zamansky⁴ 

[1] Centre for Digital Telecommunication Technologies, Saint Petersburg Electrotechnical University “LETI”, 5 Professor Popov street, 197376 Saint Petersburg, Russia;Department of Automation and Control Processes, Saint Petersburg Electrotechnical University “LETI”, 197376 Saint Petersburg, Russia;Department of Bioengineering Systems, Saint Petersburg Electrotechnical University “LETI”, 197376 Saint Petersburg, Russia;Information Systems Department, University of Haifa, Haifa 3498838, Israel;

关键词: posture classification;    skeleton detection;    motion capture;    exercise classification;    virtual rehabilitation;   

DOI  :  10.3390/app10114028

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown