Other Articles by Author(s)

Tomasz Hachaj
Marcin Piekarczyk

Author(s) and WSEAS

Tomasz Hachaj
Marcin Piekarczyk

WSEAS Transactions on Computers

Print ISSN: 1109-2750
E-ISSN: 2224-2872

Volume 18, 2019

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

Advanced Human Motion Trajectories Comparison using Dynamic Path Warping approach

AUTHORS: Tomasz Hachaj, Marcin Piekarczyk

Download as PDF

ABSTRACT: This paper describes and evaluates advanced aligning and comparison method dedicated for human motion trajectory analysis. It utilizes Dynamic Time Warping approach and can be applied for relatively long (30 seconds or longer) and complex motion paths. In contrary to other human motion analysis techniques we do not have restriction on motion direction, we use only kinematic data and we are able to compare any foot trajectory no matter how many rotations take place during the motion. As the result an algorithm outputs set of vectors along motion path that corresponds to beginning and end positions of footsteps. The left and right foot is analyzed separately. The difference of two motion paths can be expressed in any DTW-based feature namely minimal, maximal, median, mean and normalized DTW based distance. We have evaluated our method on karate kata dataset that contains four types of motion sequences performed by two black belt Shorin-Ryu karate masters with more than 20 years of experience. The evaluation of our method assured us that our approach can be easily applied for aligning and comparison of any other motion class described by two dimensional motion trajectories. The method can be applied for example in sport or physical therapy exercises data evaluation and it is invariant to body proportion and motion execution speed.

KEYWORDS: Signal processing, Human motion analysis, Path analysis, Dynamic Time Warping, Karate kata


[ 1] K. Adistambha, C. H. Ritz, and I. S. Burnett. Motion classification using dynamic time warping. 2008 IEEE 10th Workshop on Multimedia Signal Processing, pages 622–627, 2008.

[2] A. Aristidou, D. Cohen-Or, J.K. Hodgins, and A. Shamir. Self-similarity analysis for motion capture cleaning. Computer Graphics Forum, 37(2):297–309, 2018.

[3] M. J. Black and A. D. Jepson. Recognizing temporal trajectories using the condensation algorithm. Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition, pages 16–21, 1998.

[4] Jaron Blackburn and Eraldo Ribeiro. Human motion recognition using isomap and dynamic time warping. Proceedings of the 2Nd Conference on Human Motion: Understanding, Modeling, Capture and Animation, pages 285–298, 2007.

[5] Y. Chen, G. Chen, K. Chen, and B. C. Ooi. Efficient processing of warping time series join of motion capture data. 2009 IEEE 25th International Conference on Data Engineering, pages 1048–1059, 2009.

[6] O. Cigdem, T. De Laet, and J. De Schutter. Classical and subsequence dynamic time warping for recognition of rigid body motion trajectories. 2013 9th Asian Control Conference (ASCC), pages 1–6, 2013.

[7] A. Gupta, J. He, J. Martinez, J. J. Little, and R. J. Woodham. Efficient video-based retrieval of human motion with flexible alignment. 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1–9, 2016.

[8] Tomasz Hachaj and Marek Ogiela. Heuristic method for calculation of human body translation using data from inertial motion capture costume. International Journal of Electrical and Electronic Engineering & Telecommunications, 7:26–29, 2018.

[9] Tomasz Hachaj, Marek R. Ogiela, and Katarzyna Koptyra. Application of assistive computer vision methods to oyama karate techniques recognition. Symmetry, 7:1670– 1698, 2015.

[10] Tomasz Hachaj, Marcin Piekarczyk, and Marek R Ogiela. R language source code and example data for this paper, 2018. https: //github.com/browarsoftware/ DTWHumanMotionPathAnalysis.

[11] Tomasz Hachaj, Marcin Piekarczyk, and Marek R. Ogiela. Signal processing methods in human motion path analysis: a use case for karate kata. EECS 2018 conference proceedings, in press, 2019.

[12] Eugene Hsu, Marco da Silva, and Jovan Popovic. Comparing the difference be- ´ tween front-leg and back-leg round-house kicks attacking movement abilities in taekwondo. In Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation (SCA ’07). Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, pages 45–52, 2007.

[13] Mingqin Liu, Xiaoguang Zhang, and Guiyun Xu. Continuous motion classification and segmentation based on improved dynamic time warping algorithm. International Journal of Pattern Recognition and Artificial Intelligence, 32:59–66, 2018.

[14] Marion Morel, Richard Kulpa, Anthony Sorel, Catherine Achard, and Severine Dubuisson. Au- ´ tomatic and generic evaluation of spatial and temporal errors in sport motions. In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pages 542–551, 2016.

[15] A.S. Soares and Jr A.L. Apolinario. Real- ´ time 3d gesture recognition using dynamic time warping and simplification methods. Journal of WSCG, 25:59–66, 2017.

[16] Adam Switonski, Agnieszka Michalczuk, Henryk Josinski, Andrzej Polanski, and Konrad Wojciechowski. Dynamic time warping in gait classification of motion capture data. World Academy of Science, Engineering and Technology International Journal of Computer and Information Engineering, 6:1289–1294, 2012.

[17] Aras Yurtman and Billur Barshan. Automated evaluation of physical therapy exercises using multi-template dynamic time warping on wearable sensor signals. Computer Methods and Programs in Biomedicine, 117:189–207, 2014.

[18] F. Zhou and F. De la Torre. Generalized time warping for multi-modal alignment of human motion. 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 1282–1289, 2012.

[19] Feng Zhou and Torre Fernando. Canonical time warping for alignment of human behavior. Advances in Neural Information Processing Systems 22, pages 2286–2294, 2009.

WSEAS Transactions on Computers, ISSN / E-ISSN: 1109-2750 / 2224-2872, Volume 18, 2019, Art. #4, pp. 31-45

Copyright © 2018 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0

Bulletin Board


The editorial board is accepting papers.

WSEAS Main Site