Monday , July 16 2018

Clustering-based Human Locomotion Parameters for
Motion Type Classification

Ramona LUCA
Institute of Computer Science, Romanian Academy Iasi Branch,
2 T. Codrescu Street, Iassy 700481, Romania

Abstract: The paper proposes a classification method of human locomotion types from video sequences based on motion parameters clustering. A set of motion parameters is semi-automatically extracted from training video sequences that contain three different types of movement: walking, jogging and running. The motion parameters (postural, frequential, and cinematic) are stored in a relational database and statistic parameters such as minimum, maximum, average and standard deviation are computed. Then a K-means clustering is applied on all the statistic parameters combinations and the results are evaluated using the purity measure to determine most significant parameters to be used in classification. Because the number of training video sequences is reduced, the proposed method may be used as a model of classification only. The automatic determination of movement parameters will increase the data collection size and real testing of the classification method.

Keywords: human locomotion, video analysis, ontology, K-means.

>>Full text
Ramona LUCA,
Clustering-based Human Locomotion Parameters for Motion Type Classification, Studies in Informatics and Control, ISSN 1220-1766, vol. 25(3), pp. 353-362, 2016.

1. Introduction

Applications in various fields such as video games [4], dance education [18], [19], sports [3], medicine (mainly rehabilitation) [11], [21], self-driving cars and robotics [5], [6], [10], and security [1] require an improved understanding and representation of human locomotion.

Various types of human activities are categorized in: gestures, actions, interactions, and group activities [1]. Gestures are elementary movements of a body part, for example “waiving hand”. Complex activities composed from multiple gestures organized temporally are actions. Some examples of human actions are walking, running, jumping. Interaction is a human activity that involves two or more persons and/or objects. Group activities are activities that involve a group of persons and/or objects. The movements of body parts at joints are mainly rotational and take place in three reference planes: sagittal, frontal and transversal. These movements are flexion and extension (frontal axis), abduction and adduction (sagittal axis), medial and lateral (transverse axis).

Walking is a human action in which one foot touchdown is followed by the touchdown of the other foot in a continuous pattern [3]. One of the differences between walking and running is that in the running cycle for a period of time both feet are in the air [7]. Jogging is a form of running at a slower pace [15].

The type of human locomotion may be indicative for the type of activity, intentions, attitudes, circumstances perceived – e.g., dangers, and mood of the person. Consequently, the fast detection of the type and specifics of the locomotion may indicate dangerous situations, among others.

The analysis of the human movement is qualitative and quantitative. While the qualitative analysis of human movement is a non-numerically analysis and describes human actions as patterns, the quantitative analysis requires a large amount of measurements of the parameters which describe the human action and a computer to perform numerical calculation [3].

Human locomotion represents a complex kinematics, with all the body parts participating, involving a large degree of freedom. In [17], [18] and [19] the authors have proposed semantics of the human movement. A semantic representation of human motion in combination with a motion recognition algorithm for digital human simulation is presented in [16].

The combined use of fuzzy logic and complex dynamics has been advocated by several groups, for example [8], [9], [22], including in representing and classification of human movements [9], [21].

This article follows the track established by the papers [12], [13], [23]. Compared to those preliminary papers and to the literature, this article brings the following contributions: a complete statistical characterization of the movement, with the related database, including all angles between the relevant body parts, the derivation of the membership functions based on the statistics, the automatic clustering of the movement sequences in view of objective determination of the membership functions related to a more detailed analysis of the dynamics, and detection of abnormal movements applications, with relevance in the medical and security fields.

Throughout the paper, the main parameters of the human locomotion, as established in the literature, for example in [24], are taken into account. This study follows the same path by analyzing basic human locomotion and proposing a fuzzy ontology of it, based on video analysis.

The paper organization is as follows. Section 2 presents the video database and the method used to determine the parameters of the locomotion. In Section 3 a K-means algorithm was applied on extracted parameters. In the fourth section are presented the experimental results and the last section concludes the paper.


  1. AGGARWAL, J. K., M. S. RYOO, Human Activity Analysis: A Review, ACM Computing Surveys (CSUR), vol. 43(3), April 2011.
  2. ARTHUR, D., S. VASSILVITSKII, K-means++: The Advantages of Careful Seeding, SODA ‘07: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 2007, pp. 1027-1035.
  1. BARTLETT, R., Introduction to Sports Biomechanics: Analysing Human Movement Patterns, 2nd published in the Taylor & Francis e-Library, 2007.
  2. BRUDERLIN, A., T. W. CALVERT, Goal-directed, Dynamic Animation of Human Walking. Computer Graphics, vol. 23(3), July 1989.
  3. COLLINS, S., A. RUINA, A Bipedal Walking Robot with Efficient and Human-Like Gait, ICRA, May 2005.
  4. COLLINS, S., RUINA, A., TEDRAKE, R., WISSE, M., Efficient Bipedal Robots based on Passive-Dynamic Walkers. Science, vol. 307, 2005, pp. 1082-1085.
  5. CROSS, R., Standing, Walking, Running and Jumping on a Force plate, American J. of Physics, vol. 67(4), 1999, pp. 304-309.
  6. DE SALLES, D. C., N. GONCALVES, C. ARMANDO, L. G. MARUJO, Using Fuzzy Logic to Implement Decision Policies in System Dynamics Models. Exp. Sys. with Applications, vol. 55, 2016, pp. 172-183.
  7. JI, X., J. ZHANG, Y. HU, B. RAN, Pedestrian Movement Analysis in Transfer Station Corridor: Velocity-based and Acceleration-based. Physica A-Statistical Mechanics and Its Applications, vol. 450, 2016, pp. 416-434.
  8. Kim, Y. T., H. C. CHO, J. Y. SEO, H. Y. JEON, G. J. KLIR, Intelligent Path Planning of Two Cooperating Robots based on Fuzzy Logic, Intl. J. General Systems, vol. 31(4), 2002, pp. 359-376.
  9. KNUDSON, D., Fundamentals of Biomechanics, Springer, 2007.
  10. LUCA, R., H.-N. TEODORESCU, S. I. BEJINARIU, An Ontology of Human Walk for Autonomous Systems, Proc. of 18th International Conference on Systems Theory, Control and Computing, Sinaia, Romania, 2014, pp. 494-499.
  11. LUCA, R., Statistical Analysis of Some Parameters Describing Human Locomotion, 5th IEEE Intl. Conference on E-Health and Bioengineering, EHB 2015, Iasi, Romania, 2015, pp. 1-4.
  12. MANNING, C. D., P. RAGHAVAN, H. SCHÜTZE, Introduction to Information Retrieval, Cambridge University Press. 2008, pp 356-358, IR-book/pdf/16flat.pdf
  13. Merriam Webster Dictionary, jogging, last accessed on 01.06.2016.
  14. PINTZOSA, G., N. NIKOLAKISA, K. ALEXOPOULOSA, G. CHRYSSOLOURIS, Motion Parameters Identification for the Authoring of Manual Tasks in Digital Human Simulations: An Approach using Semantic Modelling, Elsevier, 48th CIRP Conference on Manufacturing systems – CIRP CMS, 2015.
  15. RODRIGUEZ, N. D., R. WIKSTRÖM, J. LILIUS, M. PEGALAJAR CUÉLLAR, M., DELGADO, C. FLORES, Understanding Movement and Interaction: An Ontology for Kinect-Based 3D Depth Sensors, Ubiquitous Computing and Ambient Intelligence. Context-Awareness and Context-Driven Interaction, Lecture Notes in Computer Science, vol. 8276, 2013, 254-261.
  16. SAAD, S., S. MAHMOUDI, P. MANNEBACK, P., Semantic Analysis of Human Movements in Videos, Proc. I-Semantics 2012, 8th Int. Conf. on Semantic Systems, Graz, Austria, 2012, pp. 141-148.
  17. SAAD, S., D. DE BEUL, S. MAHMOUDI, P. MANNEBACK, An Ontology for Video Human Movement Representation based on Benesh Notation, International Conference on Multimedia Computing and Systems (ICMCS), 2012.
  18. SCHULDT, C., I. LAPTEV, B. CAPUTO, Recognizing Human Actions: A Local SVM Approach, Proceedings of the Pattern Recognition, 17th International Conference on (ICPR’04), vol. 3, 2004, pp. 32-36,, last accessed on 01.06.2016.
  19. Teodorescu, H.-N., D. Mlynek, et al., Analysis of Chaotic Movements and Fuzzy Assessment of Hands Tremor in Rehabilitation. Proceedings KES ’98, Knowledge-Based Intelligent Electronic Systems, 1998. Second International Conference on, Adelaide, Australia, vol. 3, 1998, pp. 340-345.
  20. TEODORESCU, H. N., A. KANDEL, M. SCHNEIDER, M., Fuzzy Modeling and Dynamics, Fuzzy Sets and Systems, vol. 106(1), 1999, pp. 1-2.
  21. TEODORESCU, H.-N., Taxonomy and Procedures for Adaptive Autonomous Systems in Crowded Hybrid Environments, Proceedings of 6th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2014, Bucharest, Romania.
  22. XIANG, Y., J. S. ARORA, K. ABDEL-MALEK, Physics-based Modeling and Simulation of Human Walking: A Review of Optimization-based and Other Approaches. Structural and Multi-disciplinary Optimization, vol. 42(1), 2010, pp. 1-23.