The analysis of motion similarity, particularly human motion similarity, is needed in different areas of study: motion blending, where new motions are generated from previous ones and they are intended to be as realistic as possible; motion retrieval, where indexing, searching and retrieving a particular movement from databases of motions capture data is required; and performance analysis of dancers and athletes, where the examination of recorded dances and exercises allows to track the evolution of characteristics to be analyzed, such as strength, speed, etc.
This dissertation offers a framework for measuring human motion similarity by modeling human motion as a set of 3-dimensional curves represented as orthogonal changes of direction, and then by using a human movement notation that describes such human motion in a way that temporal and spatial analysis of human motion similarity can be achieved.
For purposes of evaluating the feasibility of this approach, a set of baseline key rehabilitation exercises has been chosen and tested using our implementation. Motion capture sessions for the key rehabilitation exercises provided the data for the experiments. FastDTW, an algorithm for measuring similarity between two temporal sequences, was used to compare the result of our implementation.
One of the main contributions of this proposal is the modeling of human motion as chain codes, or strings composed of characters from a finite alphabet. This model of human motion allows the use of string matching algorithms, sequence alignment algorithms, and statistical analysis approaches to achieve the analysis of similarity. Another contribution is the ability of spatial and temporal analysis due to the proposed model and description of the human motion. This technique takes data from a motion capture session, regardless the technique used in those sessions. The only requirement is that data must contain timed three-dimensional positions of the markers used, and information regarding the part of the body those markers were set during the motion capture session. Finally, based on the description of the key rehabilitation exercises, we suggested enhancements for LABANotation such purpose.
Many thanks to the National Council of Science and Technology -Consejo Nacional de Ciencia y Tecnología (CONACyT), México-
First I would like to thank my parents. It is amazing how easier is to take the burden of life when they encourage you to do so. Second, all my love and gratitude to my wife. I am so blessed that I have had her as a mate for this journey, and for the remaining ones.
I want to thank so much my advisory committee members: Dr. Boult, Dr. Carollo, and Dr. Lewis. Their patience and support was amazing.
Patricia Rea is another person I want to thank. Her patience and guidelines were invaluable. I still owe her a bottle of tequila.
I want to thank Dr. Yunyu Wang, and Susan Kanal for selecting the four key rehabilitation exercises used in this research.
Thanks to The Children’s Hospital in Denver, CO, especially Dr. James Carollo, for providing the motion capture data for those key rehabilitation exercises.
Professors and friends both from here and from Mexico, thank you for all your support.
And finally, Dr. Edward Chow and Dr. Xiaobo Zhou: I am in your debt. This was a really long journey and I know it couldn’t be a successful one without your help and guidance. From my heart, my deepest thanks.
¡Muchas gracias, Señor!
Table of Contents
1.1Human Motion overview 12
1.2Framework for Human Motion Similarity Analysis 23
1.3Dissertation Outline 42
1.5Dissertation Outline 43
A.2Proposed Framework for Measuring and Analyzing Human Motion Similarity 45
A.3Case of Study: Rehabilitation Therapy 49
3.1Key Rehabilitation Exercises 50
3.2 Experiments and results 57
A.4Comparison of Timed 3D ChainCode with FastDTW 79
A.5Enhancing Laban Notation for Rehabilitation Specification 102
5.1Proposed Enhancements Based on Mini-Squats Exercise 102
5.2Proposed Enhancements Based on Standing-Hip Abduction Exercise 105
A.6Design of Human Motion Tracking System Architecture for Rehabilitation Purposes 108
6.1Human Motion Tracking System Architecture for Rehabilitation 108