Keynote Lectures

Variability in Human Movement
Nikolaos Stergiou, University of Nebraska Medical Center, United States

Can Markerless Tracking of Human Motion Revolutionize the Analysis of In-game Sports Performance?
Scott Selbie, Queen's University, Canada

Variability in Human Movement

Nikolaos Stergiou
University of Nebraska Medical Center
United States

Brief Bio
Dr. Nick Stergiou is the Distinguished Community Research Chair in Biomechanics and Professor as well as the Director of the Biomechanics Research Building and the Center for Research in Human Movement Variability at the University of Nebraska at Omaha where his primary appointment is. Recently he was also appointed as the Assistant Dean and Director of the Division of Biomechanics and Research Development. He is the Founding Chair of the first ever academic Department of Biomechanics that graduates students with a BS in Biomechanics. His secondary appointment is as a Professor of the Department of Environmental, Agricultural, and Occupational Health of the College of Public Health at the University of Nebraska Medical Center. His research focuses on understanding variability inherent in human movement and he is an international authority in the study of Nonlinear Dynamics. He has published more than 200 peer-reviewed papers and have been inducted to the National Academy of Kinesiology and as a Fellow to the American Institute for Medical and Biological Engineering and the American Society of Biomechanics. Dr. Stergiou’s research spans from infant development to older adult fallers, and has impacted training techniques of surgeons and treatment and rehabilitation of pathologies, such as peripheral arterial disease. He has received more 40 million dollars in funding from NIH, NASA, NSF, the US Department of Education, and many other agencies and foundations. He has received the largest grant in the history of his University, a NIH P20 grant that was worth 10.1 million dollars. This grant allowed him to develop the Center for Research in Human Movement Variability. Recently, he was able to renew this grant by receiving another 10.3 million dollars. He has also several inventions and has procured a private donation of $6 million to build the 23,000 square feet Biomechanics Research Building that has opened in August of 2013. This is the first building dedicated to biomechanics research in the world. It is also the first building on his campus exclusively dedicated to research. Recently was able to procure 11.6 million in private donations to build a 30,000 square feet expansion to this building which opened in October of 2019.

Abstract
An optimal level of variability enables us to interact adaptively and safely to a continuously changing environment, where often our movements must be adjusted in a matter of milliseconds. A large body of research exists that demonstrates natural variability in healthy movement such as gait and posture (along with variability in other, healthy biological signals e.g. heart rate), and a loss of this variability in sports injury, as well as in a variety of neurodegenerative and physiological disorders. In this seminar I submit that this field of research is now in pressing need of an innovative “next step” that goes beyond the many descriptive studies that characterize levels of variability in various populations. We need to devise novel therapies and technologies that will harness the existing knowledge on biological variability and create new possibilities for those in need to improve performance and/or restore their decreased physical abilities. I also propose that the nature of the specific physiological limitations present in the neuromuscular apparatus may be less important in the physiological complexity framework than the control mechanisms adopted by the affected individual in the coordination of the available degrees of freedom. The theoretical underpinnings of this framework suggest that interventions and technologies designed to restore healthy system dynamics may optimize functional improvements in affected individuals. I submit that interventions based on the restoration of optimal variability and movement complexity could potentially be applied across a range of dysfunctions as it addresses the adaptability and coordination of available degrees of freedom, regardless of the internal constraints of the individual (1-6).

REFERENCES

1. Cavanaugh JT, Kelty-Stephen DG, Stergiou N. (2017). Multifractality, Interactivity, and the Adaptive Capacity of the Human Movement System: A Perspective for Advancing the Conceptual Basis of Neurologic Physical Therapy. Journal of Neurologic Physical Therapy. Oct;41(4):245-251.

2. Stergiou N, Decker LM. (2011). Human movement variability, nonlinear dynamics, and pathology: Is there a connection? Human Movement Science. Oct;30(5):869-88.

3. Stergiou N, Harbourne R, Cavanaugh J. (2006). Optimal Movement Variability: A New Theoretical Perspective for Neurologic Physical Therapy. Journal of Neurologic Physical Therapy. Sep;30(3):120-129.

4. Cavanaugh JT, Guskiewicz KM, Stergiou N. (2005). A nonlinear dynamic approach for evaluating postural control: New directions for the management of sport-related cerebral concussion. Sports Medicine. 35(11):935-950.

5. Harbourne RT, Stergiou N. (2009). Movement Variability and the Use of Nonlinear Tools: Principles to Guide Physical Therapy Practice. Physical Therapy. Mar;89(3):267-282.

6. Harrison SJ, Stergiou N. (2015). Complex Adaptive Behavior and Dexterous Action. Nonlinear Dynamics, Psychology, and Life Sciences. 19(4):345-94.

DISCLOSURE STATEMENT

This work was supported by the Center for Research in Human Movement Variability of the University of Nebraska at Omaha and the NIH (P20GM109090, R01NS114282, and R15AG063106).

Can Markerless Tracking of Human Motion Revolutionize the Analysis of In-game Sports Performance?

Scott Selbie
Queen's University
Canada

Brief Bio
Dr. Selbie is the President of Theia Markerless Inc (Canada), Director of Research at C-Motion Inc(USA), an adjunct Professor in Mechanical Engineering at Queen's University (Canada) and in the department of Kinesiology at the University of Massachusetts (USA). Scott has been involved in the development of analytical tools for biomechanics based on 3D motion capture data for most of his career, and he has been intimately involved with the evolution of C-Motion’s Visual3D software since the original technology transfer from the National Institutes of Health in 1997. Scott has joined Theia Markerless, a recent spin-off company from C-Motion, that is focused on creating the most accurate, and fully mathematically observable, 3D pose estimation of the human body from an array of video cameras.

Abstract
For some time now, researchers in biomechanics have envisioned a new paradigm where accurate data about human movement would provide a basis for analyzing in-game performance. This presentation is predicated on the proposition that 3D biomechanical analyses of player performance based on in-game recordings can be used to evaluate performance, predict the susceptibility to musculoskeletal injuries, inform training and rehabilitation strategies, and influence game strategy. If this proposition is true, markerless tracking of human motion will revolutionize the analysis of in-game sports performance. As this talk is about the performance of players on the field I will summarize the motion tracking of players that is possible during live game recording, and will focus on 3D human motion tracking using temporally and spatially synchronized commercial video cameras mounted around the field of play. Video based solutions passively observe the field of play, can be used indoors and outdoors, and do not require that sensors or markers be attached to the players; thus do not require the player’s or team’s awareness of the recording, cooperation, or coordination, and do not influence the game directly.

I will present the state of the art 3D tracking of human motion suitable for biomechanical analyses based on using Deep Similarity Learning to identify uniquely all players in the field of view of the cameras, Deep Neural Networks to identify the location of anatomical features of each player in the multiple video images, and multibody optimization to consolidate this data into a mathematically observable estimate of the 3D position and orientation (pose) of a personalized model of each player. Such systems do not require assumptions of symmetry, behavioural rules or physical constraints as they directly estimate the 3D pose. The nature of the technology lends itself to the vast amounts of accurate and meaningfully consolidated data required by the burgeoning field of sports analytics. I will present experimental results of the critical issues of accuracy, repeatability and reliability of the pose estimation in a controlled laboratory setting and speculate on these same issues in live game recording.

The potential for a biomechanical analysis of individual player performance is actual. As an example of the scope of data that is now possible, at the time of submission of this abstract Kinatrax Inc (USA) had recorded and analyzed more than 560,000 pitches during live in season Major League Baseball games.