Dr. Brent Edwards

 

University of Calgary

 

Topic: The “ins and outs” of bone loading and stress fracture prediction

Stress fractures have been characterized as a mechanical fatigue process, whereby cumulative physical activity results in microdamage accumulation and impaired tissue quality. Mechanical fatigue is heavily dependent on the resulting strain (“the ins”, i.e., internal) from the applied load (“the outs”, i.e., external), which can be approximated using in-silico modeling approaches. Unfortunately, these approaches are arduous and often limited to the laboratory. For this reason, sport scientists frequently rely on surrogate measures of bone strain, including information from wearable technology. This talk will review current understanding of the mechanical fatigue process in bone, including its dependency on strain magnitude, potential sources of variation, and probabilistic methods to characterize fatigue failure. The challenges associated with extending the mechanical fatigue paradigm to stress fracture prediction will then be discussed, along with recent advancements in load monitoring and integrative biomechanical methods for personalized risk assessment.

 

 

Lab Website, Google Scholar

 

 

 

Dr. Emma Hodson-Tole

 

Manchester Metropolitan University

 

Emma Hodson-Tole obtained her Ph.D. degree in biomechanics and muscle physiology from The Royal Veterinary College, University of London in 2007. She worked as a postdoctoral fellow in The School of Applied Physiology at Georgia Institute of Technology, USA, before being awarded a Sir Henry Wellcome Postdoctoral Fellowship, from The Wellcome Trust, and returning to the UK to work at Manchester Metropolitan University. She is now Reader in Neuromusculoskeletal Integration in the Musculoskeletal Sciences & Sports Medicine Research Centre and Institute of Sport at Manchester Metropolitan. Her work combines ultrasound imaging techniques and electromyography to investigate dynamics of skeletal muscle anatomy, motor unit recruitment and multi-muscle coordination patterns during locomotor tasks, such as cycling.

 

 

 

Academic Profile, Google Scholar

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Dr Christophe Sauret

 

Institution Nationale des Invalides - Institut de Biomécanique Humaines Georges Charpak

 

Dr. Christophe Sauret is currently head of the research and innovation department of the Centre d'Etudes et de Recherche sur l'Appareillage des Handicapés (Institution Nationale des Invalides, Paris, France), which is a dedicated center working on assistive devices for motor disability compensation. He is also associate researcher at the Institut de Biomécanique Humaine Georges Charpak (Arts & Métiers Institute of Technology, Paris, France). During the past 15 years, his researches focused on biomechanics to improve motor performance and injury prevention in contexts of high level sports (Golf, Table Tennis, Air pistol shooting, Table Tennis, Gymnastics and Tennis) and motor disability (daily and sports) through both lab and in-field approaches.

 

During this talk, Christophe Sauret will place a special emphasis on embedded sensors for biomechanical analysis of manual wheelchair propulsion for sports applications, with pros and cons of inertial measurement units and wheel-dynamometers to assess kinematics and kinetics.

 

 

 

 

 

 

 

 

 

Dr. Cat Shin

 

English Institute of Sport

 

As biomechanics consultant and project lead at the EIS, Cat has worked with over 20 Olympic and Paralympic sports. She specialises in understanding the context, clarifying the performance question and collecting relevant, accurate data directly in the field. She works closely with multi-disciplinary teams, academic institutions and external companies to achieve performance impact. Now into her third Olympic cycle, Cat is still passionate about the impact biomechanics can have on elite performance and utilising the variety of ways in which that impact is achieved.

 

Cat understands the value and challenge of combining academic rigour with applied practice, having combined her PhD research through the University of Lincoln with an embedded practitioner role at British Canoeing and her Loughborough MSc with biomechanics support to British Athletics. As well as her biomechanics role, Cat holds a technical lead role in performance analysis to develop practitioners’ ability to use code to increase efficiency and insight from data. To meet this aim, she created and developed the ‘EIS code school’ which is now on its third intake.

 

 

 

Dr. Todd Pataky*

 

Kyoto University

 

Prof. Pataky's research focuses on the analysis and simulation of random processes in biomechanical systems and continua. He studies a variety of human movement applications including: clinical gait analysis, sports performance assessment, and stochastic simulation of human dynamics. He is the creator of a number of open-source software packages for the biomechanics community including spm1d, the popular package used for 1D Statistical Parametric Mapping, and power1d for numerical estimates of statistical power.

 

*Todd's travel to ISBS2022 is currently not permitted by his university and therefore his attendance will be subject to relaxation of travel rules near to the conference dates.

 

Github sites, Google Scholar

 

 

 

 

 

 

 

Dr. Marion Mundt

 

University of Western Australia

 

On-field motion analysis: Repurposing motion capture datasets and training machine learning models to bring the lab to the field

 

Since the emergence of biomechanics as a modern scientific discipline, multi-scale analysis of human motion has been locked to laboratory environments. Predominantly this has comprised multiple near infra-red cameras and retro-reflective spherical markers affixed to an athlete's body to record 3D motion kinematics, collected concurrently with ground embedded force plates recording ground reaction forces (GRFs) for the calculation of joint kinetics via inverse-dynamics methods. Consequently, there exist large 3D motion capture databases of commonly performed movements like running or sidestepping, and other unique smaller 3D datasets on specialised populations of complex movements and use cases.

In the ongoing efforts to enhance performance and prevent injury, 3D analysis in laboratory settings serves as the analysis approach of choice owing to greater accuracy, reliability, scale, and quantum, of the higher resolution data collected in that environment. Yet the laboratory setting does not easily allow for the assessment of internal and external factors that contribute to athletic performance during on-field training and competition environments. This data quality versus ecological validity trade-off has escalated the call to shift sports biomechanics data collection practices from the lab to the field. To facilitate this shift, wearable sensors and video-based motion analysis supported by machine learning algorithms have become increasing popular tools.

 

This presentation will provide insight into current machine learning applications in motion analysis and present methods to repurpose historical motion capture datasets to support both, wearable sensor and video-based analyses.