A software solution for the analysis of asymmetry in cyclists using optical motion capture technology

A software solution for the analysis of asymmetry in cyclists using optical motion capture technology

[Extracted with permission from the University of Sunderland Digital Innovation Beacon Research Newsletter, Winter 2015/16]

A University of Sunderland Digital Innovation Beacon-funded research project is underway to investigate the use of motion capture technology for the analysis of asymmetry in cyclists.

Faculty of Applied Sciences staff Liz Gandy and Sheila Garfield from Computing, Engineering and Technology; Ken McGarry from Pharmacy, Health and Well-being; and Bob Hogg from Sport and Exercise Sciences are working on this project with external collaborators from two physiotherapy clinics: John Dennis and Phil Smith from Physiohaüs, Newcastle; and Tim Pigott from HP3, Manchester.

The aim of the project is to develop a software solution to extend the functionality of the Retül Vantage 3D Motion Capture SystemTM bike fit system, which reports an individual cyclist’s cycling position, to carry out statistical analysis on data gathered for multiple cyclists. The software will be used to evaluate the effectiveness of the technology for investigation of asymmetry in a large sample of cyclists.

Cycling is the fourth most common adult sporting and recreational activity in Britain, with 43% of the UK population having access to a bike (CTC Survey, 2011). The health benefits of cycling are widely reported and include reducing the risk of coronary heart disease, stroke, cancer, obesity and type 2 diabetes, in addition to helping keep the musculoskeletal system healthy and promoting mental wellbeing. Despite the health benefits, cycling comes with risk factors of its own, with the cycling position placing the upper body in an unnatural position, increasing the risk of pain and injury. Any asymmetries present may be an aggravating factor, exacerbated by fatigue with extended cycling duration. Despite a lack of scientific studies on lower back pain in cyclists, there is evidence that cycling position and bike setup are an important consideration but few cyclists, other than those in the elite sporting category, maintain an optimal position on the bike.

A range of techniques are employed for the assessment of bike fit and cycling position and, in recent years, systems have been developed which utilise both 2D and 3D optical motion capture technology. The Retül Vantage 3D Motion Capture SystemTM is an example of such technology. The cyclist is mounted on a stationary bike fixed to a turbo trainer and optical joint markers are attached to specific joint positions on the participant’s body. As they cycle, motion data is recorded via a receiver which utilises technology similar to that of the Microsoft KinectTM.

Data for this project is being collected by the external collaborators during bike fit assessments carried out at the two physiotherapy clinics and retrospective permission has been obtained to include additional datasets captured prior to the start of the project. To-date, this has provided a sample of 58 cyclists.

Software has been developed to automate the extraction of biomechanical and joint angle data from the PDF reports generated by the RetülTM system and analyse it for the presence of asymmetry in 32 separate measures of joint angle, limb alignment, limb movement and anthropometrics. In addition to statistical analysis of correlations between asymmetry in the measurements for individual cyclists, comparison between multiple datasets has been incorporated. This enables the extent of asymmetry present across the sample of cyclists to be studied and to determine whether there is a bias towards a particular direction.

Preliminary results for the sample of 58 cyclists have revealed the presence of asymmetry in all 32 measures, with a third of the measures showing a bias towards one side of the body or the other. For 7 measures, a significant reduction in asymmetry is shown after adjustments to the fit of the bike. A larger sample size and additional analysis is necessary to determine whether these observations are representative of the cycling population so data collection is planned to continue into 2016.

The intended outcome of the project is for the results to be written up as a journal article to be submitted for publication. A Digital Innovation Beacon Seminar is also planned for 28th June 2016 at the University of Sunderland, when more details on the technology and further discussion of the results will be presented.