In-situ visualization of pedaling forces on cycling training videos

Over the last decades, visual representations of data has been a commonly used medium to bolster human cognition in performance evaluation of professional athletes. However, the current approaches to these visualizations still build upon the paper based principles of initial designs with solid backgrounds. Due to this situation, same visualizations usually fail to provide explicit information about the physical characteristics of the scenario that the data was captured, such as the form of athletes. In this work, we present a data visualization method which combines visual representations of cyclist's pedaling with correlated frames of indoor training videos. We designed a prototype system which allows us to superimpose various pedaling visualizations onto simultaneously captured training videos of cyclists. The results of user studies we conducted with twelve professional cyclists confirmed their interest in new possibilities emerging from intuitive data visualizations. We also received valuable feedback about the feasible benefits of our approach over traditional approaches, such as reduced cognitive overload in understanding visualizations. We conclude by discussing the future implementations and application areas of our approach and further need of adjusting it to distinct training scenarios.