Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.
AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.
Motorcycling as a sport encompasses various disciplines involving the recreational and competitive use of motorcycles. These activities can range from road racing on tarmac to off-road racing on dirt, sand, or mud tracks. Understanding rider effort is key to enhance riding performance and experience. Methods for capturing rider effort currently rely on post-ride surveys, providing a global perception of the ride but are typically unable to capture effort in detailed situations. This paper explores the use of a pressure sensitive motorcycle glove and pressure sensitive handlebar grips to estimate riding effort. We introduce the technology and discuss the potential based on a small explorative trial (N=1) which provides first insights into rider effort and data from the MotoGlove. Preliminary observations show the potential to better understand rider effort at specific moments during the ride. This work serves as the basis of future intelligent systems to support motorcyclists real-world rides.