{"id":{"repo_id":"uwtsd","oai_identifier":"oai:repository.uwtsd.ac.uk:3324"},"canonical_url":"https://search.dev.ndltd.org/etd/uwtsd/oai:repository.uwtsd.ac.uk:3324","repository":{"repo_id":"uwtsd","name":"University of Wales Trinity Saint David","base_url":"https://repository.uwtsd.ac.uk/cgi/oai2"},"display":{"title":"Measurement and Analysis of Rider Input and Motorcycle Response in High-Performance Environments","abstract":"This thesis presents the development and validation of a novel rider input measurement platform designed to capture and analyse key rider inputs, including handlebar forces, footpeg inputs, and steering torque, to better understand rider and motorcycle behaviour in high-performance settings. The project aimed to provide insights into the relationship between rider inputs and motorcycle response, enabling a comparison of how different riders apply forces and control their motorcycles during high-speed riding. The study builds on the foundational work by (Przibylla 2020) and confirms several of his findings, particularly the transient effects influencing steering torque. However, this research also challenges traditional views, especially regarding the role of footpeg inputs in steering. The data confirms that counter-steering through the handlebars remains the primary input for directional changes, while footpeg forces are suspected to mainly serve to brace the rider’s body during cornering and braking. The analysis revealed that experienced riders use their body weight and footpeg forces more effectively, optimising their performance by distributing weight efficiently and minimising upper body strain. The platform was tested with riders of varying skill levels, highlighting the differences in their ability to manage steering torque, body positioning, and throttle and brake inputs. These findings suggest that the system can be a valuable tool for rider comparison and performance analysis, offering objective, quantitative data for improving riding techniques. Despite some technical challenges, the platform demonstrated great potential for future rider analysis and training. Recommendations for further development include refining the system’s data logging capabilities and expanding testing with a broader range of riders, including professional racers.","abstract_html":"This thesis presents the development and validation of a novel rider input measurement platform designed to capture and analyse key rider inputs, including handlebar forces, footpeg inputs, and steering torque, to better understand rider and motorcycle behaviour in high-performance settings. The project aimed to provide insights into the relationship between rider inputs and motorcycle response, enabling a comparison of how different riders apply forces and control their motorcycles during high-speed riding. The study builds on the foundational work by (Przibylla 2020) and confirms several of his findings, particularly the transient effects influencing steering torque. However, this research also challenges traditional views, especially regarding the role of footpeg inputs in steering. The data confirms that counter-steering through the handlebars remains the primary input for directional changes, while footpeg forces are suspected to mainly serve to brace the rider’s body during cornering and braking. The analysis revealed that experienced riders use their body weight and footpeg forces more effectively, optimising their performance by distributing weight efficiently and minimising upper body strain. The platform was tested with riders of varying skill levels, highlighting the differences in their ability to manage steering torque, body positioning, and throttle and brake inputs. These findings suggest that the system can be a valuable tool for rider comparison and performance analysis, offering objective, quantitative data for improving riding techniques. Despite some technical challenges, the platform demonstrated great potential for future rider analysis and training. Recommendations for further development include refining the system’s data logging capabilities and expanding testing with a broader range of riders, including professional racers.","abstract_has_math":false,"creators":["Iatrakis, Ilias Gideon"],"institution":"University of Wales Trinity Saint David","degree_name":"msc","degree_level":"masters","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-12","date_published":"2024-12","updated_at":"2026-07-24T05:53:05Z","subjects":["GV Hamdden Adloniant","TL Cerbydau modur. Awyrenneg. 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The platform was tested with riders of varying skill levels, highlighting the differences in their ability to manage steering torque, body positioning, and throttle and brake inputs. These findings suggest that the system can be a valuable tool for rider comparison and performance analysis, offering objective, quantitative data for improving riding techniques. Despite some technical challenges, the platform demonstrated great potential for future rider analysis and training. Recommendations for further development include refining the system’s data logging capabilities and expanding testing with a broader range of riders, including professional racers."]},{"key":"dc:format","label":"Dc Format","values":["text"]},{"key":"dc:title","label":"Title","values":["Measurement and Analysis of Rider Input and Motorcycle Response in High-Performance Environments"]}]}],"canonical_facts":{"dc:contributor.sponsor":["University of Wales Trinity Saint David"],"dc:creator":["Iatrakis, Ilias Gideon"],"dc:date":["2024-12-03"],"dc:date.issued":["2024-12"],"dc:description.abstract":["This thesis presents the development and validation of a novel rider input measurement platform designed to capture and analyse key rider inputs, including handlebar forces, footpeg inputs, and steering torque, to better understand rider and motorcycle behaviour in high-performance settings. 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