TY - GEN
T1 - A novel training bike and camera system to evaluate pose of cyclists
AU - Garimella, Raman
AU - Beyers, Koen
AU - Peeters, Thomas
AU - Verwulgen, Stijn
AU - Sels, Seppe
AU - Huysmans, Toon
PY - 2021
Y1 - 2021
N2 - Aerodynamic drag force can account for up to 90% of the opposing force experienced by a cyclist. Therefore, aerodynamic testing and efficiency is a priority in cycling. An inexpensive method to optimize performance is required. In this study, we evaluate a novel indoor setup as a tool for aerodynamic pose training. The setup consists of a bike, indoor home trainer, camera, and wearable inertial motion sensors. A camera calculates frontal area of the cyclist and the trainer varies resistance to the cyclist by using this as an input. To guide a cyclist to assume an optimal pose, joint angles of the body are an objective metric. To track joint angles, two methods were evaluated: optical (RGB camera for the two-dimensional angles in sagittal plane of 6 joints), and inertial sensors (wearable sensors for three-dimensional angles of 13 joints). One (1) male amateur cyclist was instructed to recreate certain static and dynamic poses on the bike. The inertial sensors provide excellent results (absolute error = 0.28?) for knee joint. Based on linear regression analysis, frontal area can be best predicted (correlation 0.4) by chest anterior/posterior tilt, pelvis left/right rotation, neck flexion/extension, chest left/right rotation, and chest left/right lateral tilt (p 0.01)..
AB - Aerodynamic drag force can account for up to 90% of the opposing force experienced by a cyclist. Therefore, aerodynamic testing and efficiency is a priority in cycling. An inexpensive method to optimize performance is required. In this study, we evaluate a novel indoor setup as a tool for aerodynamic pose training. The setup consists of a bike, indoor home trainer, camera, and wearable inertial motion sensors. A camera calculates frontal area of the cyclist and the trainer varies resistance to the cyclist by using this as an input. To guide a cyclist to assume an optimal pose, joint angles of the body are an objective metric. To track joint angles, two methods were evaluated: optical (RGB camera for the two-dimensional angles in sagittal plane of 6 joints), and inertial sensors (wearable sensors for three-dimensional angles of 13 joints). One (1) male amateur cyclist was instructed to recreate certain static and dynamic poses on the bike. The inertial sensors provide excellent results (absolute error = 0.28?) for knee joint. Based on linear regression analysis, frontal area can be best predicted (correlation 0.4) by chest anterior/posterior tilt, pelvis left/right rotation, neck flexion/extension, chest left/right rotation, and chest left/right lateral tilt (p 0.01)..
KW - aerodynamics
KW - cycling
KW - motion capture
KW - training
UR - http://www.scopus.com/inward/record.url?scp=85101240266&partnerID=8YFLogxK
U2 - 10.1115/IMECE2020-24069
DO - 10.1115/IMECE2020-24069
M3 - Conference contribution
AN - SCOPUS:85101240266
T3 - ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
BT - Design, Systems, and Complexity
PB - ASME
T2 - ASME 2020 International Mechanical Engineering Congress and Exposition, IMECE 2020
Y2 - 16 November 2020 through 19 November 2020
ER -