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In this paper, we visualize and quantify the differences between two three-dimensional (3D) surfaces. A human participant was scanned in standing and cycling poses using a 3D scanner. We rigged the standing scan and reposed them to a cycling pose. The two scans were then inspected for the differences in the various segments of the body. The objective of this paper is to demonstrate the potential of using a simple rigging method (Linear Blend Skinning) to re-pose a scan from one configuration to a pose of choice. This forms the first step of an innovative and accurate method to visualize human beings in any pose desired by a designer, engineer, or sports analyst. Applications of this method could be in the fields of fashion, ergonomics, and professional athlete services such as aerodynamic drag force analysis using computational fluid dynamics (CFD).

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).

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