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A NOVEL TRAINING BIKE AND CAMERA SYSTEM TO EVALUATE POSE OF CYCLISTS

机译:一种新颖的训练自行车和相机系统,以评估骑自行车者的姿势

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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 = 0.28~0) 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).
机译:空气动力阻力可以占骑自行车者所经历的最多90%的相对力量。因此,空气动力学测试和效率是循环的优先事项。需要优化性能的廉价方法。在这项研究中,我们评估了一个新颖的室内设置作为空气动力学姿势培训的工具。设置包括自行车,室内家庭教练,相机和可穿戴惯性运动传感器。相机计算骑自行车者的正面区域,并且培训师通过将其用作输入来改变对骑自行车者的抵抗力。为了引导骑自行车者假设最佳姿势,身体的关节角度是客观的公制。为了跟踪关节角度,评估两种方法:光学(RGB相机用于6个接头的矢状平面中的二维角度),和惯性传感器(用于13个关节的三维角度的可穿戴传感器)。指示一(1)名男性业余骑自行车者在自行车上重建某些静态和动态姿势。介质传感器为膝关节提供优异的结果(绝对误差= 0.28〜0)。基于线性回归分析,前面积可以最好地预测(相关> 0.4)通过胸部前倾斜,骨盆左/右旋转,颈部屈曲/伸展,胸部左/右旋转,胸部左/右侧倾斜(P <0.01)。

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