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首页> 外文期刊>Mechatronics, IEEE/ASME Transactions on >Whole-Body Pose Estimation in Human Bicycle Riding Using a Small Set of Wearable Sensors
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Whole-Body Pose Estimation in Human Bicycle Riding Using a Small Set of Wearable Sensors

机译：使用一小组可穿戴传感器在人骑自行车中的全身姿势估计

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摘要

Tracking whole-body human pose in physical human–machine interactions is challenging because of highly dimensional human motions and lack of inexpensive, nonintrusive motion sensors in outdoor environment. In this paper, we present a computational scheme to estimate the human whole-body pose with application to bicycle riding using a small set of wearable sensors. The estimation scheme is built on the fusion of gyroscopes, accelerometers, force sensors, and physical rider–bicycle interaction constraints through an extended Kalman filter design. The use of physical rider–bicycle interaction constraints helps not only eliminate the integration drifts of inertial sensor measurements but also reduce the number of the needed wearable sensors for pose estimation. For each set of the upper and the lower limb, only one tri-axial gyroscope is needed to accurately obtain the 3-D pose information. The drift-free, reliable estimation performance is demonstrated through both indoor and outdoor riding experiments.
机译：由于人体运动的维度高，并且在室外环境中缺乏廉价的，非侵入式的运动传感器，因此在人体与人机互动中跟踪人体的整体姿势具有挑战性。在本文中，我们提出了一种计算方案，用于估计人体全身姿势，并使用一小组可穿戴传感器在自行车骑行中的应用。估计方案​​是基于陀螺仪，加速度计，力传感器以及骑行者与自行车的物理交互约束的融合，并通过扩展的卡尔曼滤波器设计实现的。骑行者与自行车之间的物理交互约束的使用不仅有助于消除惯性传感器测量值的积分漂移，而且还可以减少姿态估计所需的可穿戴传感器的数量。对于每组上肢和下肢，仅需要一个三轴陀螺仪即可准确地获取3-D姿态信息。通过室内和室外骑行实验证明了无漂移，可靠的估计性能。

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