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首页> 外文期刊>IEEE Transactions on Biomedical Engineering >Rider Trunk and Bicycle Pose Estimation With Fusion of Force/Inertial Sensors
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Rider Trunk and Bicycle Pose Estimation With Fusion of Force/Inertial Sensors

机译：力/惯性传感器融合的车手后备箱和自行车姿势估计

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摘要

Estimation of human pose in physical human–machine interactions such as bicycling is challenging because of highly-dimensional human motion and lack of inexpensive, effective motion sensors. In this paper, we present a computational scheme to estimate both the rider trunk pose and the bicycle roll angle using only inertial and force sensors. The estimation scheme is built on a rider–bicycle dynamic model and the fusion of the wearable inertial sensors and the bicycle force sensors. We take advantages of the attractive properties of the robust force measurements and the motion-sensitive inertial measurements. The rider–bicycle dynamic model provides the underlying relationship between the force and the inertial measurements. The extended Kalman filter-based sensor fusion design fully incorporates the dynamic effects of the force measurements. The performance of the estimation scheme is demonstrated through extensive indoor and outdoor riding experiments.
机译：由于人类的高维度运动以及缺乏廉价，有效的运动传感器，因此在诸如骑车等人机交互过程中对人体姿态的估计具有挑战性。在本文中，我们提出了一种仅使用惯性和力传感器来估计骑乘者躯干姿势和自行车侧倾角的计算方案。估计方案​​基于骑车人-自行车动力学模型以及可穿戴惯性传感器和自行车力传感器的融合。我们利用了强大的力测量和对运动敏感的惯性测量的吸引人的特性。骑自行车动态模型提供了力和惯性测量之间的潜在关系。基于卡尔曼滤波器的扩展传感器融合设计完全融合了力测量的动态效果。通过大量的室内和室外骑行实验证明了该估计方案的性能。

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