【24h】

Kalman-Filter-Based Walking Distance Estimation for a Smart-Watch

机译:基于卡尔曼滤波器的智能手表步行距离估计

获取原文
获取原文并翻译 | 示例

摘要

A novel walking distance estimation algorithm using the inertial sensors of the smart-watch is proposed. Firstly, the peaks of the norm of the accelerometer and gyroscope signals are detected. Due to arm swing, walking step detection using these peaks are not reliable. A Kalman filter is used to combine with the peak detection algorithm applied on the accelerometer and gyroscope norm peaks and robustly detect walking steps even if there is large arm swing. Walking distance is estimated using walking step time and walking length relationship. The proposed algorithm was tested on 25 subjects: each subject walked 50 m six times with different walking speed and different arm swing speed. The standard deviation of walking distance estimation error is 3.9 m (without person dependent calibration) and 1.9 m (with person dependent calibration) for a 50m distance.
机译:提出了一种利用智能手表惯性传感器的步行距离估计算法。首先,检测加速度计和陀螺仪信号的范数的峰值。由于手臂摆动,使用这些峰值进行步行步骤检测是不可靠的。卡尔曼滤波器用于与加速度计和陀螺仪范数峰值上应用的峰值检测算法结合,即使手臂摆动较大,也可以稳健地检测步行步骤。使用步行步长和步行长度的关系来估算步行距离。该算法在25个对象上进行了测试:每个对象以不同的步行速度和不同的手臂摆动速度走了50 m六次。对于50m的距离,步行距离估计误差的标准偏差为3.9 m(无人校准)和1.9m(无人校准)。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号