论文标题
惯性测量单元的深度步态跟踪
Deep Gait Tracking With Inertial Measurement Unit
论文作者
论文摘要
本文介绍了仅使用六轴惯性测量单位（IMU）传感器数据的卷积神经网络运动跟踪。提出的方法可以通过采用基于差异的输入来适应各种步行条件。通过在IMU传感器数据上滑动和随机窗口采样，进一步增强了培训数据，以提高数据多样性以提高性能。提出的方法将三维输出的预测融合为一个模型。所提出的融合模型可以在X轴上达到2.30+-2.23 cm的平均误差，Y轴为0.91+-0.95 cm，Z轴为0.58+-0.52 cm。
This paper presents a convolutional neural network based foot motion tracking with only six-axis Inertial-Measurement-Unit (IMU) sensor data. The presented approach can adapt to various walking conditions by adopting differential and window based input. The training data are further augmented by sliding and random window samplings on IMU sensor data to increase data diversity for better performance. The proposed approach fuses predictions of three dimensional output into one model. The proposed fused model can achieve average error of 2.30+-2.23 cm in X-axis, 0.91+-0.95 cm in Y-axis and 0.58+-0.52 cm in Z-axis.
