Deep Gait Tracking With Inertial Measurement Unit. (arXiv:2205.04666v1 [cs.LG])

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 …

arxiv measurement tracking