Deep Gait Tracking With Inertial Measurement Unit

TL;DR

This paper introduces a convolutional neural network method for foot motion tracking using only six-axis IMU sensors, achieving high accuracy across various walking conditions through data augmentation and model fusion.

Contribution

It presents a novel deep learning approach that effectively fuses IMU sensor data for precise gait tracking, adaptable to different walking scenarios.

Findings

Average error of 2.30 cm in X-axis

Average error of 0.91 cm in Y-axis

Average error of 0.58 cm in Z-axis

Abstract

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.