# Deep Gait Tracking With Inertial Measurement Unit

arXiv: https://arxiv.org/abs/2205.04666 · HTML: https://mlsanity.com/paper/2205.04666/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 10 May 2022.

## Builds on (most cited first, 2 of 2)

- [Real-time Joint Object Detection and Semantic Segmentation Network for Automated Driving](https://mlsanity.com/paper/1901.03912/) (2019)
- [Sensor-based Gait Parameter Extraction with Deep Convolutional Neural Networks](https://mlsanity.com/paper/1609.03323/) (2016)

## Abstract (arXiv)

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.