Self-Adaptive Motion Tracking against On-body Displacement of Flexible Sensors
Chengxu Zuo, Jiawei Fang, Shihui Guo, Yipeng Qin
Abstract
Flexible sensors are promising for ubiquitous sensing of human status due to their flexibility and easy integration as wearable systems. However, on-body displacement of sensors is inevitable since the device cannot be firmly worn at a fixed position across different sessions. This displacement issue causes complicated patterns and significant challenges to subsequent machine learning algorithms. Our work proposes a novel self-adaptive motion tracking network to address this challenge. Our network consists of three novel components: i) a light-weight learnable Affine Transformation layer whose parameters can be tuned to efficiently adapt to unknown displacements; ii) a Fourier-encoded LSTM network for better pattern identification; iii) a novel sequence discrepancy loss equipped with auxiliary regressors for unsupervised tuning of Affine Transformation parameters.
BibTeX
@inproceedings{
zuo2023selfadaptive,
title={Self-Adaptive Motion Tracking against On-body Displacement of Flexible Sensors},
author={Chengxu Zuo and Jiawei Fang and Shihui Guo and Yipeng Qin},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=5ZMBiS1uMq}
}