Can I read LIMU-BERT: Unleashing the Potential of Unlabeled Data for IMU Sensing Applications on EtoBox?

LIMU-BERT: Unleashing the Potential of Unlabeled Data for IMU Sensing Applications by Huatao Xu; Pengfei Zhou; Rui Tan; Mo Li; Guobin Shen is a Computer Science article available to read on EtoBox.

What is LIMU-BERT: Unleashing the Potential of Unlabeled Data for IMU Sensing Applications about?

Deep learning greatly empowers Inertial Measurement Unit (IMU) sensors for a wide range of sensing applications. Most existing works require substantial amounts of wellcurated labeled data to train IMU-based sensing models, which incurs high annotation and training costs. Compared with labeled data, unlabeled IMU data are abundant and easily accessible. This article presents a novel representation learning model that can make use of unlabeled IMU data and extract generalized rather than task-specific features. With the representations learned via our model, task-specific models trained with limited labeled samples can achieve superior performances in typical IMU sensing applications, such as Human Activity Recognition (HAR).

Who reads LIMU-BERT: Unleashing the Potential of Unlabeled Data for IMU Sensing Applications?

Author

Huatao Xu; Pengfei Zhou; Rui Tan; Mo Li; Guobin Shen

Publisher

ACM

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)