article

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

Authors:

Guobin ShenAuthors Info & Claims

GetMobile: Mobile Computing and Communications, Volume 26, Issue 3

Pages 39 - 42

https://doi.org/10.1145/3568113.3568124

Published: 11 October 2022 Publication History

Abstract

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).

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Cited By

Wang JCui WZhu TNing HLiu Z(2024)An Improved Masking Strategy for Self- Supervised Masked Reconstruction in Human Activity RecognitionIEEE Sensors Journal10.1109/JSEN.2024.339075524:11(18699-18709)Online publication date: 1-Jun-2024
https://doi.org/10.1109/JSEN.2024.3390755
Ji SZheng XWu C(2024)HARGPT: Are LLMs Zero-Shot Human Activity Recognizers?2024 IEEE International Workshop on Foundation Models for Cyber-Physical Systems & Internet of Things (FMSys)10.1109/FMSys62467.2024.00011(38-43)Online publication date: 13-May-2024
https://doi.org/10.1109/FMSys62467.2024.00011
Wang HLiu YZhao CHe JDing WChen X(2023)CaliFormer: Leveraging Unlabeled Measurements to Calibrate Sensors with Self-supervised LearningAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3612917(743-748)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3612917

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Published In

cover image GetMobile: Mobile Computing and Communications

GetMobile: Mobile Computing and Communications Volume 26, Issue 3

September 2022

38 pages

ISSN:2375-0529

EISSN:2375-0537

DOI:10.1145/3568113

Editor:
Landon Cox
Microsoft Research

Issue’s Table of Contents

Copyright © 2022 Copyright is held by the owner/author(s).

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Publication History

Published: 11 October 2022

Published in SIGMOBILE-GETMOBILE Volume 26, Issue 3

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Cited By

Wang JCui WZhu TNing HLiu Z(2024)An Improved Masking Strategy for Self- Supervised Masked Reconstruction in Human Activity RecognitionIEEE Sensors Journal10.1109/JSEN.2024.339075524:11(18699-18709)Online publication date: 1-Jun-2024
https://doi.org/10.1109/JSEN.2024.3390755
Ji SZheng XWu C(2024)HARGPT: Are LLMs Zero-Shot Human Activity Recognizers?2024 IEEE International Workshop on Foundation Models for Cyber-Physical Systems & Internet of Things (FMSys)10.1109/FMSys62467.2024.00011(38-43)Online publication date: 13-May-2024
https://doi.org/10.1109/FMSys62467.2024.00011
Wang HLiu YZhao CHe JDing WChen X(2023)CaliFormer: Leveraging Unlabeled Measurements to Calibrate Sensors with Self-supervised LearningAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3612917(743-748)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3612917

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