LIMU-BERT

IF 0.7 Q4 TELECOMMUNICATIONS
Huatao Xu, Pengfei Zhou, R. Tan, Mo Li, Guobin Shen
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引用次数: 1

摘要

深度学习极大地增强了惯性测量单元(IMU)传感器的广泛应用。大多数现有的工作需要大量精心策划的标记数据来训练基于imu的传感模型,这导致了高昂的注释和训练成本。与标记数据相比,未标记的IMU数据丰富且易于获取。本文提出了一种新的表征学习模型,该模型可以利用未标记的IMU数据并提取广义特征而不是特定于任务的特征。通过我们的模型学习表征,用有限的标记样本训练的特定任务模型可以在典型的IMU传感应用中获得优异的性能,例如人类活动识别(HAR)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LIMU-BERT
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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