Spatial-Temporal Masked Autoencoder for Multi-Device Wearable Human Activity Recognition

IF 3.6 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Shenghuan Miao, Ling Chen, Rong Hu
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引用次数: 0

Abstract

The widespread adoption of wearable devices has led to a surge in the development of multi-device wearable human activity recognition (WHAR) systems. Nevertheless, the performance of traditional supervised learning-based methods to WHAR is limited by the challenge of collecting ample annotated wearable data. To overcome this limitation, self-supervised learning (SSL) has emerged as a promising solution by first training a competent feature extractor on a substantial quantity of unlabeled data, followed by refining a minimal classifier with a small amount of labeled data. Despite the promise of SSL in WHAR, the majority of studies have not considered missing device scenarios in multi-device WHAR. To bridge this gap, we propose a multi-device SSL WHAR method termed Spatial-Temporal Masked Autoencoder (STMAE). STMAE captures discriminative activity representations by utilizing the asymmetrical encoder-decoder structure and two-stage spatial-temporal masking strategy, which can exploit the spatial-temporal correlations in multi-device data to improve the performance of SSL WHAR, especially on missing device scenarios. Experiments on four real-world datasets demonstrate the efficacy of STMAE in various practical scenarios.
用于多设备可穿戴人体活动识别的时空掩码自动编码器
随着可穿戴设备的广泛应用,多设备可穿戴人体活动识别(WHAR)系统的开发也随之激增。然而,传统的基于监督学习的人类活动识别(WHAR)方法由于难以收集到大量带注释的可穿戴设备数据而性能有限。为了克服这一限制,自监督学习(SSL)成为一种很有前景的解决方案,它首先在大量未标注数据上训练一个合格的特征提取器,然后用少量标注数据完善一个最小分类器。尽管 SSL 在 WHAR 中大有可为,但大多数研究都没有考虑多设备 WHAR 中的设备缺失情况。为了弥补这一不足,我们提出了一种多设备 SSL WHAR 方法,称为空间-时间掩码自动编码器(STMAE)。STMAE 利用非对称编码器-解码器结构和两阶段空间-时间掩码策略来捕捉具有区分性的活动表示,从而利用多设备数据中的空间-时间相关性来提高 SSL WHAR 的性能,尤其是在设备缺失的情况下。在四个真实数据集上的实验证明了 STMAE 在各种实际场景中的功效。
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来源期刊
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Computer Science-Computer Networks and Communications
CiteScore
9.10
自引率
0.00%
发文量
154
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