research-article

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

Authors:

Shenghuan Miao,

Rong HuAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 7, Issue 4

Article No.: 172, Pages 1 - 25

https://doi.org/10.1145/3631415

Published: 12 January 2024 Publication History

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.

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Cited By

M JA ND SR SN BN SSingasani TVatambeti R(2024)Segment-Based Unsupervised Deep Learning for Human Activity Recognition using Accelerometer Data and SBOA based Channel Attention NetworksInternational Research Journal of Multidisciplinary Technovation10.54392/irjmt2461(1-16)Online publication date: 29-Oct-2024
https://doi.org/10.54392/irjmt2461
Logacjov A(2024)Self-supervised Learning for Accelerometer-based Human Activity Recognition: A SurveyProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997678:4(1-42)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699767
Haresamudram HEssa IPlötz T(2024)A Washing Machine is All You Need? On the Feasibility of Machine Data for Self-Supervised Human Activity Recognition2024 International Conference on Activity and Behavior Computing (ABC)10.1109/ABC61795.2024.10651688(1-10)Online publication date: 29-May-2024
https://doi.org/10.1109/ABC61795.2024.10651688

Index Terms

Spatial-Temporal Masked Autoencoder for Multi-Device Wearable Human Activity Recognition
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing theory, concepts and paradigms
      1. Mobile computing

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 7, Issue 4

December 2023

1613 pages

EISSN:2474-9567

DOI:10.1145/3640795

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Published: 12 January 2024

Published in IMWUT Volume 7, Issue 4

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Cited By

M JA ND SR SN BN SSingasani TVatambeti R(2024)Segment-Based Unsupervised Deep Learning for Human Activity Recognition using Accelerometer Data and SBOA based Channel Attention NetworksInternational Research Journal of Multidisciplinary Technovation10.54392/irjmt2461(1-16)Online publication date: 29-Oct-2024
https://doi.org/10.54392/irjmt2461
Logacjov A(2024)Self-supervised Learning for Accelerometer-based Human Activity Recognition: A SurveyProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997678:4(1-42)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699767
Haresamudram HEssa IPlötz T(2024)A Washing Machine is All You Need? On the Feasibility of Machine Data for Self-Supervised Human Activity Recognition2024 International Conference on Activity and Behavior Computing (ABC)10.1109/ABC61795.2024.10651688(1-10)Online publication date: 29-May-2024
https://doi.org/10.1109/ABC61795.2024.10651688
Contoli CFreschi VLattanzi E(2024)Energy-aware human activity recognition for wearable devices: A comprehensive reviewPervasive and Mobile Computing10.1016/j.pmcj.2024.101976104(101976)Online publication date: Nov-2024
https://doi.org/10.1016/j.pmcj.2024.101976

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