research-article

TS2ACT: Few-Shot Human Activity Sensing with Cross-Modal Co-Learning

Authors:

Sanglu LuAuthors Info & Claims

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

Article No.: 188, Pages 1 - 22

https://doi.org/10.1145/3631445

Published: 12 January 2024 Publication History

Abstract

Human Activity Recognition (HAR) based on embedded sensor data has become a popular research topic in ubiquitous computing, which has a wide range of practical applications in various fields such as human-computer interaction, healthcare, and motion tracking. Due to the difficulties of annotating sensing data, unsupervised and semi-supervised HAR methods are extensively studied, but their performance gap to the fully-supervised methods is notable. In this paper, we proposed a novel cross-modal co-learning approach called TS2ACT to achieve few-shot HAR. It introduces a cross-modal dataset augmentation method that uses the semantic-rich label text to search for human activity images to form an augmented dataset consisting of partially-labeled time series and fully-labeled images. Then it adopts a pre-trained CLIP image encoder to jointly train with a time series encoder using contrastive learning, where the time series and images are brought closer in feature space if they belong to the same activity class. For inference, the feature extracted from the input time series is compared with the embedding of a pre-trained CLIP text encoder using prompt learning, and the best match is output as the HAR classification results. We conducted extensive experiments on four public datasets to evaluate the performance of the proposed method. The numerical results show that TS2ACT significantly outperforms the state-of-the-art HAR methods, and it achieves performance close to or better than the fully supervised methods even using as few as 1% labeled data for model training. The source codes of TS2ACT are publicly available on GitHub1.

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Cited By

Bock MVan Laerhoven KMoeller MKostakos VKay JHoang T(2024)Weak-Annotation of HAR Datasets using Vision Foundation ModelsProceedings of the 2024 ACM International Symposium on Wearable Computers10.1145/3675095.3676613(55-62)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675095.3676613
Raya SOrabi MAfyouni IAl Aghbari Z(2024)Multi-modal data clustering using deep learning: A systematic reviewNeurocomputing10.1016/j.neucom.2024.128348(128348)Online publication date: Aug-2024
https://doi.org/10.1016/j.neucom.2024.128348

Index Terms

TS2ACT: Few-Shot Human Activity Sensing with Cross-Modal Co-Learning
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools

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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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Copyright © 2024 ACM.

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Published: 12 January 2024

Published in IMWUT Volume 7, Issue 4

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Cited By

Bock MVan Laerhoven KMoeller MKostakos VKay JHoang T(2024)Weak-Annotation of HAR Datasets using Vision Foundation ModelsProceedings of the 2024 ACM International Symposium on Wearable Computers10.1145/3675095.3676613(55-62)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675095.3676613
Raya SOrabi MAfyouni IAl Aghbari Z(2024)Multi-modal data clustering using deep learning: A systematic reviewNeurocomputing10.1016/j.neucom.2024.128348(128348)Online publication date: Aug-2024
https://doi.org/10.1016/j.neucom.2024.128348

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