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Cited by 29Context-Aware Activity Recognition SystemsDomain Adaptation and Few-Shot LearningTime Series Analysis and Forecasting

Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning

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Qin, Xin, Wang, Jindong, Ma, Shuo, Lu, Wang, Zhu, Yongchun, Xie, Xing, Chen, Yiqiang·2023·Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
DOI 10.1145/3580305.3599360
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Abstract

Human activity recognition (HAR) is a time series classification task that focuses on identifying the motion patterns from human sensor readings. Adequate data is essential but a major bottleneck for training a generalizable HAR model, which assists customization and optimization of online web applications. However, it is costly in time and economy to collect large-scale labeled data in reality, i.e., the low-resource challenge. Meanwhile, data collected from different persons have distribution shifts due to different living habits, body shapes, age groups, etc. The low-resource and distribution shift challenges are detrimental to HAR when applying the trained model to new unseen subjects. In this paper, we propose a novel approach called Diverse and Discriminative representation Learning (DDLearn) for generalizable low-resource HAR. DDLearn simultaneously considers diversity and discrimination learning. With the constructed self-supervised learning task, DDLearn enlarges the data diversity and explores the latent activity properties. Then, we propose a diversity preservation module to preserve the diversity of learned features by enlarging the distribution divergence between the original and augmented domains. Meanwhile, DDLearn also enhances semantic discrimination by learning discriminative representations with supervised contrastive learning. Extensive experiments on three public HAR datasets demonstrate that our method significantly outperforms state-of-art methods by an average accuracy improvement of 9.5% under the low-resource distribution shift scenarios, while being a generic, explainable, and flexible framework. Code is available at: https://github.com/microsoft/robustlearn.

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Authors
Qin, X., Wang, J., Ma, S., Lu, W., Zhu, Y., Xie, X., & Chen, Y.
Affiliations
  • Chinese Academy of Sciences (CN)
  • Microsoft Research Asia (China) (CN)
  • Beijing Key Lab. of Mobile Com., CAS, Beijing, China
ORCID
Venue
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Publisher
ACM
Published
2023-08-04
Pages
1943-1953
DOI
10.1145/3580305.3599360
OpenAlex
W4385565385
Cited by
29
References
40
Citations by year
2026: 11 · 2025: 13 · 2024: 5
FWCI
3.11
Type
conference paper
Language
en
Open access
Licence
Locations
  • ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Funders
  • National Natural Science Foundation of China (61972383)
  • Chinese Academy of Sciences (XDA28040500)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
Keywords
human activity recognition, low-resource learning, domain shift, cross-subject generalization, self-supervised learning, supervised contrastive learning, diversity preservation, representation learning, wearable sensor data, time series classification

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