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

• URL: http://arxiv.org/abs/2306.04641v2
• Date: Fri, 9 Jun 2023 02:46:34 GMT
• Title: Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning
• Authors: Xin Qin, Jindong Wang, Shuo Ma, Wang Lu, Yongchun Zhu, Xing Xie, Yiqiang Chen
• Abstract summary: Human activity recognition (HAR) is a time series classification task that focuses on identifying the motion patterns from human sensor readings. We propose a novel approach called Diverse and Discriminative representation Learning (DDLearn) for generalizable lowresource HAR. Our method significantly outperforms state-of-art methods by an average accuracy improvement of 9.5%.
• Score: 24.36351102003414
• License: http://creativecommons.org/licenses/by/4.0/
• 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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