A Semisupervised Recurrent Convolutional Attention Model for Human Activity Recognition

Kaixuan Chen, Lina Yao, Dalin Zhang, Xianzhi Wang, Xiaojun Chang, Feiping Nie

科研成果: 期刊稿件文章同行评审

333 引用 (Scopus)

摘要

Recent years have witnessed the success of deep learning methods in human activity recognition (HAR). The longstanding shortage of labeled activity data inherently calls for a plethora of semisupervised learning methods, and one of the most challenging and common issues with semisupervised learning is the imbalanced distribution of labeled data over classes. Although the problem has long existed in broad real-world HAR applications, it is rarely explored in the literature. In this paper, we propose a semisupervised deep model for imbalanced activity recognition from multimodal wearable sensory data. We aim to address not only the challenges of multimodal sensor data (e.g., interperson variability and interclass similarity) but also the limited labeled data and class-imbalance issues simultaneously. In particular, we propose a pattern-balanced semisupervised framework to extract and preserve diverse latent patterns of activities. Furthermore, we exploit the independence of multi-modalities of sensory data and attentively identify salient regions that are indicative of human activities from inputs by our recurrent convolutional attention networks. Our experimental results demonstrate that the proposed model achieves a competitive performance compared to a multitude of state-of-the-art methods, both semisupervised and supervised ones, with 10% labeled training data. The results also show the robustness of our method over imbalanced, small training data sets.

源语言英语
文章编号8767027
页(从-至)1747-1756
页数10
期刊IEEE Transactions on Neural Networks and Learning Systems
31
5
DOI
出版状态已出版 - 5月 2020
已对外发布

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