利用卷积神经网络进行小波域人类活动识别

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Ambient Intelligence and Smart Environments Pub Date : 2023-11-16 DOI:10.3233/ais-230174
Mohammad Tavakkoli, Ehsan Nazerfard, Maryam Amirmazlaghani
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引用次数: 0

摘要

人类活动识别(HAR)是人机交互研究的一个重要领域。尽管之前在这一领域做出了很多努力,但仍然需要更准确、更稳健的方法来处理来自不同传感器的时间序列数据。在本研究中,我们提出了一种新方法,利用小波变换生成图像,提取记录信号的时频特征。我们的方法采用卷积神经网络(CNN)进行特征提取和活动识别,并采用一种新的损失函数,为样本生成更密集的表示,从而提高模型对未见样本的泛化能力。为了评估我们提出的方法的有效性,我们在多个公开数据集上进行了实验。结果表明,我们的方法在活动分类准确性方面优于之前的方法。具体来说,我们的方法实现了更高的准确率,并在真实世界环境中表现出更好的鲁棒性。总之,我们提出的方法解决了从不同传感器记录的时间序列数据中准确、稳健地识别活动这一研究空白。我们的研究成果有望提高人类活动识别系统在现实世界应用中的准确性和鲁棒性。
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Wavelet-domain human activity recognition utilizing convolutional neural networks
Human activity recognition (HAR) is a crucial area of research in human-computer interaction. Despite previous efforts in this field, there is still a need for more accurate and robust methods that can handle time-series data from different sensors. In this study, we propose a novel method that generates an image using wavelet transform to extract time-frequency features of the recorded signal. Our method employs convolutional neural networks (CNNs) for feature extraction and activity recognition, and a new loss function that produces denser representations for samples, improving the model’s generalization on unseen samples. To evaluate the effectiveness of our proposed method, we conducted experiments on multiple publicly available data sets. Our results demonstrate that our method outperforms previous methods in terms of activity classification accuracy. Specifically, our method achieves higher accuracy rates and demonstrates improved robustness in real-world settings. Overall, our proposed method addresses the research gap of accurate and robust activity recognition from time-series data recorded from different sensors. Our findings have the potential to improve the accuracy and robustness of human activity recognition systems in real-world applications.
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来源期刊
Journal of Ambient Intelligence and Smart Environments
Journal of Ambient Intelligence and Smart Environments COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
4.30
自引率
17.60%
发文量
23
审稿时长
>12 weeks
期刊介绍: The Journal of Ambient Intelligence and Smart Environments (JAISE) serves as a forum to discuss the latest developments on Ambient Intelligence (AmI) and Smart Environments (SmE). Given the multi-disciplinary nature of the areas involved, the journal aims to promote participation from several different communities covering topics ranging from enabling technologies such as multi-modal sensing and vision processing, to algorithmic aspects in interpretive and reasoning domains, to application-oriented efforts in human-centered services, as well as contributions from the fields of robotics, networking, HCI, mobile, collaborative and pervasive computing. This diversity stems from the fact that smart environments can be defined with a variety of different characteristics based on the applications they serve, their interaction models with humans, the practical system design aspects, as well as the multi-faceted conceptual and algorithmic considerations that would enable them to operate seamlessly and unobtrusively. The Journal of Ambient Intelligence and Smart Environments will focus on both the technical and application aspects of these.
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