识别多模态时间序列感官数据的最佳数据增强技术:一个框架

Information Pub Date : 2024-06-11 DOI:10.3390/info15060343
Nazish Ashfaq, Muhammad Hassan Khan, M. Nisar
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摘要

最近,研究界对从可穿戴设备中的运动传感器获取的连续时间数据表现出了浓厚的兴趣。这些数据有助于对医疗保健、体育和监控等许多应用领域中的不同人类活动进行分类和分析。文献介绍了大量深度学习模型,这些模型旨在从时间感官输入中获得合适的特征表示。然而,要充分训练深度网络,大量标注训练数据的存在至关重要。然而,来自可穿戴设备的数据数量庞大,但由于缺乏标签而无效,这阻碍了我们以最佳效率训练模型的能力。这种现象导致模型出现过拟合。本文提出的研究有两方面的贡献:首先,它对 15 种不同的增强策略进行了系统评估,以解决在分类任务中起关键作用的标记数据不足问题。其次,它引入了一种自动特征学习技术,提出了一种多分支混合 Conv-LSTM 网络,利用不同可穿戴智能设备的多模态数据对人类日常生活活动进行分类。本研究的目的是引入一种集合深度模型,以有效捕捉时间数据中错综复杂的模式和相互依存关系。术语 "集合模型 "是指融合不同的深度模型,目的是利用这些模型自身的优势和能力,开发出更强大、更高效的解决方案。在两个著名的基准数据集上使用数据增强技术对集合模型进行了全面评估:CogAge 和 UniMiB-SHAR。拟议的网络采用了一系列数据增强方法,以提高原子活动和复合活动的准确性。这使得复合活动的准确性提高了 5%,原子活动的准确性提高了 30%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of Optimal Data Augmentation Techniques for Multimodal Time-Series Sensory Data: A Framework
Recently, the research community has shown significant interest in the continuous temporal data obtained from motion sensors in wearable devices. These data are useful for classifying and analysing different human activities in many application areas such as healthcare, sports and surveillance. The literature has presented a multitude of deep learning models that aim to derive a suitable feature representation from temporal sensory input. However, the presence of a substantial quantity of annotated training data is crucial to adequately train the deep networks. Nevertheless, the data originating from the wearable devices are vast but ineffective due to a lack of labels which hinders our ability to train the models with optimal efficiency. This phenomenon leads to the model experiencing overfitting. The contribution of the proposed research is twofold: firstly, it involves a systematic evaluation of fifteen different augmentation strategies to solve the inadequacy problem of labeled data which plays a critical role in the classification tasks. Secondly, it introduces an automatic feature-learning technique proposing a Multi-Branch Hybrid Conv-LSTM network to classify human activities of daily living using multimodal data of different wearable smart devices. The objective of this study is to introduce an ensemble deep model that effectively captures intricate patterns and interdependencies within temporal data. The term “ensemble model” pertains to fusion of distinct deep models, with the objective of leveraging their own strengths and capabilities to develop a solution that is more robust and efficient. A comprehensive assessment of ensemble models is conducted using data-augmentation techniques on two prominent benchmark datasets: CogAge and UniMiB-SHAR. The proposed network employs a range of data-augmentation methods to improve the accuracy of atomic and composite activities. This results in a 5% increase in accuracy for composite activities and a 30% increase for atomic activities.
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