论文标题
基于变压器的模型，以应对人类活动识别中的异质环境
Transformer-based Models to Deal with Heterogeneous Environments in Human Activity Recognition
论文作者
论文摘要
使用对从设备的惯性测量单元收集的数据进行训练的神经模型，已证明了移动设备上的人类活动识别（HAR）。这些模型已经使用卷积神经网络（CNN），长期短期记忆（LSTMS），变压器或这些组合来实现具有实时性能的最新结果。但是，在现实情况下，这些方法尚未得到广泛评估，在现实情况下，输入数据可能与培训数据不同。本文重点介绍了机器学习应用程序中数据异质性的问题，以及如何阻碍其在普遍环境中的部署。为了解决这个问题，我们提出并公开发布了两个传感器变压器架构的代码，称为Hart和Mobilehart，用于人类活动识别变压器。我们在几个公开数据集上的实验表明，这些HART体系结构的表现优于先前的浮点操作和参数少于传统变压器。结果还表明，它们更适合移动位置或设备品牌的变化，因此更适合在现实生活中遇到的异质环境。最后，源代码已公开可用。
Human Activity Recognition (HAR) on mobile devices has been demonstrated to be possible using neural models trained on data collected from the device's inertial measurement units. These models have used Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTMs), Transformers or a combination of these to achieve state-of-the-art results with real-time performance. However, these approaches have not been extensively evaluated in real-world situations where the input data may be different from the training data. This paper highlights the issue of data heterogeneity in machine learning applications and how it can hinder their deployment in pervasive settings. To address this problem, we propose and publicly release the code of two sensor-wise Transformer architectures called HART and MobileHART for Human Activity Recognition Transformer. Our experiments on several publicly available datasets show that these HART architectures outperform previous architectures with fewer floating point operations and parameters than conventional Transformers. The results also show they are more robust to changes in mobile position or device brand and hence better suited for the heterogeneous environments encountered in real-life settings. Finally, the source code has been made publicly available.
