基于传感器的多视角融合Transformer人体行为识别
Multi-View Fusion Transformer for Sensor-Based Human Activity Recognition
摘要：
基于传感器的人类活动识别（HAR）作为泛在计算和机器学习的基础问题，近年来引起了广泛关注并取得了长足进展。HAR的目标是根据从多模态传感器（如加速度计和陀螺仪）收集的丰富时间序列数据的可用性来识别人类活动。然而，最近的深度学习方法专注于数据的一个视图，即，时间视图，而浅层方法倾向于利用手工特征进行识别，例如，统计观点。在本文中，提取一个更好的功能，以提高性能，我们提出了一种新的方法，即多视图融合Transformer（MVFT）以及一种新的注意力机制。首先，MVFT编码三个信息视图，即，时间、频繁和统计视图以生成多视图特征。其次，新的注意机制揭示内部和跨视图的线索，催化三个视图之间的相互作用，详细的关系建模。此外，在两个数据集上的大量实验表明，我们的方法优于几个国家的最先进的方法。
英文摘要：
As a fundamental problem in ubiquitous computing and machine learning, sensor-based human activity recognition (HAR) has drawn extensive attention and made great progress in recent years. HAR aims to recognize human activities based on the availability of rich time-series data collected from multi-modal sensors such as accelerometers and gyroscopes. However, recent deep learning methods are focusing on one view of the data, i.e., the temporal view, while shallow methods tend to utilize the hand-craft features for recognition, e.g., the statistics view. In this paper, to extract a better feature for advancing the performance, we propose a novel method, namely multi-view fusion transformer (MVFT) along with a novel attention mechanism. First, MVFT encodes three views of information, i.e., the temporal, frequent, and statistical views to generate multi-view features. Second, the novel attention mechanism uncovers inner- and cross-view clues to catalyze mutual interactions between three views for detailed relation modeling. Moreover, extensive experiments on two datasets illustrate the superiority of our methods over several state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
