FLOW：融合与打乱全局与局部视角的跨用户IMU人体活动识别
FLOW: Fusing and Shuffling Global and Local Views for Cross-User Human Activity Recognition with IMUs
AI总结：
针对IMU人体活动识别中因佩戴方式导致的跨用户数据分布差异问题，提出提取全局视角表示并设计基于打乱机制的多视角监督网络MVFNet融合全局与局部特征，在跨用户HAR任务中超越现有最先进方法。
摘要：
惯性测量单元（IMU）传感器因其便携性、高能效以及日益增长的研究兴趣，被广泛应用于人体活动识别（HAR）。然而，IMU-HAR模型面临的一个重大挑战是，如何在不同用户之间实现稳健的泛化性能。这一局限性源于个体用户之间数据分布的显著差异。造成这种分布差异的一个主要原因是IMU传感器数据在局部坐标系中的表示，这种表示容易受到用户佩戴IMU时微小变化的影响。为了解决这一问题，我们提出了一种新颖的方法，基于IMU数据的特征提取全局视角表示，从而有效缓解由佩戴方式引起的数据分布差异。为了验证全局视角表示的有效性，我们将全局视角和局部视角数据同时输入模型进行实验。结果表明，在跨用户实验中，全局视角数据显著优于局部视角数据。此外，我们提出了一种基于打乱机制的多视角监督网络（MVFNet），以有效融合局部视角和全局视角数据。该网络通过视角划分和视角打乱来监督每个视角的特征提取，从而尽可能避免模型忽略重要特征。在OPPORTUNITY和PAMAP2数据集上进行的大量实验表明，所提出的算法在跨用户HAR任务中优于当前最先进的方法。
英文摘要：
Inertial Measurement Unit (IMU) sensors are widely employed for Human Activity Recognition (HAR) due to their portability, energy efficiency, and growing research interest. However, a significant challenge for IMU-HAR models is achieving robust generalization performance across diverse users. This limitation stems from substantial variations in data distribution among individual users. One primary reason for this distribution disparity lies in the representation of IMU sensor data in the local coordinate system, which is susceptible to subtle user variations during IMU wearing. To address this issue, we propose a novel approach that extracts a global view representation based on the characteristics of IMU data, effectively alleviating the data distribution discrepancies induced by wearing styles. To validate the efficacy of the global view representation, we fed both global and local view data into model for experiments. The results demonstrate that global view data significantly outperforms local view data in cross-user experiments. Furthermore, we propose a Multi-view Supervised Network (MVFNet) based on Shuffling to effectively fuse local view and global view data. It supervises the feature extraction of each view through view division and view shuffling, so as to avoid the model ignoring important features as much as possible. Extensive experiments conducted on OPPORTUNITY and PAMAP2 datasets demonstrate that the proposed algorithm outperforms the current state-of-the-art methods in cross-user HAR.
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。
