标题： 棱镜：在非独立同分布的IMU数据中挖掘任务感知域，以实现灵活的用户感知 显示英文标题

标题： Prism: Mining Task-aware Domains in Non-i.i.d. IMU Data for Flexible User Perception

摘要： 广泛范围的用户感知应用利用惯性测量单元（IMU）数据进行在线预测。 然而，由于从移动设备收集的IMU数据的非独立同分布性质，大多数系统仅在受控环境中表现良好（例如，针对特定用户在特定姿势下），限制了应用场景。 在移动设备上实现不受控的在线预测，称为灵活用户感知（FUP）问题，具有吸引力但难度较大。 在本文中，我们提出了一种新方案，称为Prism，该方案可以在移动设备上获得高FUP准确性。 Prism的核心是发现嵌入在IMU数据集中的任务感知领域，并在每个识别的领域上训练一个领域感知模型。 为此，我们设计了一种期望最大化（EM）算法，以针对特定的下游感知任务估计潜在领域。 最后，可以通过比较测试样本和特征空间中所有识别的领域，自动选择最佳匹配模型进行使用。 我们在各种移动设备上实现了Prism，并进行了广泛的实验。 结果表明，Prism可以在低延迟的情况下实现最佳的FUP性能。 显示英文摘要

摘要： A wide range of user perception applications leverage inertial measurement unit (IMU) data for online prediction. However, restricted by the non-i.i.d. nature of IMU data collected from mobile devices, most systems work well only in a controlled setting (e.g., for a specific user in particular postures), limiting application scenarios. To achieve uncontrolled online prediction on mobile devices, referred to as the flexible user perception (FUP) problem, is attractive but hard. In this paper, we propose a novel scheme, called Prism, which can obtain high FUP accuracy on mobile devices. The core of Prism is to discover task-aware domains embedded in IMU dataset, and to train a domain-aware model on each identified domain. To this end, we design an expectation-maximization (EM) algorithm to estimate latent domains with respect to the specific downstream perception task. Finally, the best-fit model can be automatically selected for use by comparing the test sample and all identified domains in the feature space. We implement Prism on various mobile devices and conduct extensive experiments. Results demonstrate that Prism can achieve the best FUP performance with a low latency.