Prism: 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
Prism: Mining Task-aware Domains in Non-i.i.d. IMU Data for Flexible User Perception
AI总结：
Prism通过挖掘任务感知的IMU数据域，实现移动设备上的灵活用户感知，提升预测精度并降低延迟。
摘要：
许多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，大多数系统只能在受控环境下（例如特定用户特定姿势）正常工作，限制了应用范围。为了在移动设备上实现无控制的在线预测，称为灵活的用户感知（FUP）问题，具有吸引力但困难。在本文中，我们提出了一种新的方案，称为Prism，可以在移动设备上获得高FUP精度。Prism的核心是发现嵌入在IMU数据集中的任务感知域，并在每个识别出的域上训练一个域感知模型。为此，我们设计了一个期望最大化（EM）算法来估计与特定下游感知任务相关的潜在域。最后，通过在特征空间中比较测试样本和所有识别出的域，可以自动选择最佳拟合模型。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可以以低延迟实现最佳的FUP性能。
英文摘要：
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.
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。
