通过挖掘非独立同分布的IMU数据中的任务相关域，实现移动端灵活用户感知。
Prism: Mining Task-aware Domains in Non-i.i.d. IMU Data for Flexible User Perception
- 基于期望最大化算法识别任务相关的潜在数据域
- 在多个域上训练模型并按特征空间匹配选择最优模型
- 适用于移动设备上的实时用户感知，精度高且延迟低
大量用户感知应用依赖移动设备采集的惯性测量单元（IMU）数据进行在线预测。然而，受限于移动设备上IMU数据的非独立同分布特性，现有系统仅在受控场景（如特定用户、特定姿势）下表现良好，限制了实际应用。为实现不受控环境下的移动端在线预测，即灵活用户感知（FUP）问题，具有吸引力但极具挑战性。本文提出一种新方案Prism，可在移动端实现高精度的FUP。其核心思想是挖掘嵌入在IMU数据集中的任务相关域，并在每个识别出的域上训练域感知模型。为此，我们设计了一种期望最大化（EM）算法，以针对特定下游感知任务估计潜在域。最终，通过比较测试样本与所有已识别域在特征空间中的相似性，自动选择最适配的模型。我们在多种移动设备上实现了Prism，并进行了广泛实验。结果表明，Prism能以低延迟达到最优的FUP性能。
原文摘要 · Abstract (English)
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.
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