The multitude of data generated by sensors available on users' mobile devices, combined with advances in machine learning techniques, support context-aware services in recognizing the current situation of a user (i.e., physical context) and optimizing the system's personalization features. However, context-awareness performances mainly depend on the accuracy of the context inference process, which is strictly tied to the availability of large-scale and labeled datasets. In this work, we present a framework developed to collect datasets containing heterogeneous sensing data derived from personal mobile devices. The framework has been used by 3 voluntary users for two weeks, generating a dataset with more than 36K samples and 1331 features. We also propose a lightweight approach to model the user context able to efficiently perform the entire reasoning process on the user mobile device. To this aim, we used six dimensionality reduction techniques in order to optimize the context classification. Experimental results on the generated dataset show that we achieve a 10x speed up and a feature reduction of more than 90% while keeping the accuracy loss less than 3%.

翻译：移动设备传感器产生的海量数据，结合机器学习技术的进步，支持情境感知服务识别用户当前状态（即物理情境）并优化系统的个性化功能。然而，情境感知性能主要取决于情境推理过程的准确性，而这又与大规模标注数据集的可用性密切相关。本研究提出一个框架，用于收集来自个人移动设备的异构传感数据。该框架由3名志愿用户使用两周，生成了包含超过36K样本和1331个特征的数据集。我们还提出一种轻量级用户情境建模方法，能够在用户移动设备上高效完成整个推理过程。为此，我们采用六种降维技术优化情境分类。在生成数据集上的实验结果表明，我们实现了10倍加速和超过90%的特征缩减，同时准确率损失控制在3%以内。