利用低功耗传感器挖掘用户重要的行车路线
Mining Users' Significant Driving Routes with Low-power Sensors
摘要：
虽然在感知和识别用户的重要地点方面有大量的工作，但很少关注用户的重要路线。认识到这些常规行程，为开发新的应用程序打开了大门，如个性化的旅行提醒，并增强了用户的旅行体验。然而，传统位置感测技术（诸如基于GPS或WiFi的定位）的高能耗是通过智能手机进行被动且无处不在的路线感测的障碍。在本文中，我们提出了一个被动的路线传感框架，连续监测车辆用户只通过手机的陀螺仪和加速度计。该方法可以通过时间扭曲电话在运输中所经历的角速度来区分和识别用户所采取的各种路线，并且与车辆内的电话取向和位置、小弯路和交通状况无关。我们比较了这种方法的路线学习和识别能力与GPS轨迹分析，并表明它达到了类似的性能。此外，通过嵌入式协处理器（大多数新一代手机都有），它比GPS传感器节省了一个数量级的能源。
英文摘要：
While there is significant work on sensing and recognition of significant places for users, little attention has been given to users' significant routes. Recognizing these routine journeys, opens doors to the development of novel applications, like personalized travel alerts, and enhancement of user's travel experience. However, the high energy consumption of traditional location sensing technologies, such as GPS or WiFi based localization, is a barrier to passive and ubiquitous route sensing through smartphones. In this paper, we present a passive route sensing framework that continuously monitors a vehicle user solely through a phone's gyroscope and accelerometer. This approach can differentiate and recognize various routes taken by the user by time warping angular speeds experienced by the phone while in transit and is independent of phone orientation and location within the vehicle, small detours and traffic conditions. We compare the route learning and recognition capabilities of this approach with GPS trajectory analysis and show that it achieves similar performance. Moreover, with an embedded co-processor, common to most new generation phones, it achieves energy savings of an order of magnitude over the GPS sensor.
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