Learn More
Supporting continuous sensing applications on mobile phones is challenging because of the resource demands of long-term sensing, inference and communication algorithms. We present the design, implementation and evaluation of the <i>Jigsaw continuous sensing engine</i>, which balances the performance needs of the application and the resource demands of(More)
We present Darwin, an enabling technology for mobile phone sensing that combines collaborative sensing and classification techniques to reason about human behavior and context on mobile phones. Darwin advances mobile phone sensing through the deployment of efficient but sophisticated machine learning techniques specifically designed to run directly on(More)
Trip detection is a fundamental issue in many context-sensitive information services on mobile devices. It aims to automatically recognize significant places and trips between them. The key challenge is how to minimize energy consumption while maintaining high accuracy. Previous works that use GPS/WiFi sampling are accurate but energy efficiency is low and(More)