Darwin phones: the evolution of sensing and inference on mobile phones

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 sensor-enabled mobile phones (i.e., smartphones). Darwin tackles three key sensing and inference challenges that are barriers to mass-scale adoption of mobile phone sensing applications: (i) the human-burden of training classifiers, (ii) the ability to perform reliably in different environments (e.g., indoor, outdoor) and (iii) the ability to scale to a large number of phones without jeopardizing the"phone experience" (e.g., usability and battery lifetime). Darwin is a collaborative reasoning framework built on three concepts: classifier/model evolution, model pooling, and collaborative inference. To the best of our knowledge D...

• Type: Conference
• Year: 2010
• Where: MOBISYS
• Authors: Emiliano Miluzzo, Cory Cornelius, Ashwin Ramaswamy, Tanzeem Choudhury, Zhigang Liu, Andrew T. Campbell
• Keywords: Computer Networks, Machine Learning Techniques, Mobile Phones, MOBISYS 2010, Sensing