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Unformatted text preview: Darwin Phones: the Evolution of Sensing and Inference on Mobile Phones Emiliano Miluzzo † , Cory T. Cornelius † , Ashwin Ramaswamy † , Tanzeem Choudhury † , Zhigang Liu § , Andrew T. Campbell † † Computer Science, Dartmouth College, Hanover, NH, USA § Nokia Research Center, 955 Page Mill Road, Palo Alto, CA, USA ABSTRACT We present Darwin, an enabling technology for mobile phone sensing that combines collaborative sensing and classifica- tion techniques to reason about human behavior and con- text on mobile phones. Darwin advances mobile phone sens- ing through the deployment of eﬃcient but sophisticated machine learning techniques specifically designed to run di- rectly 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 clas- sifiers, (ii) the ability to perform reliably in different envi- ronments (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 Darwin is the first system that applies distributed machine learning techniques and collaborative inference concepts to mobile phones. We implement the Darwin system on the Nokia N97 and Ap- ple iPhone. While Darwin represents a general framework applicable to a wide variety of emerging mobile sensing ap- plications, we implement a speaker recognition application and an augmented reality application to evaluate the ben- efits of Darwin. We show experimental results from eight individuals carrying Nokia N97s and demonstrate that Dar- win improves the reliability and scalability of the proof-of- concept speaker recognition application without additional burden to users. Categories and Subject Descriptors: C.3 [Special-Purpose and Application-Based Systems] Real-time and embedded systems General Terms: Algorithms, Design, Experimentation, Human Factors, Measurement, Performance Keywords: Mobile Sensing Systems, Distributed Machine Learning, Collaborative Inference, Mobile Phones Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee....
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This note was uploaded on 08/25/2011 for the course EEL 6788 taught by Professor Boloni,l during the Spring '08 term at University of Central Florida.
- Spring '08