LocateMe: Magnetic-fields-based indoor localization using smartphones
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
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“Smart” phones, such as G1 and iPhone, have made their way into people’s lives with more intelligence owing to the continually decreasing cost and increasing access to memory capability, processing power and network bandwidth. This work attempts to detect presence in different environments like office, conference, meeting and traveling outdoors, using audio sensors on the G1 phone. Specifically, a two step process involving classifying the background first and then detecting the number of people in an environment is followed. Using the Vector Quantization method, audio is classified into five predefined classes. A recognition rate ranging from 86% to 100%for individual classes and 91%overall recognition accuracy was obtained. For speaker change detection via Bayesian Information Criterion, about 79.4% of all 350 test audio clips was correctly categorized.