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Activity Recognition using WiFi Signatures

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dc.contributor.advisor Das, Samir en_US
dc.contributor.author Thirupugalsamy, Rajendran en_US
dc.contributor.other Department of Computer Science en_US
dc.date.accessioned 2012-05-15T18:07:08Z
dc.date.available 2012-05-15T18:07:08Z
dc.date.issued 1-Aug-10 en_US
dc.date.submitted Aug-10 en_US
dc.identifier Thirupugalsamy_grad.sunysb_0771M_10153.pdf en_US
dc.identifier.uri http://hdl.handle.net/1951/55649
dc.description.abstract Many high end mobile phones include a number of specialized (e.g., accelerometer, compass, GPS) and general purpose sensors (e.g., camera, microphone). The availability of these sensors has given rise to sensing applications in the areas of health care, gaming and social networks. Apart from its use in communication and localizing the WiFi"sensor" has not been used for much else. The presence of the WiFi sensor in high end mobile phones, laptops, netbooks, PDAs, etc., along with the widespread deployment of WiFi access points (APs) makes for a highly ubiquitous sensor. In this thesis we focus on how the WiFi sensor could be used for activity recognition. Our approach behind activity recognition is to observe a high level activity is indicated by a corresponding location, and WiFi signatures are a potential source of location information. Thus traces from WiFi sensors can be used to distinguish between locations and thus different activities.We present the techniques devised and implemented for recognizing the activities of a user based on WiFi sensor traces collected from mobile phones. Our techniques are a combination of activity transition detection and activity classification techniques. The key idea behind activity transition detection technique is to quantify the similarity between WiFi scans and cluster them together, thus detecting the transitions in activities. The method uses a unsupervised algorithm based on scale space analysis to detect the transitions. Our approach behind activity classification is based on the repeating activity patterns of an user. We use a supervised learning algorithm to classify the activities to one of the repeating activities labeled by the user.We present the client-server system design of our prototype implementation. The sensing client runs in the mobile phones and captures the WiFi signature. The server consists of tools for activity transition detection and classification. Towards the end we provide metrics for transition detection and classification algorithms based on experiments and realworld user data. en_US
dc.description.sponsorship Stony Brook University Libraries. SBU Graduate School in Department of Computer Science. Lawrence Martin (Dean of Graduate School). en_US
dc.format Electronic Resource en_US
dc.language.iso en_US en_US
dc.publisher The Graduate School, Stony Brook University: Stony Brook, NY. en_US
dc.subject.lcsh Computer Science en_US
dc.subject.other Activity Recognition en_US
dc.title Activity Recognition using WiFi Signatures en_US
dc.type Thesis en_US
dc.description.advisor Advisor(s): Samir Das. Committee Member(s): Jie Gao; Jennifer Wong. en_US
dc.mimetype Application/PDF en_US

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