| dc.identifier.uri | http://hdl.handle.net/1951/55649 | |
| dc.identifier.uri | http://hdl.handle.net/11401/72679 | |
| dc.description.sponsorship | This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. | en_US |
| dc.format | Monograph | |
| dc.format.medium | Electronic Resource | en_US |
| dc.language.iso | en_US | |
| dc.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dc.type | Thesis | |
| dcterms.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. | |
| dcterms.available | 2012-05-15T18:07:08Z | |
| dcterms.available | 2015-04-24T14:53:12Z | |
| dcterms.contributor | Das, Samir | en_US |
| dcterms.contributor | Jie Gao | en_US |
| dcterms.contributor | Jennifer Wong. | en_US |
| dcterms.creator | Thirupugalsamy, Rajendran | |
| dcterms.dateAccepted | 2012-05-15T18:07:08Z | |
| dcterms.dateAccepted | 2015-04-24T14:53:12Z | |
| dcterms.dateSubmitted | 2012-05-15T18:07:08Z | |
| dcterms.dateSubmitted | 2015-04-24T14:53:12Z | |
| dcterms.description | Department of Computer Science | en_US |
| dcterms.format | Monograph | |
| dcterms.format | Application/PDF | en_US |
| dcterms.identifier | Thirupugalsamy_grad.sunysb_0771M_10153.pdf | en_US |
| dcterms.identifier | http://hdl.handle.net/1951/55649 | |
| dcterms.identifier | http://hdl.handle.net/11401/72679 | |
| dcterms.issued | 2010-08-01 | |
| dcterms.language | en_US | |
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Previous issue date: 1 | en |
| dcterms.provenance | Made available in DSpace on 2015-04-24T14:53:12Z (GMT). No. of bitstreams: 3
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Thirupugalsamy_grad.sunysb_0771M_10153.pdf: 1013375 bytes, checksum: 7e55f31f7328c8d342b60648e528741b (MD5)
Previous issue date: 1 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | Activity Recognition | |
| dcterms.subject | Computer Science | |
| dcterms.title | Activity Recognition using WiFi Signatures | |
| dcterms.type | Thesis | |
