Abstract
We investigate the detection of activities and presence in the proximity of a mobile phone via the WiFi-RSSI at the phone. This is the first study to utilise RSSI in received packets at a mobile phone for the classification of activities. We discuss challenges that hinder the utilisation of WiFi PHY-layer information, recapitulate lessons learned and describe the hardware and software employed. Also, we discuss features for activity recognition (AR) based on RSSI and present two case studies. We make available our implemented tools for AR based on RSSI.
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Notes
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All tools are available at http://www.stephansigg.de/Mobiquitous2013.tar.gz
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A dictionary (or associative array is a python data type holding key-value pairs.
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Sender metadata: Type [station, access point, unknown]; Mac Address; SSID
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Receiver Operating Characteristic.
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For space constraints, we only depict results of the k-NN classifier. Results with other classifiers have been comparable.
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Acknowledgements
The authors would like to acknowledge funding by a fellowship within the Postdoc-Programme of the German Academic Exchange Service (DAAD).
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© 2014 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Sigg, S. et al. (2014). Passive, Device-Free Recognition on Your Mobile Phone: Tools, Features and a Case Study. In: Stojmenovic, I., Cheng, Z., Guo, S. (eds) Mobile and Ubiquitous Systems: Computing, Networking, and Services. MobiQuitous 2013. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 131. Springer, Cham. https://doi.org/10.1007/978-3-319-11569-6_34
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