The Telepathic Phone: Frictionless Activity Recognition from WiFi-RSSI. Sigg, S., Blanke, U., & Troester, G. In IEEE International Conference on Pervasive Computing and Communications (PerCom), of PerCom '14, 2014.
The Telepathic Phone: Frictionless Activity Recognition from WiFi-RSSI [pdf]Slides  doi  abstract   bibtex   
We investigate the use of WiFi Received Signal Strength Information (RSSI) at a mobile phone for the recognition of situations, activities and gestures. In particular, we propose a device-free and passive activity recognition system that does not require any device carried by the user and uses ambient signals. We discuss challenges and lessons learned for the design of such a system on a mobile phone and propose appropriate features to extract activity characteristics from RSSI. We demonstrate the feasibility of recognising activities, gestures and environmental situations from RSSI obtained by a mobile phone. The case studies were conducted over a period of about two months in which about 12 hours of continuous RSSI data was sampled, in two countries and with 11 participants in total. Results demonstrate the potential to utilise RSSI for the extension of the environmental perception of a mobile device as well as for the interaction with touch-free gestures. The system achieves an accuracy of 0.51 while distinguishing as many as 11 gestures and can reach 0.72 on average for four more disparate ones.
@inproceedings{Sigg_2014_telepathic,
author={Stephan Sigg and Ulf Blanke and Gerhard Troester},
title="The Telepathic Phone: Frictionless Activity Recognition from WiFi-RSSI",
booktitle = {IEEE International Conference on Pervasive Computing and Communications (PerCom)},
series = {PerCom '14},
year={2014},
doi={http://dx.doi.org/10.1109/PerCom.2014.6813955},
abstract={We investigate the use of WiFi Received Signal Strength Information (RSSI) at a mobile phone for the recognition of situations, activities and gestures. In particular, we propose a device-free and passive activity recognition system that does not require any device carried by the user and uses ambient signals. We discuss challenges and lessons learned for the design of such a system on a mobile phone and propose appropriate features to extract activity characteristics from RSSI. We demonstrate the feasibility of recognising activities, gestures and environmental situations from RSSI obtained by a mobile phone. The case studies were conducted over a period of about two months in which about 12 hours of continuous RSSI data was sampled, in two countries and with 11 participants in total. Results demonstrate the potential to utilise RSSI for the extension of the environmental perception of a mobile device as well as for the interaction with touch-free gestures. The system achieves an accuracy of 0.51 while distinguishing as many as 11 gestures and can reach 0.72 on average for four more disparate ones.},
url_Slides={http://www.stephansigg.de/stephan/slides/Talk_PerCom_1403_Sigg-small.pdf},
group = {ambience}}
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