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Teach Your WiFi-Device: Recognise Simultaneous Activities and Gestures from Time-Domain RF-Features

Teach Your WiFi-Device: Recognise Simultaneous Activities and Gestures from Time-Domain RF-Features

Stephan Sigg, Shuyu Shi, Yusheng Ji
Copyright: © 2014 |Volume: 6 |Issue: 1 |Pages: 15
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781466652354|DOI: 10.4018/ijaci.2014010102
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MLA

Sigg, Stephan, et al. "Teach Your WiFi-Device: Recognise Simultaneous Activities and Gestures from Time-Domain RF-Features." IJACI vol.6, no.1 2014: pp.20-34. http://doi.org/10.4018/ijaci.2014010102

APA

Sigg, S., Shi, S., & Ji, Y. (2014). Teach Your WiFi-Device: Recognise Simultaneous Activities and Gestures from Time-Domain RF-Features. International Journal of Ambient Computing and Intelligence (IJACI), 6(1), 20-34. http://doi.org/10.4018/ijaci.2014010102

Chicago

Sigg, Stephan, Shuyu Shi, and Yusheng Ji. "Teach Your WiFi-Device: Recognise Simultaneous Activities and Gestures from Time-Domain RF-Features," International Journal of Ambient Computing and Intelligence (IJACI) 6, no.1: 20-34. http://doi.org/10.4018/ijaci.2014010102

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Abstract

The authors consider two untackled problems in RF-based activity recognition: the distinction of simultaneously conducted activities of individuals and the recognition of gestures from purely time-domain-based features. Recognition is based on a single antenna system. This is important for the application in end-user devices which are usually single-antenna systems and have seldom access to more sophisticated, e.g. frequency-based features. In case studies with software defined radio nodes utilised in an active, device-free activity recognition (DFAR) system, the authors observe a good recognition accuracy for the detection of multiple simultaneously conducted activities with two and more receive devices. Four gestures and two baseline situations are distinguished with good accuracy in a second case study.

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