Support Vector Machine-Based Instantaneous Presence Detection for Continuous Wave Radar Systems

Schellenberger S, Shi K, Steigleder T, Michler F, Lurz F, Weigel R, Kölpin A (2018)

Publication Language: English

Publication Status: Accepted

Publication Type: Conference contribution, Original article

Future Publication Type: Conference contribution

Publication year: 2018

Pages Range: 1465-1467

Event location: Kyoto

DOI: 10.23919/APMC.2018.8617181

Abstract

Instantaneous detection of missing vital signs at inpatient beds enables fast intervention for cardiac arrests.
Using a 24 GHz bistatic radar, a fast presence detection based on a support vector machine (SVM) classifer is realized. Large body motions or even small distance deviations, such as movement of the chest induced by heartbeat or breathing, are distinguishable from the measured noise of an unoccupied bed. For classifcation two features are calculated based on windowed I and Q data. Performance is evaluated by varying window sizes from 0.2 ... 1.5 s for feature calculation and training of the SVM classifer. In the resting scenario an accuracy of 99.2% and F1-score of 99.1% with windows of 0.2 s is achieved.

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How to cite

APA:

Schellenberger, S., Shi, K., Steigleder, T., Michler, F., Lurz, F., Weigel, R., & Kölpin, A. (2018). Support Vector Machine-Based Instantaneous Presence Detection for Continuous Wave Radar Systems. In Proceedings of the 2018 Asia-Pacific Microwave Conference (pp. 1465-1467). Kyoto, JP.

MLA:

Schellenberger, Sven, et al. "Support Vector Machine-Based Instantaneous Presence Detection for Continuous Wave Radar Systems." Proceedings of the 2018 Asia-Pacific Microwave Conference, Kyoto 2018. 1465-1467.

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