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

Beitrag bei einer Tagung
(Originalarbeit)


Details zur Publikation

Autor(en): Schellenberger S, Shi K, Steigleder T, Michler F, Lurz F, Weigel R, Kölpin A
Jahr der Veröffentlichung: 2018
Seitenbereich: 1465-1467
Sprache: Englisch


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.


FAU-Autoren / FAU-Herausgeber

Lurz, Fabian
Lehrstuhl für Technische Elektronik
Michler, Fabian
Lehrstuhl für Technische Elektronik
Schellenberger, Sven
Lehrstuhl für Technische Elektronik
Shi, Kilin
Lehrstuhl für Technische Elektronik
Weigel, Robert Prof. Dr.-Ing.
Lehrstuhl für Technische Elektronik


Autor(en) der externen Einrichtung(en)
Brandenburgische Technische Universität Cottbus-Senftenberg (BTU)


Zitierweisen

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.

BibTeX: 

Zuletzt aktualisiert 2019-16-04 um 22:23