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. 2021 Jul 28;21(15):5121.
doi: 10.3390/s21155121.

Influence of the Antenna Orientation on WiFi-Based Fall Detection Systems

Jorge D Cardenas  1 , Carlos A Gutierrez  1 , Ruth Aguilar-Ponce  1
Affiliations

Influence of the Antenna Orientation on WiFi-Based Fall Detection Systems

Jorge D Cardenas et al. Sensors (Basel). .

Abstract

The growing elderly population living independently demands remote systems for health monitoring. Falls are considered recurring fatal events and therefore have become a global health problem. Fall detection systems based on WiFi radio frequency signals still have limitations due to the difficulty of differentiating the features of a fall from other similar activities. Additionally, the antenna orientation has not been taking into account as an influencing factor of classification performance. Therefore, we present in this paper an analysis of the classification performance in relation to the antenna orientation and the effects related to polarization and radiation pattern. Furthermore, the implementation of a device-free fall detection platform to collect empirical data on falls is shown. The platform measures the Doppler spectrum of a probe signal to extract the Doppler signatures generated by human movement and whose features can be used to identify falling events. The system explores two antenna polarization: horizontal and vertical. The accuracy reached by horizontal polarization is 92% with a false negative rate of 8%. Vertical polarization achieved 50% accuracy and false negatives rate.

Keywords: Doppler signatures; WiFi; device-free; fall detection; polarization.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
General diagram of a fall detection system based on radio frequency signals.
Figure 2
Figure 2
Spectrogram during a falling event.
Figure 3
Figure 3
Schematic propagation of an RF signal in a real test scenario.
Figure 4
Figure 4
Radiation pattern and polarization of antennas configuration in both scenarios.
Figure 5
Figure 5
Schematic of the test scenario.
Figure 6
Figure 6
Indoor environment for the test experimentation.
Figure 7
Figure 7
Principal components of the frequency of a Doppler spectrum.
Figure 8
Figure 8
Experimental results using VV scenario setup: (a) spectrogram, (b) sequence of spectrums.
Figure 9
Figure 9
Experimental results using HH scenario setup: (a) spectrogram, (b) sequence of spectrums.
Figure 10
Figure 10
Projection of the first and second principal components.
Figure 11
Figure 11
Confusion matrix of the SVM algorithm.
Figure 12
Figure 12
Comparison of Wifall, FallSense and VV scenario.
Figure 13
Figure 13
Comparison of Wifall, FallSense and HH scenario.
See this image and copyright information in PMC

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