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. 2022 May 31;22(11):4201.
doi: 10.3390/s22114201.

Identity Recognition in Sanitary Facilities Using Invisible Electrocardiography

Aline Santos Silva  1   2 , Miguel Velhote Correia  1   3 , Francisco de Melo  2 , Hugo Plácido da Silva  2   4
Affiliations

Identity Recognition in Sanitary Facilities Using Invisible Electrocardiography

Aline Santos Silva et al. Sensors (Basel). .

Abstract

This article proposes a new method of identity recognition in sanitary facilities based on electrocardiography (ECG) signals. Our team previously proposed a novel approach of invisible ECG at the thighs using polymeric electrodes, leading to the creation of a proof-of-concept system integrated into a toilet seat. In this work, a biometrics pipeline was devised, which tested four different classifiers, varying the population from 2 to 17 subjects and simulating a residential environment. However, for this approach to be industrially viable, further optimization is required, particularly regarding electrode materials that are compatible with industrial processes. As such, we also explore the use of a conductive silicone material as electrodes, aiming at the industrial-scale production of a toilet seat capable of recording ECG data, without the need for body-worn devices. A desirable aspect when using such a system is matching the recorded data with the monitored user, ideally using a minimal sensor set, further reinforcing the relevance of user identification through ECG signals collected at the thighs. Our approach was evaluated against a reference device for a population of 17 healthy and pathological individuals, covering a wide age range (24-70 years). With the silicone composite, we were able to acquire signals in 100% of the sessions, with a mean heart rate deviation between a reference system and our experimental device of 2.82 ± 1.99 beats per minute (BPM). In terms of ECG waveform morphology, the best cases showed a Pearson correlation coefficient of 0.91 ± 0.06. For biometric detection, the best classifier was the Binary Convolutional Neural Network (BCNN), with an accuracy of 100% for a population of up to four individuals.

Keywords: biometrics; electrocardiography; identity recognition; invisibles; off-the-person; pervasive sensing; telemedicine.

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Conflict of interest statement

The authors declare no conflict of interest, although noting that H.P.d.S. is a co-founder of PLUX—Wireless Biosignals, A.S.S., the company that commercializes the BITalino device used as reference, and that the research group at IT—Instituto de Telecomunicações within which the work is being developed has generated a spin-off company (CardioID—Technologies, Lda.) which commercializes ECG-based biometric systems.

Figures

Figure 1
Figure 1
Prototype of the toilet seat, highlighting the electrodes’ positioning.
Figure 2
Figure 2
Electrode texture and geometries used in the scope of this work: (a) PLA electrode; and (b) Silicone electrode.
Figure 3
Figure 3
Experimental setup for skin-to-electrode impedance measurement.
Figure 4
Figure 4
Impedance between skin and electrode.
Figure 5
Figure 5
Experimental setup showing the electrode placement for the reference system (ECG REF: IN+, IN− & Ground) and the experimental electrodes location on the toilet seat (ECG EXP: A).
Figure 6
Figure 6
Methodology of an ECG biometric identification system.
Figure 7
Figure 7
Radar plot for the reference NNI series (extracted from ECG REF) and for the comparison NNI Series (extracted from ECG EXP).
Figure 8
Figure 8
Example heartbeat waveforms for a test subject: (A) EXP and (B) REF electrodes.
Figure 9
Figure 9
Example of the ECG EXP signal (green) obtained using the toilet seat compared to the signal obtained using the ECG REF (blue). The red and purple dots show the R-peaks detected by the algorithm used in this work.
Figure 10
Figure 10
Accuracy, Recall and Precision of the SVM, GaussianNB, 3-NN and BCNN classifiers for random template selection.
Figure 11
Figure 11
Accuracy, Recall and Precision of SVM, GaussianNB, 3-NN and BCNN classifiers for static characterization with 1/3 test (A), 2/9 test (B) and 1/6 test (C).
See this image and copyright information in PMC

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