PMID- 35684820
OWN - NLM
STAT- MEDLINE
DCOM- 20220613
LR  - 20220716
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 22
IP  - 11
DP  - 2022 May 31
TI  - Identity Recognition in Sanitary Facilities Using Invisible Electrocardiography.
LID - 10.3390/s22114201 [doi]
LID - 4201
AB  - 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.
FAU - Silva, Aline Santos
AU  - Silva AS
AUID- ORCID: 0000-0001-7588-1349
AD  - FEUP-Faculdade de Engenharia da Universidade do Porto, 4200-465 Porto, Portugal.
AD  - IT-Instituto de Telecomunicacoes, 1049-001 Lisboa, Portugal.
FAU - Correia, Miguel Velhote
AU  - Correia MV
AUID- ORCID: 0000-0001-6065-9358
AD  - FEUP-Faculdade de Engenharia da Universidade do Porto, 4200-465 Porto, Portugal.
AD  - INESC TEC-Institute for Systems and Computer Engineering, Technology and Science, 
      4200-365 Porto, Portugal.
FAU - de Melo, Francisco
AU  - de Melo F
AUID- ORCID: 0000-0003-1919-4661
AD  - IT-Instituto de Telecomunicacoes, 1049-001 Lisboa, Portugal.
FAU - da Silva, Hugo Placido
AU  - da Silva HP
AUID- ORCID: 0000-0001-6764-8432
AD  - IT-Instituto de Telecomunicacoes, 1049-001 Lisboa, Portugal.
AD  - Department of Bioengineering, Instituto Superior Tecnico, 1049-001 Lisboa, 
      Portugal.
LA  - eng
PT  - Journal Article
DEP - 20220531
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
RN  - 0 (Silicones)
SB  - IM
MH  - Adult
MH  - Aged
MH  - *Electrocardiography/methods
MH  - Heart Rate/physiology
MH  - Humans
MH  - Identity Recognition
MH  - Middle Aged
MH  - *Signal Processing, Computer-Assisted
MH  - Silicones
MH  - Young Adult
PMC - PMC9185406
OTO - NOTNLM
OT  - biometrics
OT  - electrocardiography
OT  - identity recognition
OT  - invisibles
OT  - off-the-person
OT  - pervasive sensing
OT  - telemedicine
COIS- 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 Telecomunicacoes within which the work is being developed has 
      generated a spin-off company (CardioID-Technologies, Lda.) which commercializes 
      ECG-based biometric systems.
EDAT- 2022/06/11 06:00
MHDA- 2022/06/14 06:00
PMCR- 2022/05/31
CRDT- 2022/06/10 01:26
PHST- 2022/03/21 00:00 [received]
PHST- 2022/05/25 00:00 [revised]
PHST- 2022/05/26 00:00 [accepted]
PHST- 2022/06/10 01:26 [entrez]
PHST- 2022/06/11 06:00 [pubmed]
PHST- 2022/06/14 06:00 [medline]
PHST- 2022/05/31 00:00 [pmc-release]
AID - s22114201 [pii]
AID - sensors-22-04201 [pii]
AID - 10.3390/s22114201 [doi]
PST - epublish
SO  - Sensors (Basel). 2022 May 31;22(11):4201. doi: 10.3390/s22114201.