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Special Issue "Invisibles for Biomedical Sensing"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (10 April 2023) | Viewed by 2331

Special Issue Editors

1. I3N & Physics Department of the Aveiro University, 3810-193 Aveiro, Portugal
2. Instituto de Telecomunicações, 3810-193 Aveiro, Portugal
Interests: optical fiber sensors; e-Health platforms; structural health monitoring; biosensing
Special Issues, Collections and Topics in MDPI journals
Instituto de Telecomunicações, Technical University Lisbon, 1049-001 Lisbon, Portugal
Interests: biomedical instrumentation; biosignal acquisition; biosignal processing; machine learning; system engineering
Special Issues, Collections and Topics in MDPI journals
1. Instituto Superior de Engenharia de Lisboa, Rua Conselheiro Emídio Navarro, 1, 1959-007 Lisboa, Portugal
2. Instituto de Telecomunicações, Campus Universitário de Santiago, Aveiro, Portugal
Interests: antennas; propagation; wireless power transfer
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical hardware and software are becoming increasingly more pervasive, mostly due to the rapid growth and widespread adoption of smart wearable devices. These are capable of recording biometric and health data virtually in an always-on/always-connected approach. However, multiple limitations can hinder the use of such devices in extreme scenarios; these include limited battery lifetime, vulnerable communication infrastructure, inconscient state of the wearer, or interference with safety gear, to name a few. To address some of these limitations, a new approach is emerging, in which sensors are integrated in external objects rather than on a wearer. This approach, which can be designated as “invisible” (or off-the-person), is an enabler for near-continuous and highly pervasive health monitoring through everyday-use objects fitted with biometric- and health-sensing capabilities. In this Special Issue we seek contributions containing original research and review articles expanding the state-of-the-art in “invisibles”, including but not limited to novel electronic-, optical-, radio-, and video-based invisible biomedical sensing systems, biosignal processing methods for data collected using invisibles, and machine learning for invisible sensing applications.

Dr. Paulo Antunes
Dr. Hugo Silva
Dr. Pedro Pinho
Guest Editors

Manuscript Submission Information

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  • invisibles
  • sensors
  • biomedical instrumentation
  • biosignal acquisition and processing
  • telemedicine
  • pervasive healthcare

Published Papers (1 paper)

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17 pages, 3958 KiB  
Identity Recognition in Sanitary Facilities Using Invisible Electrocardiography
Sensors 2022, 22(11), 4201; - 31 May 2022
Cited by 2 | Viewed by 1616
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Invisibles for Biomedical Sensing)
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