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Advances in Sensors for Intelligent Personalized Monitoring and Therapy
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Advances in Sensors for Intelligent Personalized Monitoring and Therapy

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

Deadline for manuscript submissions: 20 November 2024 | Viewed by 3210

Special Issue Editor

Prof. Dr. Knut Möller

E-Mail Website
Guest Editor
Institute of Technical Medicine, Furtwangen University, 78054 Villingen-Schwenningen, Germany
Interests: personalized therapy; signal analysis and intelligent monitoring; telemedicine and automation; electrical impedance tomography

Special Issue Information

Dear Colleagues,

Personalized diagnostics and therapy, sometimes called individualized or precision medicine (PM), is an advancing field in research and medicine, that depends on non-intrusive monitoring and specialized informative sensor signals. Because an increasing need of patient specific data limits the advances and applicability of the emerging developments, such as digital twin approaches combined with system identification, we aim to collect the newest advances in research that builds on sensors for use in medical applications, their intelligent use and signal analysis.   Application specific calibration techniques or situation specific active maneuvers for information gain are equally welcome as novel attempts of integrating devices with their embedded sensors into clinical workflows and demonstrating their medical benefits.

Therefore, topics of interest include, but are not limited to, the following:

  • Sensors and sensor fusion for intelligent monitoring;
  • Sensors for digital twin approaches;
  • Active generation of sensor-based information driven by medical needs.

Prof. Dr. Knut Möller
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • medical and non-invasive sensors
  • intelligent signal analysis for monitoring
  • personalized therapy
  • sensors in digital twin solutions

Published Papers (3 papers)

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Research

12 pages, 1653 KiB  
Article
A Novel Experimental Approach for the Measurement of Vibration-Induced Changes in the Rheological Properties of Ex Vivo Ovine Brain Tissue
by Rebecca L. Lilley, Natalia Kabaliuk, Antoine Reynaud, Pavithran Devananthan, Nicole Smith and Paul D. Docherty
Sensors 2024, 24(7), 2022; https://doi.org/10.3390/s24072022 - 22 Mar 2024
Viewed by 572
Abstract
Increased incidence of traumatic brain injury (TBI) imposes a growing need to understand the pathology of brain trauma. A correlation between the incidence of multiple brain traumas and rates of behavioural and cognitive deficiencies has been identified amongst people that experienced multiple TBI [...] Read more.
Increased incidence of traumatic brain injury (TBI) imposes a growing need to understand the pathology of brain trauma. A correlation between the incidence of multiple brain traumas and rates of behavioural and cognitive deficiencies has been identified amongst people that experienced multiple TBI events. Mechanically, repetitive TBIs may affect brain tissue in a similar way to cyclic loading. Hence, the potential susceptibility of brain tissue to mechanical fatigue is of interest. Although temporal changes in ovine brain tissue viscoelasticity and biological fatigue of other tissues such as tendons and arteries have been investigated, no methodology currently exists to cyclically load ex vivo brain tissue. A novel rheology-based approach found a consistent, initial stiffening response of the brain tissue before a notable softening when subjected to a subsequential cyclic rotational shear. History dependence of the mechanical properties of brain tissue indicates susceptibility to mechanical fatigue. Results from this investigation increase understanding of the fatigue properties of brain tissue and could be used to strengthen therapy and prevention of TBI, or computational models of repetitive head injuries. Full article
(This article belongs to the Special Issue Advances in Sensors for Intelligent Personalized Monitoring and Therapy)
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19 pages, 5051 KiB  
Article
Non-Invasive Assessment of Abdominal/Diaphragmatic and Thoracic/Intercostal Spontaneous Breathing Contributions
by Ella F. S. Guy, Jaimey A. Clifton, Jennifer L. Knopp, Lui R. Holder-Pearson and J. Geoffrey Chase
Sensors 2023, 23(24), 9774; https://doi.org/10.3390/s23249774 - 12 Dec 2023
Cited by 2 | Viewed by 809
Abstract
(1) Background: Technically, a simple, inexpensive, and non-invasive method of ascertaining volume changes in thoracic and abdominal cavities are required to expedite the development and validation of pulmonary mechanics models. Clinically, this measure enables the real-time monitoring of muscular recruitment patterns and breathing [...] Read more.
(1) Background: Technically, a simple, inexpensive, and non-invasive method of ascertaining volume changes in thoracic and abdominal cavities are required to expedite the development and validation of pulmonary mechanics models. Clinically, this measure enables the real-time monitoring of muscular recruitment patterns and breathing effort. Thus, it has the potential, for example, to help differentiate between respiratory disease and dysfunctional breathing, which otherwise can present with similar symptoms such as breath rate. Current automatic methods of measuring chest expansion are invasive, intrusive, and/or difficult to conduct in conjunction with pulmonary function testing (spontaneous breathing pressure and flow measurements). (2) Methods: A tape measure and rotary encoder band system developed by the authors was used to directly measure changes in thoracic and abdominal circumferences without the calibration required for analogous strain-gauge-based or image processing solutions. (3) Results: Using scaling factors from the literature allowed for the conversion of thoracic and abdominal motion to lung volume, combining motion measurements correlated to flow-based measured tidal volume (normalised by subject weight) with R2 = 0.79 in data from 29 healthy adult subjects during panting, normal, and deep breathing at 0 cmH2O (ZEEP), 4 cmH2O, and 8 cmH2O PEEP (positive end-expiratory pressure). However, the correlation for individual subjects is substantially higher, indicating size and other physiological differences should be accounted for in scaling. The pattern of abdominal and chest expansion was captured, allowing for the analysis of muscular recruitment patterns over different breathing modes and the differentiation of active and passive modes. (4) Conclusions: The method and measuring device(s) enable the validation of patient-specific lung mechanics models and accurately elucidate diaphragmatic-driven volume changes due to intercostal/chest-wall muscular recruitment and elastic recoil. Full article
(This article belongs to the Special Issue Advances in Sensors for Intelligent Personalized Monitoring and Therapy)
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15 pages, 18105 KiB  
Article
Effect of a Patient-Specific Structural Prior Mask on Electrical Impedance Tomography Image Reconstructions
by Rongqing Chen, Sabine Krueger-Ziolek, Alberto Battistel, Stefan J. Rupitsch and Knut Moeller
Sensors 2023, 23(9), 4551; https://doi.org/10.3390/s23094551 - 7 May 2023
Cited by 2 | Viewed by 1376
Abstract
Electrical Impedance Tomography (EIT) is a low-cost imaging method which reconstructs two-dimensional cross-sectional images, visualising the impedance change within the thorax. However, the reconstruction of an EIT image is an ill-posed inverse problem. In addition, blurring, anatomical alignment, and reconstruction artefacts can hinder [...] Read more.
Electrical Impedance Tomography (EIT) is a low-cost imaging method which reconstructs two-dimensional cross-sectional images, visualising the impedance change within the thorax. However, the reconstruction of an EIT image is an ill-posed inverse problem. In addition, blurring, anatomical alignment, and reconstruction artefacts can hinder the interpretation of EIT images. In this contribution, we introduce a patient-specific structural prior mask into the EIT reconstruction process, with the aim of improving image interpretability. Such a prior mask ensures that only conductivity changes within the lung regions are reconstructed. To evaluate the influence of the introduced structural prior mask, we conducted numerical simulations with two scopes in terms of their different ventilation statuses and varying atelectasis scales. Quantitative analysis, including the reconstruction error and figures of merit, was applied in the evaluation procedure. The results show that the morphological structures of the lungs introduced by the mask are preserved in the EIT reconstructions and the reconstruction artefacts are decreased, reducing the reconstruction error by 25.9% and 17.7%, respectively, in the two EIT algorithms included in this contribution. The use of the structural prior mask conclusively improves the interpretability of the EIT images, which could facilitate better diagnosis and decision-making in clinical settings. Full article
(This article belongs to the Special Issue Advances in Sensors for Intelligent Personalized Monitoring and Therapy)
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