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Wearable and Implantable Sensors for Real-Time Detection and Diagnosis

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 2202
Please feel free to contact Guest Editors or Special Issue Editor ([email protected]) for any queries.

Special Issue Editors

1. Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
2. Department of Radiology, School of Medicine, Stanford University, Stanford, CA 94305, USA
Interests: wearable electronics; real-time sensor; bio-MEMS; implantable sensor; brain-machine-interface; transistor
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
1. Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
2. Department of Radiology, School of Medicine, Stanford University, Stanford, CA 94305, USA
Interests: chemical sensors; biosensors; point-of-care; surface functionalization and coating; polymers; nanomaterials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, with the enormous development of medical devices, there is a high demand for wearable and implantable sensors that facilitate real-time detection and diagnosis of biomarkers or chemicals near the body in various fields including therapeutics, pharmaceutics, and environment monitoring.

Real-time detection in sensors claims a breakthrough of the materials and methodologies to overcome the conventional systems. In this regard, novel sensing materials such as functional polymers, biomolecules (e.g. polynucleotides and proteins), and semiconductors have intensively been used as a core material of sensors which has significant properties of a fast and specific response, biocompatibility, and easy engineering. Besides, the microelectromechanical systems (MEMS) technology has been studied for the structural engineering and fine-tuning of the sensing segments in the devices, enabling cutting-edge performance.

We address both fundamentals and applications to cover recent impressive research in real-time wearable and implantable sensors. Fundamental studies include the discovery and engineering of novel sensing materials, MEMS process, sensing mechanism, and characterization. Applications cover chemical sensors, biosensors, drug delivery, closed-loop therapeutics, wireless system, brain-machine-interface, and other environmental monitoring. This Special Issue introduces new insights on real-time detection and diagnosis for advanced sensor technologies. We look forward to receiving your submissions!

Dr. Chan Ho Park
Dr. Ji-Won Seo
Guest Editors

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

  • Wearable sensor
  • implantable sensor
  • real-time detection
  • diagnosis
  • bio-MEMS
  • nanomaterials
  • nanostructure
  • point-of-care

Published Papers (1 paper)

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Research

16 pages, 2318 KiB  
Article
Estimation of Head Accelerations in Crashes Using Neural Networks and Sensors Embedded in the Protective Helmet
by Andrea Bracali and Niccolò Baldanzini
Sensors 2022, 22(15), 5592; https://doi.org/10.3390/s22155592 - 26 Jul 2022
Cited by 1 | Viewed by 1675
Abstract
Traumatic Brain Injuries (TBIs) are one of the most frequent and severe outcomes of a Powered Two-Wheeler (PTW) crash. Early diagnosis and treatment can greatly reduce permanent consequences. Despite the fact that devices to track head kinematics have been developed for sports applications, [...] Read more.
Traumatic Brain Injuries (TBIs) are one of the most frequent and severe outcomes of a Powered Two-Wheeler (PTW) crash. Early diagnosis and treatment can greatly reduce permanent consequences. Despite the fact that devices to track head kinematics have been developed for sports applications, they all have limitations, which hamper their use in everyday road applications. In this study, a new technical solution based on accelerometers integrated in a motorcycle helmet is presented, and the related methodology to estimate linear and rotational acceleration of the head with deep Artificial Neural Networks (dANNs) is developed. A finite element model of helmet coupled with a Hybrid III head model was used to generate data needed for the neural network training. Input data to the dANN model were time signals of (virtual) accelerometers placed on the inner surface of the helmet shell, while the output data were the components of linear and rotational head accelerations. The network was capable of estimating, with good accuracy, time patterns of the acceleration components in all impact conditions that require medical treatment. The correlation between the reference and estimated values was high for all parameters and for both linear and rotational acceleration, with coefficients of determination (R2) ranging from 0.91 to 0.97. Full article
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