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Sensing Technologies and Applied Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering

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

Deadline for manuscript submissions: 25 February 2026 | Viewed by 2571

Special Issue Editors

Dr. Octavian Postolache

E-Mail Website
Guest Editor
Instituto de Telecomunicações, Universidade de Aveiro Campus Universitário de, R. Santiago, 3810-193 Aveiro, Portugal
Interests: smart sensors; IoT; digital twin; precision agriculture; digital physical therapy
Special Issues, Collections and Topics in MDPI journals
Dr. Luigi Duraccio

E-Mail Website
Guest Editor
Department of Electrical Engineering and Information Technology, University of Naples Federico II (UNINA), 80131 Naples, Italy
Interests: applied metrology for the digital transition in healthcare and industry; metrology for human–computer interaction; measurement sustainability; measurement uncertainty

Special Issue Information

Dear Colleagues,

Research and development efforts are being increasingly dedicated to technologies like eXtended reality (XR), artificial intelligence (AI), and neural engineering (NE). The advancements in these fields are remarkable, but the aspects related to measurement are often disregarded or poorly investigated. Indeed, the abovementioned fields strongly rely on data; thus, data quality must be controlled, for example, by improving the sensors adopted to acquire such data and making a rigorous uncertainty assessment. For this reason, the role of sensing technologies and applied metrology appears crucial. Moreover, novel sensing approaches may disclose new possibilities in terms of the performance and application of XR, AI, and NE. As a further element, synergy between different expertise should be promoted. For instance, it is well known that AI is becoming increasingly pervasive and benefiting various technologies, even sensing and applied metrology. Therefore, this Special Issue invites contributions with a focus on eXtended reality, artificial intelligence methods, and neural engineering topics like brain-computer interfaces, with special attention given to sensing and measurement. 

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

  • Sensing solutions and measurement principles for enhancing the accuracy and robustness of XR-BCI systems.
  • Wearable sensors for neuroimaging.
  • Multisensory experiences and improved immersion.
  • Psychophysical condition monitoring.
  • Advanced machine learning techniques in sensing.
  • Deep-learning-based classification.
  • VR-supported mindfulness based on EEG signals.
  • Immersive user experience with XR-BCI.
  • Human-in-the-loop AI.
  • Bioengineering and rehabilitation.
  • Biosignal processing.
  • Instrumental solutions and measurement principles for smart industry.
  • New challenge for metrology in the digital transformation scenario.

Dr. Octavian Postolache
Dr. Luigi Duraccio
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 sensors in neuroimaging
  • instrumental solutions for XR-BCI systems
  • psychophysical condition monitoring.
  • in-sensor machine learning for neural interfaces
  • deep-learning-based sensor data processing
  • bioengineering and rehabilitation
  • instrumental solutions for smart industry
  • sensing technologies in the digital transformation
  • biomedical systems
  • smart rehabilitation

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Published Papers (2 papers)

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Research

36 pages, 10886 KiB  
Article
Helicopter Turboshaft Engines’ Neural Network System for Monitoring Sensor Failures
by Serhii Vladov, Łukasz Ścisło, Nina Szczepanik-Ścisło, Anatoliy Sachenko, Tomasz Perzyński, Viktor Vasylenko and Victoria Vysotska
Sensors 2025, 25(4), 990; https://doi.org/10.3390/s25040990 - 7 Feb 2025
Viewed by 863
Abstract
An effective neural network system for monitoring sensors in helicopter turboshaft engines has been developed based on a hybrid architecture combining LSTM and GRU. This system enables sequential data processing while ensuring high accuracy in anomaly detection. Using recurrent layers (LSTM/GRU) is critical [...] Read more.
An effective neural network system for monitoring sensors in helicopter turboshaft engines has been developed based on a hybrid architecture combining LSTM and GRU. This system enables sequential data processing while ensuring high accuracy in anomaly detection. Using recurrent layers (LSTM/GRU) is critical for dependencies among data time series analysis and identification, facilitating key information retention from previous states. Modules such as SensorFailClean and SensorFailNorm implement adaptive discretization and quantisation techniques, enhancing the data input quality and contributing to more accurate predictions. The developed system demonstrated anomaly detection accuracy at 99.327% after 200 training epochs, with a reduction in loss from 2.5 to 0.5%, indicating stability in anomaly processing. A training algorithm incorporating temporal regularization and a combined optimization method (SGD with RMSProp) accelerated neural network convergence, reducing the training time to 4 min and 13 s while achieving an accuracy of 0.993. Comparisons with alternative methods indicate superior performance for the proposed approach across key metrics, including accuracy at 0.993 compared to 0.981 and 0.982. Computational experiments confirmed the presence of the highly correlated sensor and demonstrated the method’s effectiveness in fault detection, highlighting the system’s capability to minimize omissions. Full article
(This article belongs to the Special Issue Sensing Technologies and Applied Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering)
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18 pages, 2246 KiB  
Article
Improving Multiscale Fuzzy Entropy Robustness in EEG-Based Alzheimer’s Disease Detection via Amplitude Transformation
by Pasquale Arpaia, Maria Cacciapuoti, Andrea Cataldo, Sabatina Criscuolo, Egidio De Benedetto, Antonio Masciullo, Marisa Pesola and Raissa Schiavoni
Sensors 2024, 24(23), 7794; https://doi.org/10.3390/s24237794 - 5 Dec 2024
Viewed by 1215
Abstract
This study investigates the effectiveness of amplitude transformation in enhancing the performance and robustness of Multiscale Fuzzy Entropy for Alzheimer’s disease detection using electroencephalography signals. Multiscale Fuzzy Entropy is a complexity measure particularly sensitive to intra- and inter-subject variations in signal amplitude, as [...] Read more.
This study investigates the effectiveness of amplitude transformation in enhancing the performance and robustness of Multiscale Fuzzy Entropy for Alzheimer’s disease detection using electroencephalography signals. Multiscale Fuzzy Entropy is a complexity measure particularly sensitive to intra- and inter-subject variations in signal amplitude, as well as the selection of key parameters such as embedding dimension (m) and similarity criterion (r), which often result in inconsistent outcomes when applied to multivariate data, such as electroencephalography signals. To address these challenges and to generalize the possibility of adopting Multiscale Fuzzy Entropy as a diagnostic tool for Alzheimer’s disease, this research explores amplitude transformation preprocessing on electroencephalography signals in Multiscale Fuzzy Entropy calculation across varying parameters. The statistical analysis of the obtained results demonstrates that amplitude transformation preprocessing significantly enhances Multiscale Fuzzy Entropy’s ability to detect Alzheimer’s disease, achieving higher and more consistent significant comparison percentages, with an average of 73.2% across all parameter combinations, compared with only one raw data combination exceeding 65%. Clustering analysis corroborates these findings, showing that amplitude transformation improves the differentiation between Alzheimer’s disease patients and healthy subjects. These results highlight the potential of amplitude transformation to stabilize Multiscale Fuzzy Entropy performance, making it a more reliable tool for early Alzheimer’s disease detection. Full article
(This article belongs to the Special Issue Sensing Technologies and Applied Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering)
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