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Advancing Sensor Technology with Artificial Intelligence: Innovations and Applications
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Advancing Sensor Technology with Artificial Intelligence: Innovations and Applications

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

Deadline for manuscript submissions: closed (25 April 2025) | Viewed by 3302

Special Issue Editor

Dr. Inga Morkvenaite-Vilkonciene

E-Mail Website
Guest Editor
Department of Nanotechnology, State Research Institute Centre for Physical Sciences and Technology, 02300 Vilnius, Lithuania
Interests: biosensors; electrochemistry; scanning electrochemical microscopy

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) applications in sensor technology are rapidly evolving, ushering in a new era of intelligent sensing solutions. Sensors are becoming more sophisticated; therefore, integrating AI techniques such as machine learning and deep learning is crucial in enhancing their performance, enabling predictive analytics, and providing actionable insights. This synergy transforms sectors from healthcare and environmental monitoring to industrial automation and smart consumer devices.

Dr. Inga Morkvenaite-Vilkonciene
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

  • AI-driven sensor data processing
  • intelligent sensor networks
  • predictive maintenance and fault detection
  • smart healthcare sensors
  • environmental and industrial monitoring

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

15 pages, 5129 KiB  
Article
Driver Head–Hand Cooperative Action Recognition Based on FMCW Millimeter-Wave Radar and Deep Learning
by Lianlong Zhang, Xiaodong Chen, Zexin Chen, Jiawen Zheng and Yinliang Diao
Sensors 2025, 25(8), 2399; https://doi.org/10.3390/s25082399 - 10 Apr 2025
Viewed by 256
Abstract
Driver status plays a critical role in ensuring driving safety. However, the current visual recognition-based methods for detecting driver actions and status are often limited to factors such as ambient light condition, occlusion, and privacy concerns. In contrast, millimeter-wave radar offers various advantages [...] Read more.
Driver status plays a critical role in ensuring driving safety. However, the current visual recognition-based methods for detecting driver actions and status are often limited to factors such as ambient light condition, occlusion, and privacy concerns. In contrast, millimeter-wave radar offers various advantages such as high accuracy, ease of integration, insensitivity to light condition, and low cost; therefore, it has been widely used for monitoring vital signals and in action recognition. Despite this, the existing studies on driver action recognition have been hindered by limited accuracy and a narrow range of detectable actions. In this study, we utilized a 77 GHz millimeter-wave frequency-modulated continuous-wave radar to construct a dataset encompassing seven types of driver head–hand cooperative actions. Furthermore, a deep learning network model based on VGG16-LSTM-CBAM using micro-Doppler spectrograms as input was developed for action classification. The experimental results demonstrated that, compared to the existing CNN-LSTM and ALEXNET-LSTM networks, the proposed network achieves a classification accuracy of 99.16%, effectively improving driver action detection. Full article
(This article belongs to the Special Issue Advancing Sensor Technology with Artificial Intelligence: Innovations and Applications)
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22 pages, 6644 KiB  
Article
A Transformer Encoder Approach for Localization Reconstruction During GPS Outages from an IMU and GPS-Based Sensor
by Kévin Cédric Guyard, Jonathan Bertolaccini, Stéphane Montavon and Michel Deriaz
Sensors 2025, 25(2), 522; https://doi.org/10.3390/s25020522 - 17 Jan 2025
Viewed by 812
Abstract
Accurate localization is crucial for numerous applications. While several methods exist for outdoor localization, typically relying on GPS signals, these approaches become unreliable in environments subject to a weak GPS signal or GPS outage. Many researchers have attempted to address this limitation, primarily [...] Read more.
Accurate localization is crucial for numerous applications. While several methods exist for outdoor localization, typically relying on GPS signals, these approaches become unreliable in environments subject to a weak GPS signal or GPS outage. Many researchers have attempted to address this limitation, primarily focusing on real-time solutions. However, for applications that do not require real-time localization, these methods remain suboptimal. This paper presents a novel Transformer-based bidirectional encoder approach to address, in postprocessing, the localization challenges during GPS weak signal phases or GPS outages. Our method predicts the velocity during periods of weak or lost GPS signals and calculates the position through bidirectional velocity integration. Additionally, it incorporates position interpolation to ensure smooth transitions between active GPS and GPS outage phases. Applied to a dataset tracking horse positions—which features velocities up to 10 times those of pedestrians and higher acceleration—our approach achieved an average trajectory error below 3 m, while maintaining stable relative distance errors regardless of the GPS outage duration. Full article
(This article belongs to the Special Issue Advancing Sensor Technology with Artificial Intelligence: Innovations and Applications)
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18 pages, 6587 KiB  
Article
Predicting the Wear Amount of Tire Tread Using 1D−CNN
by Hyunjae Park, Junyeong Seo, Kangjun Kim and Taewung Kim
Sensors 2024, 24(21), 6901; https://doi.org/10.3390/s24216901 - 28 Oct 2024
Cited by 3 | Viewed by 1754
Abstract
Since excessively worn tires pose a significant risk to vehicle safety, it is crucial to monitor tire wear regularly. This study aimed to verify the efficient tire wear prediction algorithm proposed in a previous modeling study, which minimizes the required input data, and [...] Read more.
Since excessively worn tires pose a significant risk to vehicle safety, it is crucial to monitor tire wear regularly. This study aimed to verify the efficient tire wear prediction algorithm proposed in a previous modeling study, which minimizes the required input data, and use driving test data to validate the method. First, driving tests were conducted with tires at various wear levels to measure internal accelerations. The acceleration signals were then screened using empirical functions to exclude atypical data before proceeding with the machine learning process. Finally, a tire wear prediction algorithm based on a 1D−CNN with bottleneck features was developed and evaluated. The developed algorithm showed an RMSE of 5.2% (or 0.42 mm) using only the acceleration signals. When tire pressure and vertical load were included, the prediction error was reduced by 11.5%, resulting in an RMSE of 4.6%. These findings suggest that the 1D−CNN approach is an efficient method for predicting tire wear states, requiring minimal input data. Additionally, it supports the potential usefulness of the intelligent tire technology framework proposed in the modeling study. Full article
(This article belongs to the Special Issue Advancing Sensor Technology with Artificial Intelligence: Innovations and Applications)
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