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Artificial Intelligence and Machine Learning in Engineering Sensing Applications
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Artificial Intelligence and Machine Learning in Engineering Sensing Applications

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 5026

Special Issue Editor

Dr. Biswanath Samanta

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Georgia Southern University, Statesboro, GA 30460, USA
Interests: mechatronics; bio-inspired robotics and intelligent systems; AI and deep learning; deep reinforcement learning; AI and deep learning applications in engineering and biomedicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and machine learning (ML) are rapidly transforming engineering sensing by enabling more sophisticated data analysis, predictive capabilities, and automated decision-making, leading to improved efficiency, reliability, and safety in various industrial applications. The potential areas of using AI and ML in engineering sensing include data pre-processing, sensor calibration and compensation, signal interpretation, and process optimization.

This Special Issue is aimed at publishing articles representing state-of-the-art and future trends of developing AI and ML in engineering sensing applications that include, but are not limited to, machinery and structural health monitoring, sensor networks for environmental monitoring, and autonomous vehicles. Potential authors are invited to contribute in the form of original research, theoretical developments, experimental studies, and reviews of current status and future trends in the area.

Dr. Biswanath Samanta
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 250 words) can be sent to the Editorial Office for assessment.

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

  • artificial intelligence (AI) and machine learning
  • data cleaning and preprocessing
  • anamoly detection
  • process optimization
  • sensor calibration and compensation
  • signal interpretation
  • predictive maintenance
  • machinery and structural health monitoring
  • environmental monitoring
  • quality control
  • autonomous vehicles
  • edge computing
  • federated learning

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Published Papers (4 papers)

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Research

18 pages, 5176 KB  
Article
Individual Variability in Deep Learning-Based Joint Angle Estimation from a Single IMU: A Cross-Population Study
by Koyo Toyoshima, Jae Hoon Lee, Shigeru Kogami, Teppei Miyaki and Toru Manabe
Sensors 2026, 26(1), 178; https://doi.org/10.3390/s26010178 - 26 Dec 2025
Cited by 1 | Viewed by 812
Abstract
Walking ability is crucial for maintaining independence and healthy aging. Although joint angle measurement is important for detailed gait assessment, it is rarely performed in clinical practice due to the complexity of motion capture systems. This study investigates individual variability and cross-population generalizability [...] Read more.
Walking ability is crucial for maintaining independence and healthy aging. Although joint angle measurement is important for detailed gait assessment, it is rarely performed in clinical practice due to the complexity of motion capture systems. This study investigates individual variability and cross-population generalizability of deep learning-based joint angle estimation from a single inertial measurement unit (IMU) attached to the pelvis. Gait data from three distinct populations were collected: 17 young adults, 20 healthy older adults (aged 65+), and 14 pre-operative patients scheduled for hip replacement surgery due to hip osteoarthritis (also aged 65+). A 1D ResNet-based convolutional neural network was trained to estimate bilateral hip, knee, and ankle joint angles from IMU signals. We systematically compared within-population training (trained and tested on the same population) with cross-population training (trained on combined data from all populations) using nested 5-fold cross-validation. Cross-population training showed population-specific effectiveness: older adults demonstrated consistent improvement, while young adults showed minimal change due to already high baseline performance, and pre-operative patients exhibited highly variable responses. These findings suggest that the effectiveness of cross-population learning depends on within-population gait heterogeneity, with important implications for developing clinically applicable gait analysis systems across diverse patient populations. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Engineering Sensing Applications)
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14 pages, 2983 KB  
Article
Lightweight Multimodal Fusion for Urban Tree Health and Ecosystem Services
by Abror Buriboev, Djamshid Sultanov, Ilhom Rahmatullaev, Ozod Yusupov, Erali Eshonqulov, Dilshod Bekmuradov, Nodir Egamberdiev and Andrew Jaeyong Choi
Sensors 2026, 26(1), 7; https://doi.org/10.3390/s26010007 - 19 Dec 2025
Viewed by 838
Abstract
Rapid urban expansion has heightened the demand for accurate, scalable, and real-time methods to assess tree health and the provision of ecosystem services. Urban trees are the major contributors to air-quality improvement and climate change mitigation; however, their monitoring is mostly constrained to [...] Read more.
Rapid urban expansion has heightened the demand for accurate, scalable, and real-time methods to assess tree health and the provision of ecosystem services. Urban trees are the major contributors to air-quality improvement and climate change mitigation; however, their monitoring is mostly constrained to inherently subjective and inefficient manual inspections. In order to break this barrier, we put forward a lightweight multimodal deep-learning framework that fuses RGB imagery with environmental and biometric sensor data for a combined evaluation of tree-health condition as well as the estimation of the daily oxygen production and CO2 absorption. The proposed architecture features an EfficientNet-B0 vision encoder upgraded with Mobile Inverted Bottleneck Convolutions (MBConv) and a squeeze-and-excitation attention mechanism, along with a small multilayer perceptron for sensor processing. A common multimodal representation facilitates a three-task learning set-up, thus allowing simultaneous classification and regression within a single model. Our experiments with a carefully curated dataset of segmented tree images accompanied by synchronized sensor measurements show that our method attains a health-classification accuracy of 92.03% while also lowering the regression error for O2 (MAE = 1.28) and CO2 (MAE = 1.70) in comparison with unimodal and multimodal baselines. The proposed architecture, with its 5.4 million parameters and an inference latency of 38 ms, can be readily deployed on edge devices and real-time monitoring platforms. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Engineering Sensing Applications)
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20 pages, 1910 KB  
Article
MFedBN: Tackling Data Heterogeneity with Gradient-Based Aggregation and Advanced Distribution Skew Modeling
by Kinda Mreish, Evgenia Novikova, Mikhail Chaplygin, Ivan Kholod and Tarek Alnajar
Sensors 2025, 25(23), 7314; https://doi.org/10.3390/s25237314 - 1 Dec 2025
Viewed by 753
Abstract
Federated Learning (FL) enables collaborative model training on smart edge devices while preserving data privacy, but it suffers from decreased performance when faced with non-Independent and Identically Distributed (non-IID) data. This paper addresses the problem of the evaluation of aggregation strategies in non-IID [...] Read more.
Federated Learning (FL) enables collaborative model training on smart edge devices while preserving data privacy, but it suffers from decreased performance when faced with non-Independent and Identically Distributed (non-IID) data. This paper addresses the problem of the evaluation of aggregation strategies in non-IID FL environments, and it proposes an approach to generation of the skewed datasets with different types of non-IIDness from one dataset: with Feature Distribution Skew; with Label Distribution Skew; with Same Label, Different Features skew; and with Same Features, Different Label skew. The authors also introduce a Modified Federated via Local Batch Normalization (MFedBN), which improves model convergence and robustness across various non-IID data skews by implementing a server-side gradient-style update with several Learning Rate values tested within the aggregated function. Experimental evaluation of the MFedBN strategy was conducted on two heterogeneous datasets, namely, the Commercial Vehicles Sensor dataset designed for monitoring vehicle behavior and the NF-UNSW-NB15 dataset for cybersecurity threat detection. In the majority of cases, the MFedBN algorithm outperformed the baseline FedBN, with test accuracies of up to 85% on the Commercial Vehicles Sensor dataset and 99.98% on the NF-UNSW-NB15 dataset. The model trained with MFedBN showed convergence stability and improved generalization in highly heterogeneous federated environments. The proposed algorithm and data generation methods establish a viable platform for privacy-preserving applications in IoT-based monitoring and network intrusion detection, advancing the validity of Federated Learning in real-world, non-IID conditions. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Engineering Sensing Applications)
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9 pages, 893 KB  
Article
Real-Time Monitoring of Personal Protective Equipment Adherence Using On-Device Artificial Intelligence Models
by Yam Horesh, Renana Oz Rokach, Yotam Kolben and Dean Nachman
Sensors 2025, 25(7), 2003; https://doi.org/10.3390/s25072003 - 22 Mar 2025
Cited by 4 | Viewed by 1874
Abstract
Personal protective equipment (PPE) is crucial for infection prevention and is effective only when worn correctly and consistently. Health organizations often use education or inspections to mitigate non-compliance, but these are costly and have limited success. This study developed a novel on-device, AI-based [...] Read more.
Personal protective equipment (PPE) is crucial for infection prevention and is effective only when worn correctly and consistently. Health organizations often use education or inspections to mitigate non-compliance, but these are costly and have limited success. This study developed a novel on-device, AI-based computer vision system to monitor healthcare worker PPE adherence in real time. Using a custom-built image dataset of 7142 images of 11 participants wearing various combinations of PPE (mask, gloves, gown), we trained a series of binary classifiers for each PPE item. By utilizing a lightweight MobileNetV3 model, we optimized the system for edge computing on a Raspberry Pi 5 single-board computer, enabling rapid image processing without the need for external servers. Our models achieved high accuracy in identifying individual PPE items (93–97%), with an overall accuracy of 85.58 ± 0.82% when all items were correctly classified. Real-time evaluation with 11 unseen medical staff in a cardiac intensive care unit demonstrated the practical viability of our system, maintaining a high per-item accuracy of 87–89%. This study highlights the potential for AI-driven solutions to significantly improve PPE compliance in healthcare settings, offering a cost-effective, efficient, and reliable tool for enhancing patient safety and mitigating infection risks. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Engineering Sensing Applications)
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