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

Deadline for manuscript submissions: 20 June 2026 | Viewed by 1981

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Special Issue Editor

Dr. Wahyu Rahmaniar

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Guest Editor

Biomedical Artificial Intelligence (BMAI), Institute of Science Tokyo, Tokyo, Japan
Interests: AI

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has rapidly emerged as a transformative force in sensing and imaging applications, offering new opportunities for advancing science, engineering, and medicine. By integrating machine learning, computer vision, and deep learning with modern sensor technologies, AI enables systems to perceive, analyze, and interpret complex data with unprecedented accuracy and speed. These developments are reshaping various domains, including medical imaging, industrial inspection, environmental monitoring, autonomous navigation, and smart cities.

Recent breakthroughs in AI-based algorithms allow for real-time image enhancement, anomaly detection, multimodal data fusion, and automated decision-making. In medical contexts, AI-driven imaging supports early diagnosis, personalized treatment planning, and improved patient outcomes. In industrial and environmental settings, intelligent sensing systems enhance safety, optimize efficiency, and reduce costs. At the same time, advances in hardware, cloud computing, and edge AI are expanding the reach of intelligent imaging to resource-constrained and remote environments.

This Special Issue on AI-Based Sensing and Imaging Applications aims to highlight cutting-edge research, methodologies, and case studies that demonstrate the impact of AI on sensing and imaging technologies. We welcome contributions that explore innovative algorithms, novel applications, and interdisciplinary approaches that push the boundaries of how AI enhances sensing, visualization, and decision-support systems.

Dr. Wahyu Rahmaniar
Guest Editor

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Keywords

artificial intelligence
deep learning
computer vision
medical imaging
intelligent sensing
image enhancement
anomaly detection
multimodal data fusion
autonomous systems
smart imaging applications

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Published Papers (3 papers)

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Research

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27 pages, 4998 KB

Open AccessArticle

Machine Learning-Based Human Detection Using Active Non-Line-of-Sight Laser Sensing

by Semra Çelebi and İbrahim Türkoğlu

Sensors 2026, 26(7), 2046; https://doi.org/10.3390/s26072046 - 25 Mar 2026

Viewed by 442

Abstract

Active non-line-of-sight (NLOS) human detection aims to infer the presence of hidden individuals by analyzing indirectly reflected photons between a relay surface and occluded targets. In this study, a single-photon avalanche diode (SPAD) and time-correlated single-photon counting (TCSPC)-based acquisition system were used to measure time–photon waveforms in controlled NLOS environments designed to represent post-disaster rubble scenarios. Although the effective temporal resolution of the system is limited by the detector timing jitter and laser pulse width, the recorded transient signals retain distinguishable intensity and temporal delay patterns associated with the primary and secondary reflections. To construct a representative dataset, measurements were collected under varying subject poses, orientations, and surrounding object configurations. The recorded signals were processed using a unified preprocessing pipeline that included normalization, histogram shaping, and signal windowing. Three machine learning models, namely, Convolutional Neural Network, Gated Recurrent Unit, and Random Forest, were trained and evaluated for human presence classification. All models achieved full sensitivity in detecting human presence; however, notable differences emerged in the classification of human-absent scenarios. Among the tested approaches, random forest achieved the highest overall accuracy and specificity, demonstrating stronger robustness to statistical variations in time–photon histograms under limited photon conditions. These results suggest that tree-based classifiers capture amplitude distribution patterns and temporal dispersion characteristics more effectively than deep neural architectures under the present acquisition constraints. Overall, the findings indicate that low-cost SPAD-based NLOS sensing systems can provide reliable human detection in indirect-observation scenarios. Full article

(This article belongs to the Special Issue AI-Based Sensing and Imaging Applications)

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21 pages, 1287 KB

Open AccessArticle

Machine Learning Calibration of Smartphone-Based Infrared Thermal Cameras: Improved Bias and Persistent Random Error

by Jayroop Ramesh, Tom Loney, Stefan Du Plessis, Homero Rivas, Assim Sagahyroon, Fadi Aloul and Thomas Boillat

Sensors 2026, 26(4), 1295; https://doi.org/10.3390/s26041295 - 17 Feb 2026

Viewed by 639

Abstract

Low-cost, smartphone-based thermal cameras offer unprecedented accessibility for physiological monitoring, yet their validity and reliability for absolute skin temperature measurement in clinical settings remain contentious. This study aims to quantify the agreement and repeatability of a widely used smartphone thermal camera, the FLIR One Pro, against a consumer-grade, non-contact infrared thermometer, the iHealth PT3. A method comparison study was conducted with 40 healthy adult participants, yielding a total of 2400 temperature measurements. Skin temperature of the hand dorsum was measured concurrently with the FLIR One Pro and the iHealth PT3. The protocol involved two rounds: Round 1 (R1) in a stable, static environment to assess baseline repeatability, and Round 2 (R2) in a dynamic environment mimicking clinical repositioning. The performance of the instruments was compared using paired t-tests for mean differences and Bland–Altman analysis for assessing agreement. The iHealth PT3 demonstrated superior precision, with an average intra-participant standard deviation (SD) of 0.030 °C in R1 and 0.092 °C in R2. In stark contrast, the FLIR One Pro exhibited significantly higher variability, with an average SD of 0.34 °C in R1 and 0.30 °C in R2. Bland–Altman analysis revealed a substantial mean bias of −1.42 °C in R1 and −1.15 °C, with critically wide 95% limits of agreement ranges of ≈6 °C. The substantial systematic bias and poor agreement of the FLIR One Pro far exceed both its manufacturer-stated accuracy and clinically acceptable error margins for absolute temperature measurement. To further examine whether calibration could mitigate these deficiencies, we applied a suite of ten machine learning regressors to map FLIR readings onto iHealth PT3 values. Calibration reduced systematic bias across all models, with Quantile Gradient-Boosted Regression Trees achieving the lowest MAE (1.162 °C). The Extra Trees model yielded the lowest RMSE (1.792 °C) and the highest explained variance (

R^{2}

= 0.152), yet this relatively low value confirms that the device’s high intrinsic variability limits the effectiveness of algorithmic correction. As such the device has limited utility for longitudinal patient monitoring or for diagnostic decisions that rely on precise, absolute temperature thresholds. These findings inform medical practitioners in low-resource settings of the profound limitations of using this device as a standalone clinical thermometer and emphasize that algorithmic correction cannot compensate for fundamental hardware and measurement noise constraints. Full article

(This article belongs to the Special Issue AI-Based Sensing and Imaging Applications)

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Review

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31 pages, 2718 KB

Open AccessFeature PaperReview

A Narrative Review of AI Frameworks for Chronic Stress Detection Using Physiological Sensing: Resting, Longitudinal, and Reactivity Perspectives

by Totok Nugroho, Wahyu Rahmaniar and Alfian Ma’arif

Sensors 2026, 26(8), 2345; https://doi.org/10.3390/s26082345 - 10 Apr 2026

Viewed by 505

Abstract

Chronic stress is a time-dependent condition characterized by sustained dysregulation across neural, autonomic, and endocrine systems, with important consequences for both health and socioeconomic outcomes. Unlike acute stress, which is typically characterized by short-lived physiological activation, chronic stress reflects an accumulated allostatic load and a longer-term recalibration of stress response systems. Recent advances in physiological sensing and artificial intelligence (AI) have supported the development of computational approaches for chronic stress detection using electroencephalography (EEG), heart rate variability (HRV), photoplethysmography (PPG), electrodermal activity (EDA), and wearable multimodal platforms. This narrative review examines current AI-based studies through three main inferential paradigms: resting baseline dysregulation, longitudinal physiological monitoring, and reactivity-based inference. Across modalities, classical machine learning (ML) methods, particularly support vector machines (SVMs) and tree-based ensembles, remain the most commonly used approaches, largely because available datasets are small and most pipelines still depend on engineered features. Deep learning (DL) methods are beginning to emerge, but their use remains constrained by the lack of large, standardized, longitudinal datasets specifically designed for chronic stress research. Major challenges include ambiguity in stress labeling, limited longitudinal validation, circadian confounding, inter-individual variability, and small cohort sizes. Future progress will depend on standardized datasets, biologically grounded multimodal integration, hybrid baseline-reactivity modeling, adaptive personalization, and more interpretable AI systems. Greater emphasis is also needed on clinical relevance and generalizability if AI-based chronic stress monitoring is to move beyond experimental settings. Full article

(This article belongs to the Special Issue AI-Based Sensing and Imaging Applications)

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