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

Deadline for manuscript submissions: 30 July 2026 | Viewed by 3781

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Dr. Laura Fabbiano

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Department of Mechanics Mathematics and Management, Politecnico di Bari, 70100 Bari, Italy
Interests: metrological characterization of measurement systems; analysis of uncertainty; statistical quality control; processing of images and measurement procedures applied to biometric measurements; thermofluid dynamics measurements; vibrational measurements
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Special Issue Information

Dear Colleagues,

Over the past decade, measurements and sensors have become a cornerstone of technological innovation, enabling unprecedented data acquisition, monitoring, and control capabilities, and their evolution has been driven by the development of smart systems—from smart cities and smart grids to Industry 4.0 and digital agriculture—where measurement procedures and sensors form the essential link between the physical world and digital intelligence.

Advances in materials science, MEMS/NEMS, nanotechnology, and flexible electronics are leading to novel sensing platforms with improved sensitivity, selectivity, and robustness. Meanwhile, the integration of sensors with wireless communication technologies (IoT and 5G/6G) and AI-driven data analytics is opening up new perspectives in terms of real-time decision-making, autonomous systems, and adaptive environments.

The applications of modern measurement sensors are extremely diverse and encompass the following:

Healthcare and biomedical engineering—wearable sensors, biosensors, and contactless diagnostic devices;
Robotics and autonomous systems—perception, navigation, and human–machine interaction;
Smart cities and infrastructures—structural health monitoring, environmental quality, mobility management;
Precision agriculture and food safety—soil and crop monitoring, smart irrigation, and traceability;
Energy systems—smart grids, renewable integration, and efficiency monitoring.

At the same time, the theory of measurement and the metrological characteristics of sensors (accuracy, resolution, sensitivity, stability, response time, and uncertainty) remain central to ensuring reliability and reproducibility, especially in safety-critical domains such as healthcare, aerospace, and industrial automation. The development of sensor calibration methods, standards, and uncertainty modelling is therefore essential to transform raw sensing signals into meaningful and actionable data.

Dr. Laura Fabbiano
Guest Editor

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Keywords

measurement theory
sensor design and fabrication
calibration and uncertainty analysis
multimodal sensing
signal processing and sensor fusion
AI/ML for sensor data
metrology in emerging sensors
smart cities and infrastructures
environmental and agricultural monitoring
smart applications

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Published Papers (5 papers)

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Research

20 pages, 2679 KB

Open AccessArticle

Physiological and Behavioral Response Differences Between Video-Mediated and In-Person Interaction

by Christoph Tremmel, Nathan T. M. Huneke, Daniel Hobson, Christopher Tacca and m.c. schraefel

Sensors 2026, 26(1), 34; https://doi.org/10.3390/s26010034 (registering DOI) - 20 Dec 2025

Viewed by 134

Abstract

This study investigates how virtual communication differs from in-person interaction across physiological and behavioral domains, with the goal of informing future interface design. Using a naturalistic setup, we recorded multimodal biosignals, including eye tracking, head and hand movement, heart rate, respiratory rate, and EEG during both in-person and video-based dialogues. Our results show that virtual communication significantly reduces movement and gaze dynamics, particularly in horizontal eye movements and lateral head motion, reflecting both sender- and receiver-side constraints. These physical limitations likely stem from the need to remain within the camera frame and the restricted access to nonverbal cues. Pupil dilation was significantly greater during in-person conversations, consistent with increased arousal during natural communication. Heart rate and EEG trends similarly suggested heightened engagement in face-to-face settings, though interpretation of EEG was limited by movement artifacts. Together, the findings highlight how virtual platforms alter embodied interaction, underscoring the need to address both mobility and visual access in future communication technologies to better support co-presence. Full article

(This article belongs to the Special Issue Measurement Sensors and Applications)

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15 pages, 4355 KB

Open AccessArticle

An Electromagnetic Low-Frequency Flextensional Transducer for Acoustic Logging

by Baiyong Men, Huijun Yu, Mingming Jiang, Junqiang Lu, Xiaohua Che and Shizhen Ke

Sensors 2025, 25(24), 7481; https://doi.org/10.3390/s25247481 - 9 Dec 2025

Viewed by 276

Abstract

Low-frequency acoustic logging transducers are pivotal to far-acoustic imaging logging technology and permeability logging technology. This study presents a monopole acoustic transducer driven by electromagnetic force, exploiting the low-frequency vibration characteristics of a flextensional shell. Finite element simulations were employed to evaluate multiple magnetic circuit configurations under dimensional constraints typical of logging tools. An inner magnet circuit was selected and optimized through parametric analysis. Concurrently, the vibration shell was designed and simulated under borehole conditions, accompanied by the development of a dedicated excitation circuit. The fabricated prototype (64 mm outer diameter, 154 mm height, 100 mm shell height) demonstrated operation frequency at 1300 Hz with a sound pressure level of approximately 150 dB and uniform circumferential radiation, satisfying the requirements of logging applications. Full article

(This article belongs to the Special Issue Measurement Sensors and Applications)

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18 pages, 5310 KB

Open AccessArticle

Bias Normalization for Sensors in Smart Devices

by Wonjoon Son and Lynn Choi

Sensors 2025, 25(23), 7291; https://doi.org/10.3390/s25237291 - 30 Nov 2025

Viewed by 471

Abstract

Modern electronic devices, such as smartphones and drones, integrate various sensors to enable diverse sensor-based applications. Yet, sensor measurements exhibit significant variations across different device models, even under the same environment. These variations arise from sensor biases, which occur in three different types: offset bias (additive constant errors), scale bias (multiplicative proportional errors), and drift bias (time-dependent or temperature-dependent errors). Among the biases, in this paper we specifically target offset bias, which has the greatest impact in typical smartphone usage scenarios. This generally leads to performance degradation in sensor-based applications across various device models and instances. To understand the characteristics of the offset bias, we categorize sensors into sensors with and without absolute reference values. Sensors with absolute references enable direct calibration using theoretical true values, while sensors with relative references require different approaches depending on how sensor applications process the data. For scalar-based applications that determine the current state by comparing a sensor measurement against a pre-defined reference, the offset biases can be removed by the existing procedures using reference devices. However, for sequence-based applications that determine the current state by analyzing relative changes in a sequence, the offset bias issue has not been addressed yet. We propose initial value removal and mean removal algorithms that statically and dynamically remove the offset biases from the sensor data sequences for these sequence-based applications. We evaluate our bias normalization algorithms for two different use cases in a geomagnetic-based indoor positioning system (IPS). First, we evaluate the impact of our bias normalization algorithms on the positioning performance of our LSTM-based IPS. Without bias normalization, although the reference device (Galaxy S23 Plus) showed an average positioning error of 0.6 m, the other three smartphone models (Galaxy S22 Plus, iPhone 15, and iPhone 16 Pro) exhibited much worse positioning performance, with errors of 2.48 m, 18.21 m, and 13.13 m. However, after applying our bias normalization, the average positioning errors of all models dropped below 0.68 m. Second, we also evaluate the impact of the bias normalization on detecting whether the position of a smartphone is in a pocket or in a hand-held state. For this, we analyze the sequence of light sensor measurements. We improved the detection accuracy from 42.3% to 97.6% with bias normalization across all device models without requiring individual threshold settings. Full article

(This article belongs to the Special Issue Measurement Sensors and Applications)

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16 pages, 3676 KB

Open AccessArticle

Measurement of Form and Position Error of Small-Diameter Deep Holes Based on Collaboration Between a Lateral Confocal Displacement Sensor and Helical Scanning

by Yao Liu, Daguo Yu, Huifu Du and Tong Chen

Sensors 2025, 25(22), 6863; https://doi.org/10.3390/s25226863 - 10 Nov 2025

Viewed by 445

Abstract

In this study, an innovative measurement method integrating lateral confocal technology and composite motion control is proposed to address the physical space constraints and data processing problems in the detection of the shape and position errors in deep holes with large aspect ratios and small diameters. By designing a lateral confocal displacement sensor and a cantilever measuring device, we break through the spatial constraints of

\emptyset 6 m m

deep-hole inspection and solve the problems of rigidity and surface damage in the traditional contact probe. We constructed an axis-rotation coordinated motion control model and found that the measuring points were densely arranged in a helical trajectory along the inner wall of the hole. We developed the “virtual slicing–B-spline reconstruction” algorithm and used the adaptive motion control algorithm to achieve a more efficient measurement of the hole. The innovative “virtual slicing–B-spline reconstruction” algorithm, using adaptive grouping, dynamic slicing, and a fourth-order B-spline-fitting hierarchical processing framework, reached a straightness error assessment result of the

1 μ m

order. Experiments show that, under

0.5 m m ∕ s

feed rate and

12 r m p

rotational speed, the standard deviation of straightness is ≤

0.0008 m m

and the standard deviation of cylindricity is ≤

0.0064 m m

; compared to the CMM (coordinate measuring machine) measurement results, the cylindricity and straightness evaluation errors obtained by the new measurement method are reduced by

4.6 %

and

4.5 %

, respectively. It provides a technical solution that improves both accuracy and efficiency for the precision inspection of small-diameter deep holes. Full article

(This article belongs to the Special Issue Measurement Sensors and Applications)

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14 pages, 1788 KB

Open AccessArticle

The Validity and Reliability of the Force Plates and the Linear Position Transducer in Measuring Countermovement Depth and Velocity During Countermovement Jump

by Zheng’ao Li, Wenyue Ma, Ling Zhang, Wenfei Zhu, Qian Xie and Yuliang Sun

Sensors 2025, 25(21), 6542; https://doi.org/10.3390/s25216542 - 23 Oct 2025

Viewed by 1953

Abstract

Countermovement jump (CMJ) is a key test for evaluating lower-limb neuromuscular function, and accurate measurement of countermovement depth (CMD) and countermovement velocity (CMV) is critical for determining optimal performance. However, the measurement validity and reliability of CMD and CMV—particularly when obtained from force plates (FP) and linear position transducers (LPT)—have remained uncertain. This study determined the validity and reliability of FP and LPT for measuring CMD and CMV. Twenty-eight male recreational athletes performed the CMJ test, and the variables were synchronously acquired by Motion Capture (MC), FP, and LPT. The test was divided into two sessions, with participants completing three maximal effort CMJs per session, and the second session occurred more than 48 h after the first. The reliability was evaluated using the intraclass correlation coefficient (ICC), and the validity was evaluated with linear Pearson’s correlation coefficient (r), one-way ANOVA with repeated measures, and Bland–Altman plots. The reliability results for FP and LPT indicated good to excellent (ICC = 0.809–0.900). Compared with MC, the FP showed a high to very high correlation (r = 0.894–0.937), and the LPT showed a high correlation (r = 0.721–0.726). When precise quantification of CMD/CMV is required, FP should be preferred. If only an LPT is available, it is best used for within-athlete longitudinal monitoring with a consistent setup, and cross-device comparisons should be avoided. Full article

(This article belongs to the Special Issue Measurement Sensors and Applications)

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