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Smart Sensor Technologies for Accurate Movement Monitoring and Connectivity
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Smart Sensor Technologies for Accurate Movement Monitoring and Connectivity

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 1890

Special Issue Editor

Dr. Pi-Chung Wang

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Guest Editor
Department of Computer Science and Engineering, National Chung Hsing University, Taichung, Taiwan
Interests: cloud computing; routing; wireless ad hoc networks; information and communication technology; TCP; routing protocols; switching

Special Issue Information

Dear Colleagues,

Smart sensors are used by fitness enthusiasts and athletes to track performance, enhance training, and avoid injuries. Wearable technology has sensors built in to detect variables including joint angles, speed, and acceleration, giving users immediate feedback on their performance and technique. Smart sensors keep an eye on the way machines and other equipment move in industrial settings to make sure everything runs smoothly and safely. By detecting vibrations, rotations, and other mechanical movements, these sensors cut downtime and enable predictive maintenance. Movement monitoring plays a critical role in smart city traffic management, public transportation enhancement, and pedestrian and cycling safety. Smart sensors that are integrated into infrastructure have the ability to track the movement of cars and pedestrians, improving urban safety and facilitating more effective traffic control. Although the price of smart sensors has dropped, some customers and applications may not be able to afford high-precision sensors and sophisticated communication solutions. Smart sensor technologies require connectivity in order to facilitate the smooth transfer of data between sensors and other devices.

This Special Issue invites researchers, developers, and industry experts to examine the most recent developments, industry best practices, and potential future directions in smart sensor technology. Contributions are invited from a range of disciplines and perspectives, including, but not restricted to, smart sensor technologies for accurate movement monitoring and connectivity.

Dr. Pi-Chung Wang
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

  • smart sensor
  • movement monitoring and connectivity
  • wearable technology

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Published Papers (2 papers)

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Research

16 pages, 1068 KiB  
Article
Behavioral Monitoring in Transient Ischemic Attack and Stroke Patients: Exploratory Micro- and Macrostructural Imaging Insights for Identifying Post-Stroke Depression with Accelerometers in UK Biobank
by Stephanie J. Zawada, Ali Ganjizadeh, Bart M. Demaerschalk and Bradley J. Erickson
Sensors 2025, 25(3), 963; https://doi.org/10.3390/s25030963 - 5 Feb 2025
Cited by 1 | Viewed by 937
Abstract
To examine the association between post-stroke depression (PSD) and macrostructural and microstructural brain measures, and to explore whether changes in accelerometer-measured physical activity (PA) are associated with PSD, we conducted an exploratory study in UK Biobank with dementia-free participants diagnosed with at least [...] Read more.
To examine the association between post-stroke depression (PSD) and macrostructural and microstructural brain measures, and to explore whether changes in accelerometer-measured physical activity (PA) are associated with PSD, we conducted an exploratory study in UK Biobank with dementia-free participants diagnosed with at least one prior stroke. Eligible participants (n = 1186) completed an MRI scan. Depression was classified based on positive depression screening scores (PHQ-2 ≥ 3). Multivariate linear regression models assessed the relationships between depression and structural and diffusion measures generated from brain MRI scans. Logistic regression models were used to examine the relationship between accelerometer-measured daily PA and future depression (n = 367). Depression was positively associated with total white matter hyperintensities (WMHs) volume (standardized β [95% CI]—0.1339 [0.012, 0.256]; FDR-adjusted p-value—0.039), periventricular WMHs volume (standardized β [95% CI]—0.1351 [0.020, 0.250]; FDR-adjusted p-value—0.027), and reduced MD for commissural fibers (standardized β [95% CI]—−0.139 [−0.255, −0.024]; adjusted p-value—0.045). The odds of depression decreased by 0.3% for each daily minute spent in objectively measured light PA, while each minute spent in sleep from midnight to 6:00 AM was associated with a 0.9% decrease in the odds of depression. This early-stage analysis using a population cohort offers a scientific rationale for researchers using multimodal data sources to investigate the heterogenous nature of PSD and, potentially, identify stroke patients at risk of poor outcomes. Full article
(This article belongs to the Special Issue Smart Sensor Technologies for Accurate Movement Monitoring and Connectivity)
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12 pages, 1496 KiB  
Article
Center of Mass Estimation During Single-Leg Standing Using a Force Platform and Inertial Sensors
by Ryosuke Takahashi and Motomichi Sonobe
Sensors 2025, 25(3), 871; https://doi.org/10.3390/s25030871 - 31 Jan 2025
Cited by 1 | Viewed by 672
Abstract
Single-leg standing is a conventional balance evaluation method used in medicine. Although the center of mass (COM) displacement should be evaluated to determine balance quality, no practical COM estimation methods have been developed for single-leg standing. This study aimed to estimate the COM [...] Read more.
Single-leg standing is a conventional balance evaluation method used in medicine. Although the center of mass (COM) displacement should be evaluated to determine balance quality, no practical COM estimation methods have been developed for single-leg standing. This study aimed to estimate the COM displacement in the anteroposterior and mediolateral directions during single-leg standing using practical measurements. We used a force platform and three inertial measurement units to estimate the COM displacement based on rigid-link models in the sagittal and frontal planes. The rigid-link models were composed of the stance leg, upper body, and non-stance leg. Seven healthy male subjects participated in the experiment to validate the estimation accuracy. The COM estimation accuracy was verified by comparison with measurements obtained using an optical motion capture system. The root mean square error of this method was 1.18 mm in the sagittal plane and 1.26 mm in the frontal plane. This technique will contribute to the detailed evaluation of individual balance abilities in the medical and sports fields. Full article
(This article belongs to the Special Issue Smart Sensor Technologies for Accurate Movement Monitoring and Connectivity)
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