Sensors

18 pages, 7774 KiB

Open AccessArticle

Attention-Guided Disentangled Feature Aggregation for Video Object Detection

by Shishir Muralidhara, Khurram Azeem Hashmi, Alain Pagani, Marcus Liwicki, Didier Stricker and Muhammad Zeshan Afzal

Sensors 2022, 22(21), 8583; https://doi.org/10.3390/s22218583 - 7 Nov 2022

Cited by 4 | Viewed by 3758

Abstract

Object detection is a computer vision task that involves localisation and classification of objects in an image. Video data implicitly introduces several challenges, such as blur, occlusion and defocus, making video object detection more challenging in comparison to still image object detection, which [...] Read more.

Object detection is a computer vision task that involves localisation and classification of objects in an image. Video data implicitly introduces several challenges, such as blur, occlusion and defocus, making video object detection more challenging in comparison to still image object detection, which is performed on individual and independent images. This paper tackles these challenges by proposing an attention-heavy framework for video object detection that aggregates the disentangled features extracted from individual frames. The proposed framework is a two-stage object detector based on the Faster R-CNN architecture. The disentanglement head integrates scale, spatial and task-aware attention and applies it to the features extracted by the backbone network across all the frames. Subsequently, the aggregation head incorporates temporal attention and improves detection in the target frame by aggregating the features of the support frames. These include the features extracted from the disentanglement network along with the temporal features. We evaluate the proposed framework using the ImageNet VID dataset and achieve a mean Average Precision (mAP) of 49.8 and 52.5 using the backbones of ResNet-50 and ResNet-101, respectively. The improvement in performance over the individual baseline methods validates the efficacy of the proposed approach. Full article

(This article belongs to the Section Sensing and Imaging)

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18 pages, 1532 KiB

Open AccessArticle

Consortium Framework Using Blockchain for Asthma Healthcare in Pandemics

by Muhammad Shoaib Farooq, Maryam Suhail, Junaid Nasir Qureshi, Furqan Rustam, Isabel de la Torre Díez, Juan Luis Vidal Mazón, Carmen Lili Rodríguez and Imran Ashraf

Sensors 2022, 22(21), 8582; https://doi.org/10.3390/s22218582 - 7 Nov 2022

Cited by 4 | Viewed by 2955

Abstract

Asthma is a deadly disease that affects the lungs and air supply of the human body. Coronavirus and its variants also affect the airways of the lungs. Asthma patients approach hospitals mostly in a critical condition and require emergency treatment, which creates a [...] Read more.

Asthma is a deadly disease that affects the lungs and air supply of the human body. Coronavirus and its variants also affect the airways of the lungs. Asthma patients approach hospitals mostly in a critical condition and require emergency treatment, which creates a burden on health institutions during pandemics. The similar symptoms of asthma and coronavirus create confusion for health workers during patient handling and treatment of disease. The unavailability of patient history to physicians causes complications in proper diagnostics and treatments. Many asthma patient deaths have been reported especially during pandemics, which necessitates an efficient framework for asthma patients. In this article, we have proposed a blockchain consortium healthcare framework for asthma patients. The proposed framework helps in managing asthma healthcare units, coronavirus patient records and vaccination centers, insurance companies, and government agencies, which are connected through the secure blockchain network. The proposed framework increases data security and scalability as it stores encrypted patient data on the Interplanetary File System (IPFS) and keeps data hash values on the blockchain. The patient data are traceable and accessible to physicians and stakeholders, which helps in accurate diagnostics, timely treatment, and the management of patients. The smart contract ensures the execution of all business rules. The patient profile generation mechanism is also discussed. The experiment results revealed that the proposed framework has better transaction throughput, query delay, and security than existing solutions. Full article

(This article belongs to the Section Internet of Things)

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28 pages, 10325 KiB

Open AccessReview

Review of Vibration Control Strategies of High-Rise Buildings

by Mohamed Hechmi El Ouni, Mahdi Abdeddaim, Said Elias and Nabil Ben Kahla

Sensors 2022, 22(21), 8581; https://doi.org/10.3390/s22218581 - 7 Nov 2022

Cited by 19 | Viewed by 6831

Abstract

Since the early ages of human existence on Earth, humans have fought against natural hazards for survival. Over time, the most dangerous hazards humanity has faced are earthquakes and strong winds. Since then and till nowadays, the challenges are ongoing to construct higher [...] Read more.

Since the early ages of human existence on Earth, humans have fought against natural hazards for survival. Over time, the most dangerous hazards humanity has faced are earthquakes and strong winds. Since then and till nowadays, the challenges are ongoing to construct higher buildings that can withstand the forces of nature. This paper is a detailed review of various vibration control strategies used to enhance the dynamical response of high-rise buildings. Hence, different control strategies studied and used in civil engineering are presented with illustrations of real applications if existing. The main aim of this review paper is to provide a reference-rich document for all the contributors to the vibration control of structures. This paper will clarify the applicability of specific control strategies for high-rise buildings. It is worth noting that not all the studied and investigated methods are applicable to high-rise buildings; a few of them remain limited by many parameters such as cost-effectiveness and engineering-wise installation and maintenance. Full article

(This article belongs to the Special Issue Structural Assessment, Vibration Mitigation and Monitoring of Civil Structures)

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12 pages, 5793 KiB

Open AccessArticle

Learning-Based Image Damage Area Detection for Old Photo Recovery

by Tien-Ying Kuo, Yu-Jen Wei, Po-Chyi Su and Tzu-Hao Lin

Sensors 2022, 22(21), 8580; https://doi.org/10.3390/s22218580 - 7 Nov 2022

Cited by 4 | Viewed by 2631

Abstract

Most methods for repairing damaged old photos are manual or semi-automatic. With these methods, the damaged region must first be manually marked so that it can be repaired later either by hand or by an algorithm. However, damage marking is a time-consuming and [...] Read more.

Most methods for repairing damaged old photos are manual or semi-automatic. With these methods, the damaged region must first be manually marked so that it can be repaired later either by hand or by an algorithm. However, damage marking is a time-consuming and labor-intensive process. Although there are a few fully automatic repair methods, they are in the style of end-to-end repairing, which means they provide no control over damaged area detection, potentially destroying or being unable to completely preserve valuable historical photos to the full degree. Therefore, this paper proposes a deep learning-based architecture for automatically detecting damaged areas of old photos. We designed a damage detection model to automatically and correctly mark damaged areas in photos, and this damage can be subsequently repaired using any existing inpainting methods. Our experimental results show that our proposed damage detection model can detect complex damaged areas in old photos automatically and effectively. The damage marking time is substantially reduced to less than 0.01 s per photo to speed up old photo recovery processing. Full article

(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors II)

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18 pages, 9368 KiB

Open AccessArticle

Characterization of Damage Progress in the Defective Grouted Sleeve Connection Using Combined Acoustic Emission and Ultrasonics

by Lu Zhang, Zhenmin Fang, Yongze Tang, Hongyu Li and Qizhou Liu

Sensors 2022, 22(21), 8579; https://doi.org/10.3390/s22218579 - 7 Nov 2022

Cited by 4 | Viewed by 2009

Abstract

The grouted sleeve connection is one of the most widely used connections for prefabricated buildings (PBs). Usually, its quality can have a significant impact on the safety of the whole PB, especially for the internal flaws that form during sleeve grouting. It is [...] Read more.

The grouted sleeve connection is one of the most widely used connections for prefabricated buildings (PBs). Usually, its quality can have a significant impact on the safety of the whole PB, especially for the internal flaws that form during sleeve grouting. It is directly related to the mechanical performance and failure behavior of the grouted sleeve. Therefore, it is essential to understand the damage progression of the defective grouted sleeve connection. However, destructive testing is the mainstream measure to evaluate the grout sleeves, which is not applicable for in situ inspection. Therefore, this paper proposes a combined acoustic emission (AE) and ultrasonic testing (UT) method to characterize the damage progress of a grouted sleeve with different degrees of internal flaws under tensile loading. The UT was conducted before loading to evaluate the internal flaws. Additionally, the AE was used as the processing monitoring technique during the tensile testing. Two damage modes were identified: (i) brittle mode associated with the rebar pullout; (ii) ductile mode associated with the rapture of the rebar. The UT energy ratio was selected as the most sensitive feature to the internal flaws, both numerically and experimentally. The AE signatures of different damage phases and different damage modes were determined and characterized. For the brittle and ductile damage modes, two and three phases appeared in the AE activities, respectively. The proposed combined AE and UT method can provide a reliable and convenient nondestructive evaluation of grouted sleeves with internal flaws. Moreover, it can also characterize the damage progress of the grouted sleeve connections in real-time. Full article

(This article belongs to the Special Issue Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) for Infrastructure and Manufacturing)

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22 pages, 4729 KiB

Open AccessArticle

COVIDX-LwNet: A Lightweight Network Ensemble Model for the Detection of COVID-19 Based on Chest X-ray Images

by Wei Wang, Shuxian Liu, Huan Xu and Le Deng

Sensors 2022, 22(21), 8578; https://doi.org/10.3390/s22218578 - 7 Nov 2022

Cited by 4 | Viewed by 2384

Abstract

Recently, the COVID-19 pandemic coronavirus has put a lot of pressure on health systems around the world. One of the most common ways to detect COVID-19 is to use chest X-ray images, which have the advantage of being cheap and fast. However, in [...] Read more.

Recently, the COVID-19 pandemic coronavirus has put a lot of pressure on health systems around the world. One of the most common ways to detect COVID-19 is to use chest X-ray images, which have the advantage of being cheap and fast. However, in the early days of the COVID-19 outbreak, most studies applied pretrained convolutional neural network (CNN) models, and the features produced by the last convolutional layer were directly passed into the classification head. In this study, the proposed ensemble model consists of three lightweight networks, Xception, MobileNetV2 and NasNetMobile as three original feature extractors, and then three base classifiers are obtained by adding the coordinated attention module, LSTM and a new classification head to the original feature extractors. The classification results from the three base classifiers are then fused by a confidence fusion method. Three publicly available chest X-ray datasets for COVID-19 testing were considered, with ternary (COVID-19, normal and other pneumonia) and quaternary (COVID-19, normal) analyses performed on the first two datasets, bacterial pneumonia and viral pneumonia classification, and achieved high accuracy rates of 95.56% and 91.20%, respectively. The third dataset was used to compare the performance of the model compared to other models and the generalization ability on different datasets. We performed a thorough ablation study on the first dataset to understand the impact of each proposed component. Finally, we also performed visualizations. These saliency maps not only explain key prediction decisions of the model, but also help radiologists locate areas of infection. Through extensive experiments, it was finally found that the results obtained by the proposed method are comparable to the state-of-the-art methods. Full article

(This article belongs to the Special Issue Advanced Sensing and Image Processing Techniques for Healthcare Applications—2nd Edition)

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21 pages, 5249 KiB

Open AccessArticle

Adverse Weather Target Detection Algorithm Based on Adaptive Color Levels and Improved YOLOv5

by Jiale Yao, Xiangsuo Fan, Bing Li and Wenlin Qin

Sensors 2022, 22(21), 8577; https://doi.org/10.3390/s22218577 - 7 Nov 2022

Cited by 20 | Viewed by 3446

Abstract

With the continuous development of artificial intelligence and computer vision technology, autonomous vehicles have developed rapidly. Although self-driving vehicles have achieved good results in normal environments, driving in adverse weather can still pose a challenge to driving safety. To improve the detection ability [...] Read more.

With the continuous development of artificial intelligence and computer vision technology, autonomous vehicles have developed rapidly. Although self-driving vehicles have achieved good results in normal environments, driving in adverse weather can still pose a challenge to driving safety. To improve the detection ability of self-driving vehicles in harsh environments, we first construct a new color levels offset compensation model to perform adaptive color levels correction on images, which can effectively improve the clarity of targets in adverse weather and facilitate the detection and recognition of targets. Then, we compare several common one-stage target detection algorithms and improve on the best-performing YOLOv5 algorithm. We optimize the parameters of the Backbone of the YOLOv5 algorithm by increasing the number of model parameters and incorporating the Transformer and CBAM into the YOLOv5 algorithm. At the same time, we use the loss function of EIOU to replace the loss function of the original CIOU. Finally, through the ablation experiment comparison, the improved algorithm improves the detection rate of the targets, with the mAP reaching 94.7% and the FPS being 199.86. Full article

(This article belongs to the Special Issue Control Systems, Vision Technology and Sensor Fusion for Unmanned Robotic Vehicles)

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17 pages, 17815 KiB

Open AccessArticle

Analysis of Ultrasonic Machining Characteristics under Dynamic Load

by Zhangping Chen, Xinghong Zhao, Shixing Chen, Honghuan Chen, Pengfei Ni and Fan Zhang

Sensors 2022, 22(21), 8576; https://doi.org/10.3390/s22218576 - 7 Nov 2022

Cited by 2 | Viewed by 2279

Abstract

This research focuses on the load characteristics of piezoelectric transducers in the process of longitudinal vibration ultrasonic welding. We are primarily interested in the impedance characteristics of the piezoelectric transducer during loading, which is studied by leveraging the equivalent circuit theory of piezoelectric [...] Read more.

This research focuses on the load characteristics of piezoelectric transducers in the process of longitudinal vibration ultrasonic welding. We are primarily interested in the impedance characteristics of the piezoelectric transducer during loading, which is studied by leveraging the equivalent circuit theory of piezoelectric transducers. Specifically, we propose a cross-value mapping method. This method can well map the load change in ultrasonic welding to the impedance change, aiming to obtain an equivalent model of impedance and load. The least-squares strategy is used for parameter identification during data fitting. Extensive simulations and physical experiments are conducted to verify the proposed model. As a result, we can empirically find that the result from our model agrees with the impedance characteristics from the real-life data measured by the impedance meter, indicating its potential for real practice in controller research and transducer design. Full article

(This article belongs to the Special Issue The Development of Piezoelectric Sensors and Actuators)

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17 pages, 5924 KiB

Open AccessArticle

Compact Camera Fluorescence Detector for Parallel-Light Lens-Based Real-Time PCR System

by Seul-Bit-Na Koo, Yu-Seop Kim, Chan-Young Park and Deuk-Ju Lee

Sensors 2022, 22(21), 8575; https://doi.org/10.3390/s22218575 - 7 Nov 2022

Cited by 1 | Viewed by 2010

Abstract

The polymerase chain reaction is an important technique in biological research. However, it is time consuming and has a number of disadvantages. Therefore, real-time PCR technology that can be used in real-time monitoring has emerged, and many studies are being conducted regarding its [...] Read more.

The polymerase chain reaction is an important technique in biological research. However, it is time consuming and has a number of disadvantages. Therefore, real-time PCR technology that can be used in real-time monitoring has emerged, and many studies are being conducted regarding its use. Real-time PCR requires many optical components and imaging devices such as expensive, high-performance cameras. Therefore, its cost and assembly process are limitations to its use. Currently, due to the development of smart camera devices, small, inexpensive cameras and various lenses are being developed. In this paper, we present a Compact Camera Fluorescence Detector for use in parallel-light lens-based real-time PCR devices. The proposed system has a simple optical structure, the system cost can be reduced, and the size can be miniaturized. This system only incorporates Fresnel lenses without additional optics in order for the same field of view to be achieved for 25 tubes. In the center of the Fresnel lens, one LED and a complementary metal-oxide semiconductor camera were placed in directions that were as similar as possible. In addition, to achieve the accurate analysis of the results, image processing was used to correct them. As a result of an experiment using a reference fluorescent substance and double-distilled water, it was confirmed that stable fluorescence detection was possible. Full article

(This article belongs to the Special Issue I3S 2022 Selected Papers)

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32 pages, 11796 KiB

Open AccessArticle

Piton: Investigating the Controllability of a Wearable Telexistence Robot

by Abdullah Iskandar, Mohammed Al-Sada, Tamon Miyake, Yamen Saraiji, Osama Halabi and Tatsuo Nakajima

Sensors 2022, 22(21), 8574; https://doi.org/10.3390/s22218574 - 7 Nov 2022

Cited by 4 | Viewed by 4430

Abstract

The COVID-19 pandemic impacted collaborative activities, travel, and physical contact, increasing the demand for real-time interactions with remote environments. However, the existing remote communication solutions provide limited interactions and do not convey a high sense of presence within a remote environment. Therefore, we [...] Read more.

The COVID-19 pandemic impacted collaborative activities, travel, and physical contact, increasing the demand for real-time interactions with remote environments. However, the existing remote communication solutions provide limited interactions and do not convey a high sense of presence within a remote environment. Therefore, we propose a snake-shaped wearable telexistence robot, called Piton, that can be remotely used for a variety of collaborative applications. To the best of our knowledge, Piton is the first snake-shaped wearable telexistence robot. We explain the implementation of Piton, its control architecture, and discuss how Piton can be deployed in a variety of contexts. We implemented three control methods to control Piton: HM—using a head-mounted display (HMD), HH—using an HMD and hand-held tracker, and FM—using an HMD and a foot-mounted tracker. We conducted a user study to investigate the applicability of the proposed control methods for telexistence, focusing on body ownership (Alpha IVBO), mental and physical load (NASA-TLX), motion sickness (VRSQ), and a questionnaire to measure user impressions. The results show that both the HM and HH provide relevantly high levels of body ownership, had high perceived accuracy, and were highly favored, whereas the FM control method yielded the lowest body ownership effect and was least favored. We discuss the results and highlight the advantages and shortcomings of the control methods with respect to various potential application contexts. Based on our design and evaluation of Piton, we extracted a number of insights and future research directions to deepen our investigation and realization of wearable telexistence robots. Full article

(This article belongs to the Special Issue Challenges and Future Trends of Wearable Robotics)

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13 pages, 5363 KiB

Open AccessArticle

Research on a Non-Contact Multi-Electrode Voltage Sensor and Signal Processing Algorithm

by Wenbin Zhang, Yonglong Yang, Jingjing Zhao, Rujin Huang, Kang Cheng and Mingxing He

Sensors 2022, 22(21), 8573; https://doi.org/10.3390/s22218573 - 7 Nov 2022

Cited by 4 | Viewed by 3824

Abstract

Traditional contact voltage measurement requires a direct electrical connection to the system, which is not easy to install and maintain. The voltage measurement based on the electric field coupling plate capacitance structure does not need to be in contact with the measured object [...] Read more.

Traditional contact voltage measurement requires a direct electrical connection to the system, which is not easy to install and maintain. The voltage measurement based on the electric field coupling plate capacitance structure does not need to be in contact with the measured object or the ground, which can avoid the above problems. However, most of the existing flat-plate structure voltage measurement sensors are not only expensive to manufacture, but also bulky, and when the relative position between the wire under test and the sensor changes, it will bring great measurement errors, making it difficult to meet actual needs. Aiming to address the above problems, this paper proposes a multi-electrode array structure non-contact voltage sensor and signal processing algorithm. The sensor is manufactured by the PCB process, which effectively reduces the manufacturing cost and process difficulty. The experimental and simulation results show that, when the relative position of the wire and the sensor is offset by 10 mm in the 45° direction, the relative error of the traditional single-electrode voltage sensor is 17.62%, while the relative error of the multi-electrode voltage sensor designed in this paper is only 0.38%. In addition, the ratio error of the sensor under the condition of power frequency of 50 Hz is less than ±1% and the phase difference is less than 4°. The experimental results show that the sensor has good accuracy and linearity. Full article

(This article belongs to the Collection Multi-Sensor Information Fusion)

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13 pages, 4226 KiB

Open AccessArticle

Chronic and Acute Effects on Skin Temperature from a Sport Consisting of Repetitive Impacts from Hitting a Ball with the Hands

by Jose Luis Sánchez-Jiménez, Robert Tejero-Pastor, María del Carmen Calzadillas-Valles, Irene Jimenez-Perez, Rosa Maria Cibrián Ortiz de Anda, Rosario Salvador-Palmer and Jose Ignacio Priego-Quesada

Sensors 2022, 22(21), 8572; https://doi.org/10.3390/s22218572 - 7 Nov 2022

Cited by 1 | Viewed by 1862

Abstract

Valencian handball consists in hitting the ball with the hands and it may contribute to injury development on the hands. This study aimed to analyze skin temperature asymmetries and recovery after a cold stress test (CST) in professional players of Valencian handball before [...] Read more.

Valencian handball consists in hitting the ball with the hands and it may contribute to injury development on the hands. This study aimed to analyze skin temperature asymmetries and recovery after a cold stress test (CST) in professional players of Valencian handball before and after a competition. Thirteen professional athletes and a control group of ten physically active participants were measured. For both groups, infrared images were taken at the baseline condition; later they underwent a thermal stress test (pressing for 2 min with the palm of the hand on a metal plate) and then recovery images were taken. In athletes, the images were also taken after their competition. Athletes at baseline condition presented lower temperatures (p < 0.05) in the dominant hand compared with the non-dominant hand. There were asymmetries in all regions after their match (p < 0.05). After CST, a higher recovery rate was found after the game. The regions with the most significant differences in variation, asymmetries and recovery patterns were the index, middle and ring fingers, and the palm of the dominant hand. Taking into account that lower temperatures and the absence of temperature variation may be the consequence of a vascular adaptation, thermography could be used as a method to prevent injuries in athletes from Valencian handball. Full article

(This article belongs to the Special Issue Applications, Wearables and Sensing Technology in Sports and Physical Activity)

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14 pages, 3226 KiB

Open AccessArticle

Monopole Antenna with Enhanced Bandwidth and Stable Radiation Patterns Using Metasurface and Cross-Ground Structure

by Patrick Danuor, Kyei Anim and Young-Bae Jung

Sensors 2022, 22(21), 8571; https://doi.org/10.3390/s22218571 - 7 Nov 2022

Cited by 3 | Viewed by 3636

Abstract

In this paper, a printed monopole antenna with stable omnidirectional radiation patterns is presented for applications in ocean buoy and the marine Internet of Things (IoT). The antenna is composed of a rectangular patch, a cross-ground structure, and two frequency-selective surface (FSS) unit [...] Read more.

In this paper, a printed monopole antenna with stable omnidirectional radiation patterns is presented for applications in ocean buoy and the marine Internet of Things (IoT). The antenna is composed of a rectangular patch, a cross-ground structure, and two frequency-selective surface (FSS) unit cells. The cross-ground structure is incorporated into the antenna design to maintain consistent monopole-like radiation patterns over the antenna’s operating band, and the FSS unit cells are placed at the backside of the antenna to improve the antenna gain aiming at the L-band. In addition, the FSS unit cells exhibit resonance characteristics that, when incorporated with the cross-ground structure, result in a broader impedance bandwidth compared to the conventional monopole antenna. To validate the structure, a prototype is fabricated and measured. Good agreement between the simulated and measured results show that the proposed antenna exhibits an impedance bandwidth of 83.2% from 1.65 to 4 GHz, compared to the conventional printed monopole antenna. The proposed antenna realizes a peak gain of 4.57 dBi and a total efficiency of 97% at 1.8 GHz. Full article

(This article belongs to the Special Issue Antenna Design and Sensors for Internet of Things)

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19 pages, 3511 KiB

Open AccessArticle

Binary PSO with Classification Trees Algorithm for Enhancing Power Efficiency in 5G Networks

by Mayada Osama, Salwa El Ramly and Bassant Abdelhamid

Sensors 2022, 22(21), 8570; https://doi.org/10.3390/s22218570 - 7 Nov 2022

Cited by 2 | Viewed by 1971

Abstract

The dense deployment of small cells (SCs) in the 5G heterogeneous networks (HetNets) fulfills the demand for vast connectivity and larger data rates. Unfortunately, the power efficiency (PE) of the network is reduced because of the elevated power consumption of the densely deployed [...] Read more.

The dense deployment of small cells (SCs) in the 5G heterogeneous networks (HetNets) fulfills the demand for vast connectivity and larger data rates. Unfortunately, the power efficiency (PE) of the network is reduced because of the elevated power consumption of the densely deployed SCs and the interference that arise between them. An approach to ameliorate the PE is proposed by switching off the redundant SCs using machine learning (ML) techniques while sustaining the quality of service (QoS) for each user. In this paper, a linearly increasing inertia weight–binary particle swarm optimization (IW-BPSO) algorithm for SC on/off switching is proposed to minimize the power consumption of the network. Moreover, a soft frequency reuse (SFR) algorithm is proposed using classification trees (CTs) to alleviate the interference and elevate the system throughput. The results show that the proposed algorithms outperform the other conventional algorithms, as they reduce the power consumption of the network and the interference among the SCs, ameliorating the total throughput and the PE of the system. Full article

(This article belongs to the Special Issue Energy-Efficient Communication Networks and Systems)

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19 pages, 2299 KiB

Open AccessArticle

Design and Implementation of a Cloud PACS Architecture

by Jacek Kawa, Bartłomiej Pyciński, Michał Smoliński, Paweł Bożek, Marek Kwasecki, Bartosz Pietrzyk and Dariusz Szymański

Sensors 2022, 22(21), 8569; https://doi.org/10.3390/s22218569 - 7 Nov 2022

Cited by 5 | Viewed by 7077

Abstract

The limitations of the classic PACS (picture archiving and communication system), such as the backward-compatible DICOM network architecture and poor security and maintenance, are well-known. They are challenged by various existing solutions employing cloud-related patterns and services. However, a full-scale cloud-native PACS has [...] Read more.

The limitations of the classic PACS (picture archiving and communication system), such as the backward-compatible DICOM network architecture and poor security and maintenance, are well-known. They are challenged by various existing solutions employing cloud-related patterns and services. However, a full-scale cloud-native PACS has not yet been demonstrated. The paper introduces a vendor-neutral cloud PACS architecture. It is divided into two main components: a cloud platform and an access device. The cloud platform is responsible for nearline (long-term) image archive, data flow, and backend management. It operates in multi-tenant mode. The access device is responsible for the local DICOM (Digital Imaging and Communications in Medicine) interface and serves as a gateway to cloud services. The cloud PACS was first implemented in an Amazon Web Services environment. It employs a number of general-purpose services designed or adapted for a cloud environment, including Kafka, OpenSearch, and Memcached. Custom services, such as a central PACS node, queue manager, or flow worker, also developed as cloud microservices, bring DICOM support, external integration, and a management layer. The PACS was verified using image traffic from, among others, computed tomography (CT), magnetic resonance (MR), and computed radiography (CR) modalities. During the test, the system was reliably storing and accessing image data. In following tests, scaling behavior differences between the monolithic Dcm4chee server and the proposed solution are shown. The growing number of parallel connections did not influence the monolithic server’s overall throughput, whereas the performance of cloud PACS noticeably increased. In the final test, different retrieval patterns were evaluated to assess performance under different scenarios. The current production environment stores over 450 TB of image data and handles over 4000 DICOM nodes. Full article

(This article belongs to the Topic Advanced Systems Engineering: Theory and Applications)

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23 pages, 1630 KiB

Open AccessArticle

Detection of Physical Activity Using Machine Learning Methods Based on Continuous Blood Glucose Monitoring and Heart Rate Signals

by Lehel Dénes-Fazakas, Máté Siket, László Szilágyi, Levente Kovács and György Eigner

Sensors 2022, 22(21), 8568; https://doi.org/10.3390/s22218568 - 7 Nov 2022

Cited by 9 | Viewed by 3028

Abstract

Non-coordinated physical activity may lead to hypoglycemia, which is a dangerous condition for diabetic people. Decision support systems related to type 1 diabetes mellitus (T1DM) still lack the capability of automated therapy modification by recognizing and categorizing the physical activity. Further, this desired [...] Read more.

Non-coordinated physical activity may lead to hypoglycemia, which is a dangerous condition for diabetic people. Decision support systems related to type 1 diabetes mellitus (T1DM) still lack the capability of automated therapy modification by recognizing and categorizing the physical activity. Further, this desired adaptive therapy should be achieved without increasing the administrative load, which is already high for the diabetic community. These requirements can be satisfied by using artificial intelligence-based solutions, signals collected by wearable devices, and relying on the already available data sources, such as continuous glucose monitoring systems. In this work, we focus on the detection of physical activity by using a continuous glucose monitoring system and a wearable sensor providing the heart rate—the latter is accessible even in the cheapest wearables. Our results show that the detection of physical activity is possible based on these data sources, even if only low-complexity artificial intelligence models are deployed. In general, our models achieved approximately 90% accuracy in the detection of physical activity. Full article

(This article belongs to the Special Issue Recent Advances in Digital Healthcare and Applications)

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19 pages, 6345 KiB

Open AccessArticle

Rice Crop Counting Using Aerial Imagery and GIS for the Assessment of Soil Health to Increase Crop Yield

by Syeda Iqra Hassan, Muhammad Mansoor Alam, Muhammad Yousuf Irfan Zia, Muhammad Rashid, Usman Illahi and Mazliham Mohd Su’ud

Sensors 2022, 22(21), 8567; https://doi.org/10.3390/s22218567 - 7 Nov 2022

Cited by 10 | Viewed by 3799

Abstract

Rice is one of the vital foods consumed in most countries throughout the world. To estimate the yield, crop counting is used to indicate improper growth, identification of loam land, and control of weeds. It is becoming necessary to grow crops healthy, precisely, [...] Read more.

Rice is one of the vital foods consumed in most countries throughout the world. To estimate the yield, crop counting is used to indicate improper growth, identification of loam land, and control of weeds. It is becoming necessary to grow crops healthy, precisely, and proficiently as the demand increases for food supplies. Traditional counting methods have numerous disadvantages, such as long delay times and high sensitivity, and they are easily disturbed by noise. In this research, the detection and counting of rice plants using an unmanned aerial vehicle (UAV) and aerial images with a geographic information system (GIS) are used. The technique is implemented in the area of forty acres of rice crop in Tando Adam, Sindh, Pakistan. To validate the performance of the proposed system, the obtained results are compared with the standard plant count techniques as well as approved by the agronomist after testing soil and monitoring the rice crop count in each acre of land of rice crops. From the results, it is found that the proposed system is precise and detects rice crops accurately, differentiates from other objects, and estimates the soil health based on plant counting data; however, in the case of clusters, the counting is performed in semi-automated mode. Full article

(This article belongs to the Section Smart Agriculture)

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18 pages, 2498 KiB

Open AccessArticle

A Hybrid Spider Monkey and Hierarchical Particle Swarm Optimization Approach for Intrusion Detection on Internet of Things

by Sandhya Ethala and Annapurani Kumarappan

Sensors 2022, 22(21), 8566; https://doi.org/10.3390/s22218566 - 7 Nov 2022

Cited by 16 | Viewed by 2272

Abstract

The Internet of Things (IoT) network integrates physical objects such as sensors, networks, and electronics with software to collect and exchange data. Physical objects with a unique IP address communicate with external entities over the internet to exchange data in the network. Due [...] Read more.

The Internet of Things (IoT) network integrates physical objects such as sensors, networks, and electronics with software to collect and exchange data. Physical objects with a unique IP address communicate with external entities over the internet to exchange data in the network. Due to a lack of security measures, these network entities are vulnerable to severe attacks. To address this, an efficient security mechanism for dealing with the threat and detecting attacks is necessary. The proposed hybrid optimization approach combines Spider Monkey Optimization (SMO) and Hierarchical Particle Swarm Optimization (HPSO) to handle the huge amount of intrusion data classification problems and improve detection accuracy by minimizing false alarm rates. After finding the best optimum values, the Random Forest Classifier (RFC) was used to classify attacks from the NSL-KDD and UNSW-NB 15 datasets. The SVM model obtained accuracy of 91.82%, DT of 98.99%, and RFC of 99.13%, and the proposed model obtained 99.175% for the NSL-KDD dataset. Similarly, SVM obtained accuracy of 85.88%, DT of 88.87%, RFC of 91.65%, and the proposed model obtained 99.18% for the UNSW NB-15 dataset. The proposed model achieved accuracy of 99.175% for the NSL-KDD dataset which is higher than the state-of-the-art techniques such as DNN of 97.72% and Ensemble Learning at 85.2%. Full article

(This article belongs to the Special Issue Advanced Management of Fog/Edge Networks and IoT Sensors Devices)

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20 pages, 4045 KiB

Open AccessArticle

A Generic Pixel Pitch Calibration Method for Fundus Camera via Automated ROI Extraction

by Tengfei Long, Yi Xu, Haidong Zou, Lina Lu, Tianyi Yuan, Zhou Dong, Jiqun Dong, Xin Ke, Saiguang Ling and Yingyan Ma

Sensors 2022, 22(21), 8565; https://doi.org/10.3390/s22218565 - 7 Nov 2022

Cited by 11 | Viewed by 2615

Abstract

Pixel pitch calibration is an essential step to make the fundus structures in the fundus image quantitatively measurable, which is important for the diagnosis and treatment of many diseases, e.g., diabetes, arteriosclerosis, hereditary optic atrophy, etc. The conventional calibration approaches require the specific [...] Read more.

Pixel pitch calibration is an essential step to make the fundus structures in the fundus image quantitatively measurable, which is important for the diagnosis and treatment of many diseases, e.g., diabetes, arteriosclerosis, hereditary optic atrophy, etc. The conventional calibration approaches require the specific parameters of the fundus camera or several specially shot images of the chess board, but these are generally not accessible, and the calibration results cannot be generalized to other cameras. Based on automated ROI (region of interest) and optic disc detection, the diameter ratio of ROI and optic disc (ROI–disc ratio) is quantitatively analyzed for a large number of fundus images. With the prior knowledge of the average diameter of an optic disc in fundus, the pixel pitch can be statistically estimated from a large number of fundus images captured by a specific camera without the availability of chess board images or detailed specifics of the fundus camera. Furthermore, for fundus cameras of FOV (fixed field-of-view), the pixel pitch of a fundus image of 45° FOV can be directly estimated according to the automatically measured diameter of ROI in the pixel. The average ROI–disc ratio is approximately constant, i.e., 6.404 ± 0.619 in the pixel, according to 40,600 fundus images, captured by different cameras, of 45° FOV. In consequence, the pixel pitch of a fundus image of 45° FOV can be directly estimated according to the automatically measured diameter of ROI in the pixel, and results show the pixel pitches of Canon CR2, Topcon NW400, Zeiss Visucam 200, and Newvision RetiCam 3100 cameras are 6.825 ± 0.666 μm, 6.625 ± 0.647 μm, 5.793 ± 0.565 μm, and 5.884 ± 0.574 μm, respectively. Compared with the manually measured pixel pitches, based on the method of ISO 10940:2009, i.e., 6.897 μm, 6.807 μm, 5.693 μm, and 6.050 μm, respectively, the bias of the proposed method is less than 5%. Since our method doesn’t require chess board images or detailed specifics, the fundus structures on the fundus image can be measured accurately, according to the pixel pitch obtained by this method, without knowing the type and parameters of the camera. Full article

(This article belongs to the Collection Biomedical Imaging & Instrumentation)

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21 pages, 5892 KiB

Open AccessReview

Smart Home Privacy Protection Methods against a Passive Wireless Snooping Side-Channel Attack

by Mohammad Ali Nassiri Abrishamchi, Anazida Zainal, Fuad A. Ghaleb, Sultan Noman Qasem and Abdullah M. Albarrak

Sensors 2022, 22(21), 8564; https://doi.org/10.3390/s22218564 - 7 Nov 2022

Cited by 10 | Viewed by 3786

Abstract

Smart home technologies have attracted more users in recent years due to significant advancements in their underlying enabler components, such as sensors, actuators, and processors, which are spreading in various domains and have become more affordable. However, these IoT-based solutions are prone to [...] Read more.

Smart home technologies have attracted more users in recent years due to significant advancements in their underlying enabler components, such as sensors, actuators, and processors, which are spreading in various domains and have become more affordable. However, these IoT-based solutions are prone to data leakage; this privacy issue has motivated researchers to seek a secure solution to overcome this challenge. In this regard, wireless signal eavesdropping is one of the most severe threats that enables attackers to obtain residents’ sensitive information. Even if the system encrypts all communications, some cyber attacks can still steal information by interpreting the contextual data related to the transmitted signals. For example, a “fingerprint and timing-based snooping (FATS)” attack is a side-channel attack (SCA) developed to infer in-home activities passively from a remote location near the targeted house. An SCA is a sort of cyber attack that extracts valuable information from smart systems without accessing the content of data packets. This paper reviews the SCAs associated with cyber–physical systems, focusing on the proposed solutions to protect the privacy of smart homes against FATS attacks in detail. Moreover, this work clarifies shortcomings and future opportunities by analyzing the existing gaps in the reviewed methods. Full article

(This article belongs to the Collection IoT and Smart Homes)

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20 pages, 4348 KiB

Open AccessArticle

Robust Estimation and Optimized Transmission of 3D Feature Points for Computer Vision on Mobile Communication Network

by Jin-Kyum Kim, Byung-Seo Park, Woosuk Kim, Jung-Tak Park, Sol Lee and Young-Ho Seo

Sensors 2022, 22(21), 8563; https://doi.org/10.3390/s22218563 - 7 Nov 2022

Cited by 1 | Viewed by 2416

Abstract

Due to the amount of transmitted data and the security of personal or private information in wireless communication, there are cases where the information for a multimedia service should be directly transferred from the user’s device to the cloud server without the captured [...] Read more.

Due to the amount of transmitted data and the security of personal or private information in wireless communication, there are cases where the information for a multimedia service should be directly transferred from the user’s device to the cloud server without the captured original images. This paper proposes a new method to generate 3D (dimensional) keypoints based on a user’s mobile device with a commercial RGB camera in a distributed computing environment such as a cloud server. The images are captured with a moving camera and 2D keypoints are extracted from them. After executing feature extraction between continuous frames, disparities are calculated between frames using the relationships between matched keypoints. The physical distance of the baseline is estimated by using the motion information of the camera, and the actual distance is calculated by using the calculated disparity and the estimated baseline. Finally, 3D keypoints are generated by adding the extracted 2D keypoints to the calculated distance. A keypoint-based scene change method is proposed as well. Due to the existing similarity between continuous frames captured from a camera, not all 3D keypoints are transferred and stored, only the new ones. Compared with the ground truth of the TUM dataset, the average error of the estimated 3D keypoints was measured as 5.98 mm, which shows that the proposed method has relatively good performance considering that it uses a commercial RGB camera on a mobile device. Furthermore, the transferred 3D keypoints were decreased to about 73.6%. Full article

(This article belongs to the Special Issue Disruptive Technologies and Wireless Sensor Network Communication Algorithms)

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12 pages, 2762 KiB

Open AccessArticle

A Multi-AUV Maritime Target Search Method for Moving and Invisible Objects Based on Multi-Agent Deep Reinforcement Learning

by Guangcheng Wang, Fenglin Wei, Yu Jiang, Minghao Zhao, Kai Wang and Hong Qi

Sensors 2022, 22(21), 8562; https://doi.org/10.3390/s22218562 - 7 Nov 2022

Cited by 21 | Viewed by 2981

Abstract

Target search for moving and invisible objects has always been considered a challenge, as the floating objects drift with the flows. This study focuses on target search by multiple autonomous underwater vehicles (AUV) and investigates a multi-agent target search method (MATSMI) for moving [...] Read more.

Target search for moving and invisible objects has always been considered a challenge, as the floating objects drift with the flows. This study focuses on target search by multiple autonomous underwater vehicles (AUV) and investigates a multi-agent target search method (MATSMI) for moving and invisible objects. In the MATSMI algorithm, based on the multi-agent deep deterministic policy gradient (MADDPG) method, we add spatial and temporal information to the reinforcement learning state and set up specialized rewards in conjunction with a maritime target search scenario. Additionally, we construct a simulation environment to simulate a multi-AUV search for the floating object. The simulation results show that the MATSMI method has about 20% higher search success rate and about 70 steps shorter search time than the traditional search method. In addition, the MATSMI method converges faster than the MADDPG method. This paper provides a novel and effective method for solving the maritime target search problem. Full article

(This article belongs to the Special Issue Sensors, Modeling and Control for Intelligent Marine Robots)

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20 pages, 3127 KiB

Open AccessArticle

YPD-SLAM: A Real-Time VSLAM System for Handling Dynamic Indoor Environments

by Yi Wang, Haoyu Bu, Xiaolong Zhang and Jia Cheng

Sensors 2022, 22(21), 8561; https://doi.org/10.3390/s22218561 - 7 Nov 2022

Cited by 8 | Viewed by 2838

Abstract

Aiming at the problem that Simultaneous localization and mapping (SLAM) is greatly disturbed by many dynamic elements in the actual environment, this paper proposes a real-time Visual SLAM (VSLAM) algorithm to deal with a dynamic indoor environment. Firstly, a lightweight YoloFastestV2 deep learning [...] Read more.

Aiming at the problem that Simultaneous localization and mapping (SLAM) is greatly disturbed by many dynamic elements in the actual environment, this paper proposes a real-time Visual SLAM (VSLAM) algorithm to deal with a dynamic indoor environment. Firstly, a lightweight YoloFastestV2 deep learning model combined with NCNN and Mobile Neural Network (MNN) inference frameworks is used to obtain preliminary semantic information of images. The dynamic feature points are removed according to epipolar constraint and dynamic properties of objects between consecutive frames. Since reducing the number of feature points after rejection affects the pose estimation, this paper innovatively combines Cylinder and Plane Extraction (CAPE) planar detection. We generate planes from depth maps and then introduce planar and in-plane point constraints into the nonlinear optimization of SLAM. Finally, the algorithm is tested on the publicly available TUM (RGB-D) dataset, and the average improvement in localization accuracy over ORB-SLAM2, DS-SLAM, and RDMO-SLAM is about 91.95%, 27.21%, and 30.30% under dynamic sequences, respectively. The single-frame tracking time of the whole system is only 42.68 ms, which is 44.1%, being 14.6–34.33% higher than DS-SLAM, RDMO-SLAM, and RDS-SLAM respectively. The system that we proposed significantly increases processing speed, performs better in real-time, and is easily deployed on various platforms. Full article

(This article belongs to the Section Navigation and Positioning)

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12 pages, 3686 KiB

Open AccessArticle

A Fissure-Aided Registration Approach for Automatic Pulmonary Lobe Segmentation Using Deep Learning

by Mengfan Xue, Lu Han, Yiran Song, Fan Rao and Dongliang Peng

Sensors 2022, 22(21), 8560; https://doi.org/10.3390/s22218560 - 7 Nov 2022

Cited by 4 | Viewed by 2167

Abstract

The segmentation of pulmonary lobes is important in clinical assessment, lesion location, and surgical planning. Automatic lobe segmentation is challenging, mainly due to the incomplete fissures or the morphological variation resulting from lung disease. In this work, we propose a learning-based approach that [...] Read more.

The segmentation of pulmonary lobes is important in clinical assessment, lesion location, and surgical planning. Automatic lobe segmentation is challenging, mainly due to the incomplete fissures or the morphological variation resulting from lung disease. In this work, we propose a learning-based approach that incorporates information from the local fissures, the whole lung, and priori pulmonary anatomy knowledge to separate the lobes robustly and accurately. The prior pulmonary atlas is registered to the test CT images with the aid of the detected fissures. The result of the lobe segmentation is obtained by mapping the deformation function on the lobes-annotated atlas. The proposed method is evaluated in a custom dataset with COPD. Twenty-four CT scans randomly selected from the custom dataset were segmented manually and are available to the public. The experiments showed that the average dice coefficients were 0.95, 0.90, 0.97, 0.97, and 0.97, respectively, for the right upper, right middle, right lower, left upper, and left lower lobes. Moreover, the comparison of the performance with a former learning-based segmentation approach suggests that the presented method could achieve comparable segmentation accuracy and behave more robustly in cases with morphological specificity. Full article

(This article belongs to the Special Issue Biomedical Image Processing Based on Artificial Intelligence and Pattern Recognition Using Sensors)

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20 pages, 9391 KiB

Open AccessArticle

Design and Implementation of Embedded-Based Vein Image Processing System with Enhanced Denoising Capabilities

by Jongwon Lee, Incheol Jeong, Kapyol Kim and Jinsoo Cho

Sensors 2022, 22(21), 8559; https://doi.org/10.3390/s22218559 - 7 Nov 2022

Cited by 4 | Viewed by 2888

Abstract

In general, it is very difficult to visually locate blood vessels for intravenous injection or surgery. In addition, if vein detection fails, physical and mental pain occurs to the patient and leads to financial loss in the hospital. In order to prevent this [...] Read more.

In general, it is very difficult to visually locate blood vessels for intravenous injection or surgery. In addition, if vein detection fails, physical and mental pain occurs to the patient and leads to financial loss in the hospital. In order to prevent this problem, NIR-based vein detection technology is developing. The proposed study combines vein detection and digital hair removal to eliminate body hair, a noise that hinders the accuracy of detection, improving the performance of the entire algorithm by about 10.38% over existing systems. In addition, as a result of performing venous detection of patients without body hair, 5.04% higher performance than the existing system was detected, and the proposed study results were verified. It is expected that the use of devices to which the proposed study is applied will provide more accurate vascular maps in general situations. Full article

(This article belongs to the Section Sensing and Imaging)

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12 pages, 4560 KiB

Open AccessArticle

Imaging and Deep Learning Based Approach to Leaf Wetness Detection in Strawberry

by Arth M. Patel, Won Suk Lee and Natalia A. Peres

Sensors 2022, 22(21), 8558; https://doi.org/10.3390/s22218558 - 7 Nov 2022

Cited by 4 | Viewed by 2088

Abstract

The Strawberry Advisory System (SAS) is a tool developed to help Florida strawberry growers determine the risk of common fungal diseases and the need for fungicide applications. Leaf wetness duration (LWD) is one of the important parameters in SAS disease risk modeling. By [...] Read more.

The Strawberry Advisory System (SAS) is a tool developed to help Florida strawberry growers determine the risk of common fungal diseases and the need for fungicide applications. Leaf wetness duration (LWD) is one of the important parameters in SAS disease risk modeling. By accurately measuring the LWD, disease risk can be better assessed, leading to less fungicide use and more economic benefits to the farmers. This research aimed to develop and test a more accurate leaf wetness detection system than traditional leaf wetness sensors. In this research, a leaf wetness detection system was developed and tested using color imaging of a reference surface and a convolutional neural network (CNN), which is one of the artificial-intelligence-based learning methods. The system was placed at two separate field locations during the 2021–2022 strawberry-growing season. The results from the developed system were compared against manual observation to determine the accuracy of the system. It was found that the AI- and imaging-based system had high accuracy in detecting wetness on a reference surface. The developed system can be used in SAS for determining accurate disease risks and fungicide recommendations for strawberry production and allows the expansion of the system to multiple locations. Full article

(This article belongs to the Special Issue AI-Based Sensors and Sensing Systems for Smart Agriculture)

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12 pages, 23180 KiB

Open AccessArticle

Sagnac with Double-Sense Twisted Low-Birefringence Standard Fiber as Vibration Sensor

by Héctor Santiago-Hernández, Anuar Benjamín Beltrán-González, Azael Mora-Nuñez, Beethoven Bravo-Medina and Olivier Pottiez

Sensors 2022, 22(21), 8557; https://doi.org/10.3390/s22218557 - 7 Nov 2022

Cited by 1 | Viewed by 1841

Abstract

In this work, we study a double-sense twisted low-birefringence Sagnac loop structure as a sound/vibration sensing device. We study the relation between the adjustments of a wave retarder inside the loop (which allows controlling the transmission characteristic to deliver 10, 100, and 300 [...] Read more.

In this work, we study a double-sense twisted low-birefringence Sagnac loop structure as a sound/vibration sensing device. We study the relation between the adjustments of a wave retarder inside the loop (which allows controlling the transmission characteristic to deliver 10, 100, and 300

μ W

average power at the output of the system) and the response of the Sagnac sensor to vibration frequencies ranging from 0 to 22 kHz. For a 300 m loop Sagnac, two sets of experiments were carried out, playing at the same time all the sound frequencies mixed for ∼1 s, and playing a sweep of frequencies for 30 s. In both cases, the time- and frequency-domain transmission amplitudes are larger for an average power of 10

μ W

, and smaller for an average power of 300

μ W

. For mixed frequencies, the Fourier analysis shows that the Sagnac response is larger for low frequencies (from 0 to ∼5 kHz) than for high frequencies (from ∼5 kHz to ∼22 kHz). For a sweep of frequencies, the results reveal that the interferometer perceives all frequencies. However, beyond ∼2.5 kHz, harmonics are present each ∼50 Hz, revealing that some resonances are present. The results about the influence of the power transmission through the polarizer and power emission of laser diode (LD) on the Sagnac interferometer response at high frequencies reveal that our system is robust, and the results are highly reproducible, and harmonics do not depend on the state of polarization at the input of the Sagnac interferometer. Furthermore, increasing the LD output power from 5 mW to 67.5 mW allows us to eliminate noisy signals at the system output. in our setup, the minimum sound level detected was 56 dB. On the other hand, the experimental results of a 10 m loop OFSI reveal that the response at low frequencies (1.5 kHz to 5 kHz) is minor compared with the 300 m loop OFSI. However, the response at high frequencies is low but still enables the detection of these frequencies, yielding the possibility of tuning the response of the vibration sensor by varying the length of the Sagnac loop. Full article

(This article belongs to the Special Issue Advances in Fiber Laser Sensors)

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6 pages, 198 KiB

Open AccessEditorial

From Sensor Data to Educational Insights

by José A. Ruipérez-Valiente, Roberto Martínez-Maldonado, Daniele Di Mitri and Jan Schneider

Sensors 2022, 22(21), 8556; https://doi.org/10.3390/s22218556 - 7 Nov 2022

Cited by 7 | Viewed by 1987

Abstract

Technology is gradually becoming an integral part of learning at all levels of educational [...] Full article

(This article belongs to the Special Issue From Sensor Data to Educational Insights)

16 pages, 3943 KiB

Open AccessArticle

Reliability and Validity of Inertial Sensor Assisted Reaction Time Measurement Tools among Healthy Young Adults

by Brent Harper, Michael Shiraishi and Rahul Soangra

Sensors 2022, 22(21), 8555; https://doi.org/10.3390/s22218555 - 6 Nov 2022

Cited by 3 | Viewed by 2573

Abstract

The assessment of movement reaction time (RT) as a sideline assessment is a valuable biomarker for mild TBI or concussion. However, such assessments require controlled laboratory environments, which may not be feasible for sideline testing during a game. Body-worn wearable devices are advantageous [...] Read more.

The assessment of movement reaction time (RT) as a sideline assessment is a valuable biomarker for mild TBI or concussion. However, such assessments require controlled laboratory environments, which may not be feasible for sideline testing during a game. Body-worn wearable devices are advantageous as being cost-effective, easy to don and use, wirelessly transmit data, and ensure unhindered movement performance. This study aimed to develop a Drop-stick Test System (DTS) with a wireless inertial sensor and confirm its reliability for different standing conditions (Foam versus No Foam) and task types (Single versus Dual), and postures (Standing versus sitting). Fourteen healthy young participants (seven females, seven males; age 24.7 ± 2.6 years) participated in this study. The participants were asked to catch a falling stick attached to the sensor during a drop test. Reaction Times (RTs) were calculated from data for each trial from DTS and laboratory camera system (gold standard). Intraclass correlation coefficients (ICC 3,k) were computed to determine inter-instrument reliability. The RT measurements from participants using the camera system and sensor-based DTS showed moderate to good inter-instrument reliability with an overall ICC of 0.82 (95% CI 0.78–0.85). Bland–Altman plots and 95% levels of agreement revealed a bias where the DTS underestimated RT by approximately 50 ms. Full article

(This article belongs to the Special Issue Applications, Wearables and Sensing Technology in Sports and Physical Activity)

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19 pages, 5962 KiB

Open AccessArticle

Integrated Video and Acoustic Emission Data Fusion for Intelligent Decision Making in Material Surface Inspection System

by Andrey V. Chernov, Ilias K. Savvas, Alexander A. Alexandrov, Oleg O. Kartashov, Dmitry S. Polyanichenko, Maria A. Butakova and Alexander V. Soldatov

Sensors 2022, 22(21), 8554; https://doi.org/10.3390/s22218554 - 6 Nov 2022

Cited by 3 | Viewed by 2799

Abstract

In the field of intelligent surface inspection systems, particular attention is paid to decision making problems, based on data from different sensors. The combination of such data helps to make an intelligent decision. In this research, an approach to intelligent decision making based [...] Read more.

In the field of intelligent surface inspection systems, particular attention is paid to decision making problems, based on data from different sensors. The combination of such data helps to make an intelligent decision. In this research, an approach to intelligent decision making based on a data integration strategy to raise awareness of a controlled object is used. In the following article, this approach is considered in the context of reasonable decisions when detecting defects on the surface of welds that arise after the metal pipe welding processes. The main data types were RGB, RGB-D images, and acoustic emission signals. The fusion of such multimodality data, which mimics the eyes and ears of an experienced person through computer vision and digital signal processing, provides more concrete and meaningful information for intelligent decision making. The main results of this study include an overview of the architecture of the system with a detailed description of its parts, methods for acquiring data from various sensors, pseudocodes for data processing algorithms, and an approach to data fusion meant to improve the efficiency of decision making in detecting defects on the surface of various materials. Full article

(This article belongs to the Section Internet of Things)

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