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Madahana MCI, Ekoru JED, Sebothoma B, Khoza-Shangase K. Development of an artificial intelligence based occupational noise induced hearing loss early warning system for mine workers. Front Neurosci 2024; 18:1321357. [PMID: 38576872 PMCID: PMC10991701 DOI: 10.3389/fnins.2024.1321357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 03/06/2024] [Indexed: 04/06/2024] Open
Abstract
Introduction Occupational Noise Induced Hearing Loss (ONIHL) is one of the most prevalent conditions among mine workers globally. This reality is due to mine workers being exposed to noise produced by heavy machinery, rock drilling, blasting, and so on. This condition can be compounded by the fact that mine workers often work in confined workspaces for extended periods of time, where little to no attenuation of noise occurs. The objective of this research work is to present a preliminary study of the development of a hearing loss, early monitoring system for mine workers. Methodology The system consists of a smart watch and smart hearing muff equipped with sound sensors which collect noise intensity levels and the frequency of exposure. The collected information is transferred to a database where machine learning algorithms namely the logistic regression, support vector machines, decision tree and Random Forest Classifier are used to classify and cluster it into levels of priority. Feedback is then sent from the database to a mine worker smart watch based on priority level. In cases where the priority level is extreme, indicating high levels of noise, the smart watch vibrates to alert the miner. The developed system was tested in a mock mine environment consisting of a 67 metres tunnel located in the basement of a building whose roof top represents the "surface" of a mine. The mock-mine shape, size of the tunnel, steel-support infrastructure, and ventilation system are analogous to deep hard-rock mine. The wireless channel propagation of the mock-mine is statistically characterized in 2.4-2.5 GHz frequency band. Actual underground mine material was used to build the mock mine to ensure it mimics a real mine as close as possible. The system was tested by 50 participants both male and female ranging from ages of 18 to 60 years. Results and discussion Preliminary results of the system show decision tree had the highest accuracy compared to the other algorithms used. It has an average testing accuracy of 91.25% and average training accuracy of 99.79%. The system also showed a good response level in terms of detection of noise input levels of exposure, transmission of the information to the data base and communication of recommendations to the miner. The developed system is still undergoing further refinements and testing prior to being tested in an actual mine.
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Affiliation(s)
- Milka C. I. Madahana
- School of Mining Engineering, University of the Witwatersrand, Johannesburg, South Africa
| | - John E. D. Ekoru
- School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
| | - Ben Sebothoma
- Department of Audiology, University of the Witwatersrand, Johannesburg, South Africa
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Triana AM, Saramäki J, Glerean E, Hayward NMEA. Neuroscience meets behavior: A systematic literature review on magnetic resonance imaging of the brain combined with real-world digital phenotyping. Hum Brain Mapp 2024; 45:e26620. [PMID: 38436603 PMCID: PMC10911114 DOI: 10.1002/hbm.26620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 01/19/2024] [Accepted: 01/24/2024] [Indexed: 03/05/2024] Open
Abstract
A primary goal of neuroscience is to understand the relationship between the brain and behavior. While magnetic resonance imaging (MRI) examines brain structure and function under controlled conditions, digital phenotyping via portable automatic devices (PAD) quantifies behavior in real-world settings. Combining these two technologies may bridge the gap between brain imaging, physiology, and real-time behavior, enhancing the generalizability of laboratory and clinical findings. However, the use of MRI and data from PADs outside the MRI scanner remains underexplored. Herein, we present a Preferred Reporting Items for Systematic Reviews and Meta-Analysis systematic literature review that identifies and analyzes the current state of research on the integration of brain MRI and PADs. PubMed and Scopus were automatically searched using keywords covering various MRI techniques and PADs. Abstracts were screened to only include articles that collected MRI brain data and PAD data outside the laboratory environment. Full-text screening was then conducted to ensure included articles combined quantitative data from MRI with data from PADs, yielding 94 selected papers for a total of N = 14,778 subjects. Results were reported as cross-frequency tables between brain imaging and behavior sampling methods and patterns were identified through network analysis. Furthermore, brain maps reported in the studies were synthesized according to the measurement modalities that were used. Results demonstrate the feasibility of integrating MRI and PADs across various study designs, patient and control populations, and age groups. The majority of published literature combines functional, T1-weighted, and diffusion weighted MRI with physical activity sensors, ecological momentary assessment via PADs, and sleep. The literature further highlights specific brain regions frequently correlated with distinct MRI-PAD combinations. These combinations enable in-depth studies on how physiology, brain function and behavior influence each other. Our review highlights the potential for constructing brain-behavior models that extend beyond the scanner and into real-world contexts.
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Affiliation(s)
- Ana María Triana
- Department of Computer Science, School of ScienceAalto UniversityEspooFinland
| | - Jari Saramäki
- Department of Computer Science, School of ScienceAalto UniversityEspooFinland
| | - Enrico Glerean
- Department of Neuroscience and Biomedical Engineering, School of ScienceAalto UniversityEspooFinland
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Bofill J, Abisado M, Villaverde J, Sampedro GA. Exploring Digital Twin-Based Fault Monitoring: Challenges and Opportunities. Sensors (Basel) 2023; 23:7087. [PMID: 37631622 PMCID: PMC10458716 DOI: 10.3390/s23167087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 07/04/2023] [Accepted: 07/11/2023] [Indexed: 08/27/2023]
Abstract
High efficiency and safety are critical factors in ensuring the optimal performance and reliability of systems and equipment across various industries. Fault monitoring (FM) techniques play a pivotal role in this regard by continuously monitoring system performance and identifying the presence of faults or abnormalities. However, traditional FM methods face limitations in fully capturing the complex interactions within a system and providing real-time monitoring capabilities. To overcome these challenges, Digital Twin (DT) technology has emerged as a promising solution to enhance existing FM practices. By creating a virtual replica or digital copy of a physical equipment or system, DT offers the potential to revolutionize fault monitoring approaches. This paper aims to explore and discuss the diverse range of predictive methods utilized in DT and their implementations in FM across industries. Furthermore, it will showcase successful implementations of DT in FM across a wide array of industries, including manufacturing, energy, transportation, and healthcare. The utilization of DT in FM enables a comprehensive understanding of system behavior and performance by leveraging real-time data, advanced analytics, and machine learning algorithms. By integrating physical and virtual components, DT facilitates the monitoring and prediction of faults, providing valuable insights into the system's health and enabling proactive maintenance and decision making.
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Affiliation(s)
- Jherson Bofill
- Research and Development Center, Philippine Coding Camp, Manila 1004, Philippines;
| | - Mideth Abisado
- College of Computing and Information Technologies, National University, Manila 1008, Philippines;
| | - Jocelyn Villaverde
- School of Electrical, Electronics and Computer Engineering, Mapúa University, Manila 1002, Philippines;
| | - Gabriel Avelino Sampedro
- Faculty of Information and Communication Studies, University of the Philippines Open University, Laguna 4031, Philippines
- Center for Computational Imaging and Visual Innovations, De La Salle University, 2401 Taft Ave., Malate, Manila 1004, Philippines
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Han F, Xie X, Wang T, Cao C, Li J, Sun T, Liu H, Geng S, Wei Z, Li J, Xu F. Wearable Hydrogel-Based Epidermal Sensor with Thermal Compatibility and Long Term Stability for Smart Colorimetric Multi-Signals Monitoring. Adv Healthc Mater 2023; 12:e2201730. [PMID: 36259562 DOI: 10.1002/adhm.202201730] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 09/06/2022] [Indexed: 01/26/2023]
Abstract
Hydrogel-based wearable epidermal sensors (HWESs) have attracted widespread attention in health monitoring, especially considering their colorimetric readout capability. However, it remains challenging for HWESs to work at extreme temperatures with long term stability due to the existence of water. Herein, a wearable transparent epidermal sensor with thermal compatibility and long term stability for smart colorimetric multi-signals monitoring is developed, based on an anti-freezing and anti-drying hydrogel with high transparency (over 90% transmittance), high stretchability (up to 1500%) and desirable adhesiveness to various kinds of substrates. The hydrogel consists of polyacrylic acid, polyacrylamide, and tannic acid-coated cellulose nanocrystals in glycerin/water binary solvents. When glycerin readily forms strong hydrogen bonds with water, the hydrogel exhibits outstanding thermal compatibility. Furthermore, the hydrogel maintains excellent adhesion, stretchability, and transparency after long term storage (45 days) or at subzero temperatures (-20 °C). For smart colorimetric multi-signals monitoring, the freestanding smart colorimetric HWESs are utilized for simultaneously monitoring the pH, T and light, where colorimetric signals can be read and stored by artificial intelligence strategies in a real time manner. In summary, the developed wearable transparent epidermal sensor holds great potential for monitoring multi-signals with visible readouts in long term health monitoring.
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Affiliation(s)
- Fei Han
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P. R. China.,Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, 710049, P. R. China
| | - Xueyong Xie
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P. R. China.,Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, 710049, P. R. China
| | - Tiansong Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P. R. China.,Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, 710049, P. R. China
| | - Chaoyu Cao
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P. R. China.,Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, 710049, P. R. China
| | - Juju Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P. R. China.,Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, 710049, P. R. China
| | - Tianying Sun
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P. R. China.,Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, 710049, P. R. China
| | - Hao Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P. R. China.,Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, 710049, P. R. China
| | - Songmei Geng
- Department of Dermatology, The Second Affiliated Hospital, School of Medicine, Xi'an Jiaotong University, Xi'an, 710004, P. R. China
| | - Zhao Wei
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P. R. China.,Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, 710049, P. R. China
| | - Jing Li
- Department of Burns and Plastic Surgery, Second Affiliated Hospital of Air Force Military Medical University, Xi'an, 710038, P. R. China
| | - Feng Xu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P. R. China.,Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, 710049, P. R. China
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Heaney J, Buick J, Hadi MU, Soin N. Internet of Things-Based ECG and Vitals Healthcare Monitoring System. Micromachines (Basel) 2022; 13:2153. [PMID: 36557452 PMCID: PMC9780965 DOI: 10.3390/mi13122153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 11/29/2022] [Accepted: 11/30/2022] [Indexed: 06/17/2023]
Abstract
Health monitoring and its associated technologies have gained enormous importance over the past few years. The electrocardiogram (ECG) has long been a popular tool for assessing and diagnosing cardiovascular diseases (CVDs). Since the literature on ECG monitoring devices is growing at an exponential rate, it is becoming difficult for researchers and healthcare professionals to select, compare, and assess the systems that meet their demands while also meeting the monitoring standards. This emphasizes the necessity for a reliable reference to guide the design, categorization, and analysis of ECG monitoring systems, which will benefit both academics and practitioners. We present a complete ECG monitoring system in this work, describing the design stages and implementation of an end-to-end solution for capturing and displaying the patient's heart signals, heart rate, blood oxygen levels, and body temperature. The data will be presented on an OLED display, a developed Android application as well as in MATLAB via serial communication. The Internet of Things (IoT) approaches have a clear advantage in tackling the problem of heart disease patient care as they can transform the service mode into a widespread one and alert the healthcare services based on the patient's physical condition. Keeping this in mind, there is also the addition of a web server for monitoring the patient's status via WiFi. The prototype, which is compliant with the electrical safety regulations and medical equipment design, was further benchmarked against a commercially available off-the-shelf device, and showed an excellent accuracy of 99.56%.
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Sampedro GAR, Rachmawati SM, Kim DS, Lee JM. Exploring Machine Learning-Based Fault Monitoring for Polymer-Based Additive Manufacturing: Challenges and Opportunities. Sensors (Basel) 2022; 22:9446. [PMID: 36502146 PMCID: PMC9738791 DOI: 10.3390/s22239446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/25/2022] [Accepted: 12/01/2022] [Indexed: 06/17/2023]
Abstract
Three-dimensional printing, often known as additive manufacturing (AM), is a groundbreaking technique that enables rapid prototyping. Monitoring AM delivers benefits, as monitoring print quality can prevent waste and excess material costs. Machine learning is often applied to automating fault detection processes, especially in AM. This paper explores recent research on machine learning-based mechanical fault monitoring systems in fused deposition modeling (FDM). Specifically, various machine learning-based algorithms are applied to measurements extracted from different parts of a 3D printer to diagnose and identify faults. The studies often use mechanical-based fault analysis from data gathered from sensors that measure attitude, acoustic emission, acceleration, and vibration signals. This survey examines what has been achieved and opens up new opportunities for further research in underexplored areas such as SLM-based mechanical fault monitoring.
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Affiliation(s)
- Gabriel Avelino R. Sampedro
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea
- College of Computer Studies, De La Salle University, 2401 Taft Ave., Malate, Manila 1004, Philippines
- Faculty of Information and Communication Studies, University of the Philippines Open University, Los Baños 4031, Philippines
| | - Syifa Maliah Rachmawati
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea
| | - Dong-Seong Kim
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea
| | - Jae-Min Lee
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea
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Fedullo T, Morato A, Peserico G, Trevisan L, Tramarin F, Vitturi S, Rovati L. An IoT Measurement System Based on LoRaWAN for Additive Manufacturing. Sensors (Basel) 2022; 22:5466. [PMID: 35897970 PMCID: PMC9331730 DOI: 10.3390/s22155466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/14/2022] [Accepted: 07/18/2022] [Indexed: 06/15/2023]
Abstract
The Industrial Internet of Things (IIoT) paradigm represents a significant leap forward for sensor networks, potentially enabling wide-area and innovative measurement systems. In this scenario, smart sensors might be equipped with novel low-power and long range communication technologies to realize a so-called low-power wide-area network (LPWAN). One of the most popular representative cases is the LoRaWAN (Long Range WAN) network, where nodes are based on the widespread LoRa physical layer, generally optimized to minimize energy consumption, while guaranteeing long-range coverage and low-cost deployment. Additive manufacturing is a further pillar of the IIoT paradigm, and advanced measurement capabilities may be required to monitor significant parameters during the production of artifacts, as well as to evaluate environmental indicators in the deployment site. To this end, this study addresses some specific LoRa-based smart sensors embedded within artifacts during the early stage of the production phase, as well as their behavior once they have been deployed in the final location. An experimental evaluation was carried out considering two different LoRa end-nodes, namely, the Microchip RN2483 LoRa Mote and the Tinovi PM-IO-5-SM LoRaWAN IO Module. The final goal of this research was to assess the effectiveness of the LoRa-based sensor network design, both in terms of suitability for the aforementioned application and, specifically, in terms of energy consumption and long-range operation capabilities. Energy optimization, battery life prediction, and connectivity range evaluation are key aspects in this application context, since, once the sensors are embedded into artifacts, they will no longer be accessible.
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Affiliation(s)
- Tommaso Fedullo
- Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, Via P. Vivarelli 10, 41125 Modena, Italy; (T.F.); (L.R.)
- Department of Management and Engineering, University of Padova, S. S. Nicola 3, 36100 Vicenza, Italy
| | - Alberto Morato
- National Research Council of Italy, CNR–IEIIT, Via Gradenigo 6/B, 35131 Padova, Italy; (A.M.); (S.V.)
| | - Giovanni Peserico
- Department of Information Engineering, University of Padova, Via Gradenigo 6/B, 35100 Padova, Italy;
- Autec s.r.l., Via dei Pomari 65, 36030 Caldogno, Italy
| | - Luca Trevisan
- Consorzio RFX, Corso Stati Uniti 4, 35127 Padova, Italy;
| | - Federico Tramarin
- Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, Via P. Vivarelli 10, 41125 Modena, Italy; (T.F.); (L.R.)
| | - Stefano Vitturi
- National Research Council of Italy, CNR–IEIIT, Via Gradenigo 6/B, 35131 Padova, Italy; (A.M.); (S.V.)
| | - Luigi Rovati
- Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, Via P. Vivarelli 10, 41125 Modena, Italy; (T.F.); (L.R.)
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Cheng CY, Pourhejazy P, Hung CY, Yuangyai C. Smart Monitoring of Manufacturing Systems for Automated Decision-Making: A Multi-Method Framework. Sensors (Basel) 2021; 21:6860. [PMID: 34696072 DOI: 10.3390/s21206860] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 09/30/2021] [Accepted: 10/04/2021] [Indexed: 11/18/2022]
Abstract
Smart monitoring plays a principal role in the intelligent automation of manufacturing systems. Advanced data collection technologies, like sensors, have been widely used to facilitate real-time data collection. Computationally efficient analysis of the operating systems, however, remains relatively underdeveloped and requires more attention. Inspired by the capabilities of signal analysis and information visualization, this study proposes a multi-method framework for the smart monitoring of manufacturing systems and intelligent decision-making. The proposed framework uses the machine signals collected by noninvasive sensors for processing. For this purpose, the signals are filtered and classified to facilitate the realization of the operational status and performance measures to advise the appropriate course of managerial actions considering the detected anomalies. Numerical experiments based on real data are used to show the practicability of the developed monitoring framework. Results are supportive of the accuracy of the method. Applications of the developed approach are worthwhile research topics to research in other manufacturing environments.
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Serhani MA, T. El Kassabi H, Ismail H, Nujum Navaz A. ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges. Sensors (Basel) 2020; 20:E1796. [PMID: 32213969 PMCID: PMC7147367 DOI: 10.3390/s20061796] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 03/17/2020] [Accepted: 03/19/2020] [Indexed: 02/01/2023]
Abstract
Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems' components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems' value chain is conducted, and a thorough review of the relevant literature, classified against the experts' taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.
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Affiliation(s)
- Mohamed Adel Serhani
- Department of Information Systems and Security, College of Information Technology, UAE University, Al Ain 15551, United Arab Emirates;
| | - Hadeel T. El Kassabi
- Department of Computer Science and Software Engineering, College of Information Technology, UAE University, Al Ain 15551, United Arab Emirates; (H.T.E.K.)
| | - Heba Ismail
- Department of Computer Science and Software Engineering, College of Information Technology, UAE University, Al Ain 15551, United Arab Emirates; (H.T.E.K.)
| | - Alramzana Nujum Navaz
- Department of Information Systems and Security, College of Information Technology, UAE University, Al Ain 15551, United Arab Emirates;
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Freddi A, Longhi S, Monteriù A. Special Issue on "Smart Homes": Editors' Notes. Sensors (Basel) 2019; 19:E836. [PMID: 30781649 DOI: 10.3390/s19040836] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 02/13/2019] [Indexed: 11/17/2022]
Abstract
In this editorial, we provide an overview of the content of the Special Issue on “Smart Homes”. The aim of this Special Issue is to provide a comprehensive collection of some of the current state-of-the-art technologies in the context of smart homes, together with new advanced theoretical and technological solutions that enable smart technology diffusion into homes.
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Xiao C, Chen N, Li D, Lv Y, Gong J. SCRMS: An RFID and Sensor Web-Enabled Smart Cultural Relics Management System. Sensors (Basel) 2016; 17:E60. [PMID: 28042820 DOI: 10.3390/s17010060] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Revised: 12/07/2016] [Accepted: 12/26/2016] [Indexed: 11/29/2022]
Abstract
Cultural relics represent national or even global resources of inestimable value. How to efficiently manage and preserve these cultural relics is a vitally important issue. To achieve this goal, this study proposed, designed, and implemented an RFID and Sensor Web–enabled smart cultural relics management system (SCRMS). In this system, active photovoltaic subtle energy-powered Radio Frequency Identification (RFID) is used for long-range contactless identification and lifecycle management of cultural relics during their storage and circulation. In addition, different types of ambient sensors are integrated with the RFID tags and deployed around cultural relics to monitor their environmental parameters, helping to ensure that they remain in good condition. An Android-based smart mobile application, as middleware, is used in collaboration with RFID readers to collect information and provide convenient management for the circulation of cultural relics. Moreover, multiple sensing techniques are taken advantage of simultaneously for preservation of cultural relics. The proposed system was successfully applied to a museum in the Yongding District, Fujian Province, China, demonstrating its feasibility and advantages for smart and efficient management and preservation of cultural relics.
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