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Bahache M, Tahari AEK, Herrera-Tapia J, Lagraa N, Calafate CT, Kerrache CA. Towards an Accurate Faults Detection Approach in Internet of Medical Things Using Advanced Machine Learning Techniques. Sensors (Basel) 2022; 22:5893. [PMID: 35957453 PMCID: PMC9371421 DOI: 10.3390/s22155893] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [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: 07/20/2022] [Revised: 08/04/2022] [Accepted: 08/04/2022] [Indexed: 06/15/2023]
Abstract
Remotely monitoring people's healthcare is still among the most important research topics for researchers from both industry and academia. In addition, with the Wireless Body Networks (WBANs) emergence, it becomes possible to supervise patients through an implanted set of body sensors that can communicate through wireless interfaces. These body sensors are characterized by their tiny sizes, and limited resources (power, computing, and communication capabilities), which makes these devices prone to have faults and sensible to be damaged. Thus, it is necessary to establish an efficient system to detect any fault or anomalies when receiving sensed data. In this paper, we propose a novel, optimized, and hybrid solution between machine learning and statistical techniques, for detecting faults in WBANs that do not affect the devices' resources and functionality. Experimental results illustrate that our approach can detect unwanted measurement faults with a high detection accuracy ratio that exceeds the 99.62%, and a low mean absolute error of 0.61%, clearly outperforming the existing state-of-art solutions.
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Affiliation(s)
- Mohamed Bahache
- Laboratoire d’Informatique et de Mathématiques, Université Amar Telidji, Laghouat 03000, Algeria
| | - Abdou El Karim Tahari
- Laboratoire d’Informatique et de Mathématiques, Université Amar Telidji, Laghouat 03000, Algeria
| | - Jorge Herrera-Tapia
- Facultad de Ciencias Informáticas, Universidad Laica Eloy Alfaro de Manabí, Manta 130214, Ecuador
| | - Nasreddine Lagraa
- Laboratoire d’Informatique et de Mathématiques, Université Amar Telidji, Laghouat 03000, Algeria
| | - Carlos Tavares Calafate
- Computer Engineering Department (DISCA), Universitat Politècnica de València, 46022 Valencia, Spain
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Bellisle R, Bjune C, Newman D. Considerations for Wearable Sensors to Monitor Physical Performance During Spaceflight Intravehicular Activities. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2020:4160-4164. [PMID: 33018914 DOI: 10.1109/embc44109.2020.9175674] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Wearable sensors provide the capability to noninvasively monitor physiological parameters during spaceflight, including those related to physical performance and daily activity. Regular monitoring of general health and exercise capabilities in astronauts can ensure adequate performance levels and record health changes caused by the space environment. Relevant measurables include vital signs, cardiovascular health, and activity monitoring. Wearable sensor devices can be comfortable for long-term use and easy to operate, which is particularly important during more autonomous future planetary missions. Many devices are currently being developed and tested, but few wearable devices or integrated "smart" garments have been assigned for regular use on the International Space Station. The unique needs of the space environment must be considered to facilitate the development and implementation of wearable devices, particularly "smart" sensor garments, for space applications.
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Erickson K, McMahon M, Dunne LE, Larsen C, Olmstead B, Hipp J. Design and Analysis of a Sensor-Enabled In-Ear Device for Physiological Monitoring1. J Med Device 2016. [DOI: 10.1115/1.4033200] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Affiliation(s)
- Kira Erickson
- Department of Design, Housing, and Apparel, University of Minnesota, St. Paul, MN 55108
| | - Molly McMahon
- Department of Design, Housing, and Apparel, University of Minnesota, St. Paul, MN 55108
| | - Lucy E. Dunne
- Department of Design, Housing, and Apparel, University of Minnesota, St. Paul, MN 55108
| | | | | | - Jeremy Hipp
- Honeywell International, Inc., Minneapolis, MN 55422
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Salem O, Liu Y, Mehaoua A. Detection of Faulty Measurements in WBANs using Gaussian Mixture Model and Ant Colony. International Journal of E-Health and Medical Communications 2014. [DOI: 10.4018/ijehmc.2014100102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Wireless sensor networks are subject to different types of faults and interferences after their deployment. Abnormal values reported by sensors should be separated from faulty or injected measurements to ensure reliable monitoring operation. The aim of this paper is to propose a lightweight approach for the detection and suppression of faulty measurements in medical wireless sensor networks. The proposed approach is based on the combination of statistical model and machine learning algorithm. The authors begin by collecting physiological data and then they cluster the data collected during the first few minutes using the Gaussian mixture decomposition. They use the resulted labeled data as the input for the Ant Colony algorithm to derive classification rules in the central base station. Afterward, the derived rules are transmitted and installed in each associated sensor to detect abnormal values in distributed manner, and notify anomalies to the base station. Finally, the authors exploit the spatial and temporal correlations between monitored attributes to differentiate between faulty sensor readings and clinical emergency. They evaluate their approach with real and synthetic patient datasets. The experimental results demonstrate that their proposed approach achieves a high rate of detection accuracy for clinical emergency with reduced false alarm rate when compared to robust Mahalanobis distance.
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Affiliation(s)
- Osman Salem
- LIPADE Laboratory, University of Paris Descartes, Paris, France
| | | | - Ahmed Mehaoua
- LIPADE Laboratory, University of Paris Descartes, Paris, France
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Salem O, Liu Y, Mehaoua A, Boutaba R. Online Anomaly Detection in Wireless Body Area Networks for Reliable Healthcare Monitoring. IEEE J Biomed Health Inform 2014; 18:1541-51. [DOI: 10.1109/jbhi.2014.2312214] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Salem O, Guerassimov A, Mehaoua A, Marcus A, Furht B. Anomaly Detection in Medical Wireless Sensor Networks using SVM and Linear Regression Models. International Journal of E-Health and Medical Communications 2014. [DOI: 10.4018/ijehmc.2014010102] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This paper details the architecture and describes the preliminary experimentation with the proposed framework for anomaly detection in medical wireless body area networks for ubiquitous patient and healthcare monitoring. The architecture integrates novel data mining and machine learning algorithms with modern sensor fusion techniques. Knowing wireless sensor networks are prone to failures resulting from their limitations (i.e. limited energy resources and computational power), using this framework, the authors can distinguish between irregular variations in the physiological parameters of the monitored patient and faulty sensor data, to ensure reliable operations and real time global monitoring from smart devices. Sensor nodes are used to measure characteristics of the patient and the sensed data is stored on the local processing unit. Authorized users may access this patient data remotely as long as they maintain connectivity with their application enabled smart device. Anomalous or faulty measurement data resulting from damaged sensor nodes or caused by malicious external parties may lead to misdiagnosis or even death for patients. The authors' application uses a Support Vector Machine to classify abnormal instances in the incoming sensor data. If found, the authors apply a periodically rebuilt, regressive prediction model to the abnormal instance and determine if the patient is entering a critical state or if a sensor is reporting faulty readings. Using real patient data in our experiments, the results validate the robustness of our proposed framework. The authors further discuss the experimental analysis with the proposed approach which shows that it is quickly able to identify sensor anomalies and compared with several other algorithms, it maintains a higher true positive and lower false negative rate.
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Affiliation(s)
- Osman Salem
- LIPADE Laboratory, University of Paris Descartes, Paris, France
| | | | - Ahmed Mehaoua
- LIPADE Laboratory, University of Paris Descartes, France, Centre National de la Recherche Scientifique (CNRS), LaBRI, Bordeaux, France
| | - Anthony Marcus
- Department of Computer and Electrical, Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA
| | - Borko Furht
- Department of Computer and Electrical, Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA
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Abstract
OBJECTIVE In this article, we describe the important aspects like major characteristics, research issues, and challenges with body area sensor networks in telemedicine systems for patient monitoring in different scenarios. Present and emerging developments in communications integrated with the developments in microelectronics and embedded system technologies will have a dramatic impact on future patient monitoring and health information delivery systems. The important challenges are bandwidth limitations, power consumption, and skin or tissue protection. MATERIALS AND METHODS This article presents a detailed survey on wireless body area networks (WBANs). RESULTS AND CONCLUSIONS We have designed the framework for integrating body area networks on telemedicine systems. Recent trends, overall WBAN-telemedicine framework, and future research scope have also been addressed in this article.
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Affiliation(s)
- Chinmay Chakraborty
- Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, India.
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Kumar P, Lee HJ. Security issues in healthcare applications using wireless medical sensor networks: a survey. Sensors (Basel) 2011; 12:55-91. [PMID: 22368458 PMCID: PMC3279202 DOI: 10.3390/s120100055] [Citation(s) in RCA: 237] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2011] [Revised: 12/07/2011] [Accepted: 12/15/2011] [Indexed: 11/16/2022]
Abstract
Healthcare applications are considered as promising fields for wireless sensor networks, where patients can be monitored using wireless medical sensor networks (WMSNs). Current WMSN healthcare research trends focus on patient reliable communication, patient mobility, and energy-efficient routing, as a few examples. However, deploying new technologies in healthcare applications without considering security makes patient privacy vulnerable. Moreover, the physiological data of an individual are highly sensitive. Therefore, security is a paramount requirement of healthcare applications, especially in the case of patient privacy, if the patient has an embarrassing disease. This paper discusses the security and privacy issues in healthcare application using WMSNs. We highlight some popular healthcare projects using wireless medical sensor networks, and discuss their security. Our aim is to instigate discussion on these critical issues since the success of healthcare application depends directly on patient security and privacy, for ethic as well as legal reasons. In addition, we discuss the issues with existing security mechanisms, and sketch out the important security requirements for such applications. In addition, the paper reviews existing schemes that have been recently proposed to provide security solutions in wireless healthcare scenarios. Finally, the paper ends up with a summary of open security research issues that need to be explored for future healthcare applications using WMSNs.
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Affiliation(s)
- Pardeep Kumar
- Department of Ubiquitous-IT, Graduate School of Design & IT, Dongseo University, San 69-1, Jurye-2-Dong, Sasang-Gu, Busan 617-716, Korea; E-Mail:
| | - Hoon-Jae Lee
- Department of Ubiquitous-IT, Graduate School of Design & IT, Dongseo University, San 69-1, Jurye-2-Dong, Sasang-Gu, Busan 617-716, Korea; E-Mail:
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Linder SP, Wendelken SM, Wei E, McGrath SP. Using the morphology of photoplethysmogram peaks to detect changes in posture. J Clin Monit Comput 2006; 20:151-8. [PMID: 16688391 DOI: 10.1007/s10877-006-9015-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2005] [Accepted: 02/21/2006] [Indexed: 10/24/2022]
Abstract
The morphology of the pulsatile component of the photoplethysmogram (PPG) has been shown to vary with physiology, but changes in the morphology caused by the baroreflex response to orthostatic stress have not been investigated. Using two FDA approved Nonin pulse oximeters placed on the finger and ear, we monitored 11 subjects, for three trials each, as they stood from a supine position. Each cardiac cycle was automatically extracted from the PPG waveform and characterized using statistics corresponding to normalized peak width, instantaneous heart rate, and amplitude of the pulsatile component of the ear PPG. A nonparametric Wilcoxon rank sum test was then used to detect in real-time changes in these features with p < 0.01. In all 33 trials, the standing event was detected as an abrupt change in at least two of these features, with only one false alarm. In 26 trials, an abrupt change was detected in all three features, with no false alarms. An increase in the normalize peak width was detected before an increase in heart rate, and in 21 trials a peak in the feature was detected before or as standing commenced. During standing, the pulse rate always increases, and then amplitude of the ear PPG constricts by a factor of two or more. We hypothesis that the baroreflex first reduces the percentage of time blood flow is stagnant during the cardiac cycle, then increases the hear rate, and finally vasoconstricts the peripheral tissue in order to reestablishing a nominal blood pressure. These three features therefore can be used as a detector of the baroreflex response to changes in posture or other forms of blood volume sequestration.
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Affiliation(s)
- Stephen P Linder
- Department of Computer Science, Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA.
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