Mobile Multi-parametric Sensor System for Diagnosis of Epilepsy and Brain Related Disorders

Αναστασοπούλου, Παναγιώτα; Antonopoulos, Christos; Shgir, Hatem; Krikis, George; Voros, Nikolaos S.; Hey, Stefan

doi:10.1007/978-3-642-37893-5_24

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Shu et al [24] presented a comprehensive review on physiological signalbased emotion recognition and conduct a summary and comparison of the recent studies. In recent years, the application on disease diagnosis based on multi-sensor fusion has also been emerging, such as epilepsy diagnosis [27], atrial hypertrophy disease diagnosis [28], and children's chronic illness monitoring [29]. In addition, there are rehabilitation applications including intelligent body posture analysis model [30], rehabilitation exoskeleton [31], end-of-arm force display system [32] and so on.…”

Section: A Bsns Applications Based On Information Fusionmentioning

confidence: 99%

An Adaptive Multi-Homogeneous Sensor Weight Calculation Method for Body Sensor Networks

et al. 2019

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In the Body Sensor Networks (BSNs), a comprehensive and intuitive conclusion can be obtained by analyzing the physiological state in the mode of multi-heterogeneous source fusion. However, if a node in the sensing layer fails, the input-oriented analysis system is no longer reliable. In this paper, we propose a homogenous sensor weight calculation method for a single sensing node in the BSNs. Under the premise of valid sensing, a set of sequences will be randomly selected as a referee to make the rules of weight allocation. Weak competitiveness means that sensors have a high degree of antagonism, which indicates that the measuring results have errors or there may be abnormal sensors in the sensing module. Meanwhile, we introduce the trend factor of human body state to strengthen the connection between adjacent data in time series, so as to further improve the reliability of trust weight. The proposed algorithm is helpful for sensing nodes in BSNs to obtain more reliable original physiological data and ensure the accuracy of subsequent feature analysis and system decision. Specifically, it can (1) improve the robustness of sensing in BSNs applications, (2) suppress the interference of abnormal signals, (3) solve data conflict, and (4) be universal to the sensors in BSNs devices. The ECG simulations verify the validity of the algorithm in all extreme cases of valid sensing, and the PPG experiments show that the proposed algorithm is applicable to different types of biosensors. Finally, the evaluation and comparison with other four known data-level fusion methods show that the proposed algorithm has better fusion performance. INDEX TERMS Body sensor networks, physiological parameter, information conflict, information fusion.

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Section: A Bsns Applications Based On Information Fusionmentioning

confidence: 99%

An Adaptive Multi-Homogeneous Sensor Weight Calculation Method for Body Sensor Networks

et al. 2019

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“…Compared with a battlefield situation, we are more concerned with the physiology of a soldier, which is a neglected part of current military smart devices. Previous research on wearable technology mainly focuses on disease diagnosis [13,14], physical rehabilitation [15], and action recognition [16] in non-military fields. However, these techniques are not suitable for highly trained soldiers on the battlefield.…”

Section: Introductionmentioning

confidence: 99%

Systematic Analysis of a Military Wearable Device Based on a Multi-Level Fusion Framework: Research Directions

Shi

Zhang

Liu

et al. 2019

Sensors

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With the development of the Internet of Battlefield Things (IoBT), soldiers have become key nodes of information collection and resource control on the battlefield. It has become a trend to develop wearable devices with diverse functions for the military. However, although densely deployed wearable sensors provide a platform for comprehensively monitoring the status of soldiers, wearable technology based on multi-source fusion lacks a generalized research system to highlight the advantages of heterogeneous sensor networks and information fusion. Therefore, this paper proposes a multi-level fusion framework (MLFF) based on Body Sensor Networks (BSNs) of soldiers, and describes a model of the deployment of heterogeneous sensor networks. The proposed framework covers multiple types of information at a single node, including behaviors, physiology, emotions, fatigue, environments, and locations, so as to enable Soldier-BSNs to obtain sufficient evidence, decision-making ability, and information resilience under resource constraints. In addition, we systematically discuss the problems and solutions of each unit according to the frame structure to identify research directions for the development of wearable devices for the military.

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Mobile Sensors for Multiparametric Monitoring in Epileptic Patients

Hey

Αναστασοπούλου

Bideaux

et al. 2015

Cyberphysical Systems for Epilepsy and Related Brain Disorders

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Mobile Multi-parametric Sensor System for Diagnosis of Epilepsy and Brain Related Disorders

Cited by 4 publications

References 7 publications

An Adaptive Multi-Homogeneous Sensor Weight Calculation Method for Body Sensor Networks

An Adaptive Multi-Homogeneous Sensor Weight Calculation Method for Body Sensor Networks

Systematic Analysis of a Military Wearable Device Based on a Multi-Level Fusion Framework: Research Directions

Mobile Sensors for Multiparametric Monitoring in Epileptic Patients

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