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Body Sensors Networks for E-Health Applications

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

Deadline for manuscript submissions: closed (30 October 2019) | Viewed by 57478

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Special Issue Editors

Dr. David Naranjo-Hernández

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Guest Editor

Biomedical Engineering Group, University of Seville, 41092 Sevilla, Spain
Interests: biomedical smart sensors; wireless body sensor networks; bioelectromagnetics; intrabody communications; bioimpedance; accelerometry; capacitive sensing; fall detection; non-invasive glucose sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Laura M. Roa

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Guest Editor

Biomedical Engineering Group, University of Seville, 41092 Sevilla, Spain
Interests: multiscale computational modeling for multimodal diagnosis; architectures for the integration of social/health services; intelligent devices for ambient assisted living; bioelectromagnetics
Special Issues, Collections and Topics in MDPI journals

Dr. Javier Reina-Tosina

E-Mail Website
Guest Editor

Biomedical Engineering Group, University of Seville, 41092 Sevilla, Spain
Interests: bioelectromagnetics; intelligent devices for ambient assisted living; multiscale computational modeling for multimodal diagnosis; architectures for the integration of social/health services
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The monitoring and analysis of physiological variables through biomedical sensors is fundamental for the diagnosis and monitoring of users and/or patients in the context of e-Health. A biomedical sensor is usually located on the patient to record and analyze physiological signals such as the electrocardiogram, oxygen saturation, blood pressure, body temperature, respiratory rate, heart rate or blood glucose concentration, which can be performed on a 24/7 continuous monitoring scheme (24 hours a day, 7 days a week). The biomedical sensors are connected wirelessly to each other or to an external link device, forming a body sensors network (BSN). BSNs enable real-time, ubiquitous, pervasive and non-obstructive monitoring of the patient's health status and the detection of emergency situations. They provide a reduction in the cost of medical care, since monitoring is done outside the clinical setting, and results in an improvement of the quality of diagnosis and medical follow-up, making possible the early diagnosis of a possible disease and the early management of patients outside the hospital.

These sensors are usually small and lightweight, portable (placed on the skin or in a garment), but also implantable, to allow non-intrusive monitoring, performed in a transparent way, so that the user can obtain an actual measurement of the measured physiological variable, without it being affected by the measurement process itself, and avoiding any type of discomfort to the user.

Despite the advances in BSN, there are still many challenges to be addressed, such as the miniaturization of sensor devices for transparent monitoring, usability and scalability, energy efficiency and energy harvesting to provide greater autonomy, the standardization of low-power wireless communication, design and characterization issues related to antennas in a body environment or the integration of implantable devices.

This Special Issue intends to publish high-quality research papers as well as review articles that would address recent advances and challenges in BSNs for e-Health applications.

Potential topics include, but are not limited to:

Novel and enhanced sensors of physiological variables
Innovative health-sensing technologies and applications
Implantable and minimally invasive devices and nanosensors
BSN hardware platforms for e-Health applications
Artifact correction and enhanced monitoring using information fusion
New processing algorithms and machine learning in BSNs
Low-power wireless communication technologies for BSNs
Design and characterization of antennas in BSNs
Energy efficiency and energy harvesting for body sensor devices
Big data challenges and Internet of Things in BSNs
Future challenges of BSNs in e-Health applications

Dr. David Naranjo-Hernández
Dr. Laura M. Roa
Dr. Javier Reina-Tosina
Guest Editors

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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Published Papers (10 papers)

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Editorial

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7 pages, 180 KiB

Open AccessEditorial

Special Issue “Body Sensors Networks for E-Health Applications”

by David Naranjo-Hernández, Javier Reina-Tosina and Laura M. Roa

Sensors 2020, 20(14), 3944; https://doi.org/10.3390/s20143944 - 16 Jul 2020

Cited by 12 | Viewed by 2593

Abstract

Body Sensor Networks (BSN) have emerged as a particularization of Wireless Sensor Networks (WSN) in the context of body monitoring environments, closely linked to healthcare applications. These networks are made up of smart biomedical sensors that allow the monitoring of physiological parameters and serve as the basis for e-Health applications. This Special Issue collects some of the latest developments in the field of BSN related to new developments in biomedical sensor technologies, the design and experimental characterization of on-body/in-body antennas and new communication protocols for BSN, including some review studies. Full article

(This article belongs to the Special Issue Body Sensors Networks for E-Health Applications)

Research

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7 pages, 1640 KiB

Open AccessArticle

Camera-Integrable Wide-Bandwidth Antenna for Capsule Endoscope

by Jong Jin Baek, Se Woong Kim and Youn Tae Kim

Sensors 2020, 20(1), 232; https://doi.org/10.3390/s20010232 - 31 Dec 2019

Cited by 11 | Viewed by 3341

Abstract

This paper presents a new antenna design for a capsule endoscope. The proposed antenna comprises a camera hole and meandered line. These features enable the antenna to be integrated on the same side as the camera, within the capsule endoscope. Moreover, light-emitting diodes can be mounted on the surface of the antenna for illumination. The antenna achieves a wide bandwidth, despite the small size owing to its meandered line structure. Full article

(This article belongs to the Special Issue Body Sensors Networks for E-Health Applications)

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17 pages, 10641 KiB

Open AccessArticle

Lessons Learned about the Design and Active Characterization of On-Body Antennas in the 2.4 GHz Frequency Band

by David Naranjo-Hernández, Javier Reina-Tosina and Laura M. Roa

Sensors 2020, 20(1), 224; https://doi.org/10.3390/s20010224 - 31 Dec 2019

Cited by 4 | Viewed by 3325

Abstract

This work addresses the design and experimental characterization of on-body antennas, which play an essential role within Body Sensor Networks. Four antenna designs were selected from a set of eighteen antenna choices and finally implemented for both passive and active measurements. The issues raised during the process of this work (requirements study, technology selection, development and optimization of antennas, impedance matching, unbalanced to balanced transformation, passive and active characterization, off-body and on-body configurations, etc.) were studied and solved, driving a methodology for the characterization of on-body antennas, including transceiver effects. Despite the influence of the body, the antennas showed appropriate results for an in-door environment. Another novelty is the proposal and validation of a phantom to emulate human experimentation. The differences between experimental and simulated results highlight a set of circumstances to be taken into account during the design process of an on-body antenna: more comprehensive simulation schemes to take into account the hardware effects and a custom design process that considers the application for which the device will be used, as well as the effects that can be caused by the human body. Full article

(This article belongs to the Special Issue Body Sensors Networks for E-Health Applications)

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27 pages, 7444 KiB

Open AccessArticle

Smart Bioimpedance Spectroscopy Device for Body Composition Estimation

by David Naranjo-Hernández, Javier Reina-Tosina, Laura M. Roa, Gerardo Barbarov-Rostán, Nuria Aresté-Fosalba, Alfonso Lara-Ruiz, Pilar Cejudo-Ramos and Francisco Ortega-Ruiz

Sensors 2020, 20(1), 70; https://doi.org/10.3390/s20010070 - 21 Dec 2019

Cited by 27 | Viewed by 6281

Abstract

The purpose of this work is to describe a first approach to a smart bioimpedance spectroscopy device for its application to the estimation of body composition. The proposed device is capable of carrying out bioimpedance measurements in multiple configurable frequencies, processing the data to obtain the modulus and the bioimpedance phase in each of the frequencies, and transmitting the processed information wirelessly. Another novelty of this work is a new algorithm for the identification of Cole model parameters, which is the basis of body composition estimation through bioimpedance spectroscopy analysis. Against other proposals, the main advantages of the proposed method are its robustness against parasitic effects by employing an extended version of Cole model with phase delay and three dispersions, its simplicity and low computational load. The results obtained in a validation study with respiratory patients show the accuracy and feasibility of the proposed technology for bioimpedance measurements. The precision and validity of the algorithm was also proven in a validation study with peritoneal dialysis patients. The proposed method was the most accurate compared with other existing algorithms. Moreover, in those cases affected by parasitic effects the proposed algorithm provided better approximations to the bioimpedance values than a reference device. Full article

(This article belongs to the Special Issue Body Sensors Networks for E-Health Applications)

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23 pages, 4236 KiB

Open AccessArticle

An Energy Balance Clustering Routing Protocol for Intra-Body Wireless Nanosensor Networks

by Juan Xu, Yan Zhang, Jiaolong Jiang and Jiali Kan

Sensors 2019, 19(22), 4875; https://doi.org/10.3390/s19224875 - 8 Nov 2019

Cited by 21 | Viewed by 5485

Abstract

Wireless NanoSensor Networks (WNSNs) are a new type of network that combines nanotechnology and sensor networks. Because WNSNs have great application prospects in intra-body health monitoring, biomedicine and damage detection, intra-body Wireless NanoSensor Networks (iWNSNs) have become a new research hotspot. An energy balance clustering routing protocol (EBCR) is proposed for the intra-body nanosensor nodes with low computing and processing capabilities, short communication range and limited energy storage. The protocol reduces the communication load of nano-nodes by adopting a new hierarchical clustering method. The nano-nodes in the cluster can transmit data directly to the cluster head nodes by one-hop, and the cluster head nodes can transmit data to the nano control node by multi-hop routing among themselves. Furthermore, there is a tradeoff between distance and channel capacity when choosing the next hop node in order to reduce energy consumption while ensuring successful data packet transmission. The simulation results show that the protocol has great advantages in balancing energy consumption, prolonging network lifetime and ensuring data packet transmission success rate. It can be seen that EBCR protocol can be used as an effective routing scheme for iWNSNs. Full article

(This article belongs to the Special Issue Body Sensors Networks for E-Health Applications)

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11 pages, 1591 KiB

Open AccessArticle

Deep Learning-Based LOS and NLOS Identification in Wireless Body Area Networks

by Krzysztof K. Cwalina, Piotr Rajchowski, Olga Blaszkiewicz, Alicja Olejniczak and Jaroslaw Sadowski

Sensors 2019, 19(19), 4229; https://doi.org/10.3390/s19194229 - 29 Sep 2019

Cited by 28 | Viewed by 3676

Abstract

In this article, the usage of deep learning (DL) in ultra-wideband (UWB) Wireless Body Area Networks (WBANs) is presented. The developed approach, using channel impulse response, allows higher efficiency in identifying the direct visibility conditions between nodes in off-body communication with comparison to the methods described in the literature. The effectiveness of the proposed deep feedforward neural network was checked on the basis of the measurement data for dynamic scenarios in an indoor environment. The obtained results clearly prove the validity of the proposed DL approach in the UWB WBANs and high (over 98.6% for most cases) efficiency for LOS and NLOS conditions classification. Full article

(This article belongs to the Special Issue Body Sensors Networks for E-Health Applications)

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28 pages, 8306 KiB

Open AccessArticle

An Advanced First Aid System Based on an Unmanned Aerial Vehicles and a Wireless Body Area Sensor Network for Elderly Persons in Outdoor Environments

by Saif Saad Fakhrulddin, Sadik Kamel Gharghan, Ali Al-Naji and Javaan Chahl

Sensors 2019, 19(13), 2955; https://doi.org/10.3390/s19132955 - 4 Jul 2019

Cited by 33 | Viewed by 12690

Abstract

For elderly persons, a fall can cause serious injuries such as a hip fracture or head injury. Here, an advanced first aid system is proposed for monitoring elderly patients with heart conditions that puts them at risk of falling and for providing first aid supplies using an unmanned aerial vehicle. A hybridized fall detection algorithm (FDB-HRT) is proposed based on a combination of acceleration and a heart rate threshold. Five volunteers were invited to evaluate the performance of the heartbeat sensor relative to a benchmark device, and the extracted data was validated using statistical analysis. In addition, the accuracy of fall detections and the recorded locations of fall incidents were validated. The proposed FDB-HRT algorithm was 99.16% and 99.2% accurate with regard to heart rate measurement and fall detection, respectively. In addition, the geolocation error of patient fall incidents based on a GPS module was evaluated by mean absolute error analysis for 17 different locations in three cities in Iraq. Mean absolute error was 1.08 × 10⁻⁵° and 2.01 × 10⁻⁵° for latitude and longitude data relative to data from the GPS Benchmark system. In addition, the results revealed that in urban areas, the UAV succeeded in all missions and arrived at the patient’s locations before the ambulance, with an average time savings of 105 s. Moreover, a time saving of 31.81% was achieved when using the UAV to transport a first aid kit to the patient compared to an ambulance. As a result, we can conclude that when compared to delivering first aid via ambulance, our design greatly reduces delivery time. The proposed advanced first aid system outperformed previous systems presented in the literature in terms of accuracy of heart rate measurement, fall detection, and information messages and UAV arrival time. Full article

(This article belongs to the Special Issue Body Sensors Networks for E-Health Applications)

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14 pages, 903 KiB

Open AccessArticle

Design and Accuracy of an Instrumented Insole Using Pressure Sensors for Step Count

by Armelle M. Ngueleu, Andréanne K. Blanchette, Laurent Bouyer, Désirée Maltais, Bradford J. McFadyen, Hélène Moffet and Charles S. Batcho

Sensors 2019, 19(5), 984; https://doi.org/10.3390/s19050984 - 26 Feb 2019

Cited by 38 | Viewed by 5736

Abstract

Despite the accessibility of several step count measurement systems, count accuracy in real environments remains a major challenge. Microelectromechanical systems and pressure sensors seem to present a potential solution for step count accuracy. The purpose of this study was to equip an insole with pressure sensors and to test a novel and potentially more accurate method of detecting steps. Methods: Five force-sensitive resistors (FSR) were integrated under the heel, the first, third, and fifth metatarsal heads and the great toe. This system was tested with twelve healthy participants at self-selected and maximal walking speeds in indoor and outdoor settings. Step counts were computed based on previously reported calculation methods, individual and averaged FSR-signals, and a new method: cumulative sum of all FSR-signals. These data were compared to a direct visual step count for accuracy analysis. Results: This system accurately detected steps with success rates ranging from 95.5 ± 3.5% to 98.5 ± 2.1% (indoor) and from 96.5 ± 3.9% to 98.0 ± 2.3% (outdoor) for self-selected walking speeds and from 98.1 ± 2.7% to 99.0 ± 0.7% (indoor) and 97.0 ± 6.2% to 99.4 ± 0.7% (outdoor) for maximal walking speeds. Cumulative sum of pressure signals during the stance phase showed high step detection accuracy (99.5 ± 0.7%–99.6 ± 0.4%) and appeared to be a valid method of step counting. Conclusions: The accuracy of step counts varied according to the calculation methods, with cumulative sum-based method being highly accurate. Full article

(This article belongs to the Special Issue Body Sensors Networks for E-Health Applications)

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Review

Jump to: Editorial, Research

42 pages, 1938 KiB

Open AccessReview

Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review

by David Naranjo-Hernández, Javier Reina-Tosina and Laura M. Roa

Sensors 2020, 20(2), 365; https://doi.org/10.3390/s20020365 - 8 Jan 2020

Cited by 37 | Viewed by 8300

Abstract

Non-oncologic chronic pain is a common high-morbidity impairment worldwide and acknowledged as a condition with significant incidence on quality of life. Pain intensity is largely perceived as a subjective experience, what makes challenging its objective measurement. However, the physiological traces of pain make possible its correlation with vital signs, such as heart rate variability, skin conductance, electromyogram, etc., or health performance metrics derived from daily activity monitoring or facial expressions, which can be acquired with diverse sensor technologies and multisensory approaches. As the assessment and management of pain are essential issues for a wide range of clinical disorders and treatments, this paper reviews different sensor-based approaches applied to the objective evaluation of non-oncological chronic pain. The space of available technologies and resources aimed at pain assessment represent a diversified set of alternatives that can be exploited to address the multidimensional nature of pain. Full article

(This article belongs to the Special Issue Body Sensors Networks for E-Health Applications)

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22 pages, 3931 KiB

Open AccessReview

Technologies for Monitoring Lifestyle Habits Related to Brain Health: A Systematic Review

by Diego Moreno-Blanco, Javier Solana-Sánchez, Patricia Sánchez-González, Ignacio Oropesa, César Cáceres, Gabriele Cattaneo, Josep M. Tormos-Muñoz, David Bartrés-Faz, Álvaro Pascual-Leone and Enrique J. Gómez

Sensors 2019, 19(19), 4183; https://doi.org/10.3390/s19194183 - 26 Sep 2019

Cited by 9 | Viewed by 5020

Abstract

Brain health refers to the preservation of brain integrity and function optimized for an individual’s biological age. Several studies have demonstrated that our lifestyles habits impact our brain health and our cognitive and mental wellbeing. Monitoring such lifestyles is thus critical and mobile technologies are essential to enable such a goal. Three databases were selected to carry out the search. Then, a PRISMA and PICOTS based criteria for a more detailed review on the basis of monitoring lifestyle aspects were used to filter the publications. We identified 133 publications after removing duplicates. Fifteen were finally selected from our criteria. Many studies still use questionnaires as the only tool for monitoring and do not apply advanced analytic or AI approaches to fine-tune results. We anticipate a transformative boom in the near future developing and implementing solutions that are able to integrate, in a flexible and adaptable way, data from technologies and devices that users might already use. This will enable continuous monitoring of objective data to guide the personalized definition of lifestyle goals and data-driven coaching to offer the necessary support to ensure adherence and satisfaction. Full article

(This article belongs to the Special Issue Body Sensors Networks for E-Health Applications)

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