E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Wireless Sensor Network for Pervasive Medical Care"

Quicklinks

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

Deadline for manuscript submissions: closed (15 October 2014)

Special Issue Editor

Guest Editor
Dr. Nauman Aslam (Website)

Faculty of Engineering & Environment, Northumbria University Pandon Building, Room-247 Camden Street, Newcastle Upon Tyne, UK, NE2-1XE
Interests: wireless sensor networks; energy-efficient communication protocols; medical applications of wireless sensor networks; ad hoc networks; security and privacy enhancing technologies

Special Issue Information

Dear Colleague,

Recent advances in Wireless Sensor Networks (WSNs) have led to several emerging applications in pervasive medical care. A combination of Wireless Body Area Networks (WBANs) with wearable or implantable sensors and contextual information could potentially advance medical monitoring, diagnosis, treatment and patient care. There are numerous challenges to realization of pervasive medical care. These challenges include interoperability of communication interface, wearable computing, sensor data fusion, security and privacy of sensitive data, reliability of communication protocols, energy efficiency of sensor devices, decision support systems and user-friendly interfaces, etc. Although significant progress has been made in the last few years, more work is needed for the pragmatic realization of this technology. This special issue seeks original contributions addressing recent developments, medical sensor devices, techniques, applications, algorithms, architectures and implementations in pervasive medical care using WSNs. In particular, this special issue solicits original unpublished work concerning (but without being limited to) the following topics:

 

  • Wearable sensor devices in medical monitoring
  • Algorithms, communication protocols and architectures for medical monitoring applications
  • Integration of smart application for pervasive medical care
  • Security, integrity and privacy of medical data
  • Management of Wireless Body Area Sensor Networks
  • Interoperability of WSN/WBAN technologies with clinical environments
  • Trade-offs for energy efficiency and quality of service (QoS) in WBAN/WSNs in pervasive medical care
  • Decision support systems for analysis of data in pervasive medical care
  • Novel architectures and systems for pervasive health care services

Dr. Nauman Aslam
Guest Editor

Submission

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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed Open Access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs).

Keywords

  • wireless sensor networks
  • pervasive medical care
  • body area networks
  • biosensors
  • smart patient monitoring
  • algorithms and communication protocols
  • medical monitoring applications

Published Papers (16 papers)

View options order results:
result details:
Displaying articles 1-16
Export citation of selected articles as:

Research

Jump to: Review

Open AccessArticle Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare
Sensors 2015, 15(4), 8764-8786; doi:10.3390/s150408764
Received: 29 August 2014 / Revised: 25 March 2015 / Accepted: 1 April 2015 / Published: 15 April 2015
Cited by 3 | PDF Full-text (1289 KB) | HTML Full-text | XML Full-text
Abstract
Wireless Sensor Networks (WSN) are vulnerable to various sensor faults and faulty measurements. This vulnerability hinders efficient and timely response in various WSN applications, such as healthcare. For example, faulty measurements can create false alarms which may require unnecessary intervention from healthcare [...] Read more.
Wireless Sensor Networks (WSN) are vulnerable to various sensor faults and faulty measurements. This vulnerability hinders efficient and timely response in various WSN applications, such as healthcare. For example, faulty measurements can create false alarms which may require unnecessary intervention from healthcare personnel. Therefore, an approach to differentiate between real medical conditions and false alarms will improve remote patient monitoring systems and quality of healthcare service afforded by WSN. In this paper, a novel approach is proposed to detect sensor anomaly by analyzing collected physiological data from medical sensors. The objective of this method is to effectively distinguish false alarms from true alarms. It predicts a sensor value from historic values and compares it with the actual sensed value for a particular instance. The difference is compared against a threshold value, which is dynamically adjusted, to ascertain whether the sensor value is anomalous. The proposed approach has been applied to real healthcare datasets and compared with existing approaches. Experimental results demonstrate the effectiveness of the proposed system, providing high Detection Rate (DR) and low False Positive Rate (FPR). Full article
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)
Open AccessArticle A New mHealth Communication Framework for Use in Wearable WBANs and Mobile Technologies
Sensors 2015, 15(2), 3379-3408; doi:10.3390/s150203379
Received: 15 October 2014 / Revised: 10 November 2014 / Accepted: 23 December 2014 / Published: 3 February 2015
Cited by 2 | PDF Full-text (2927 KB) | HTML Full-text | XML Full-text
Abstract
Driven by the development of biomedical sensors and the availability of high mobile bandwidth, mobile health (mHealth) systems are now offering a wider range of new services. This revolution makes the idea of in-home health monitoring practical and provides the opportunity for [...] Read more.
Driven by the development of biomedical sensors and the availability of high mobile bandwidth, mobile health (mHealth) systems are now offering a wider range of new services. This revolution makes the idea of in-home health monitoring practical and provides the opportunity for assessment in “real-world” environments producing more ecologically valid data. In the field of insomnia diagnosis, for example, it is now possible to offer patients wearable sleep monitoring systems which can be used in the comfort of their homes over long periods of time. The recorded data collected from body sensors can be sent to a remote clinical back-end system for analysis and assessment. Most of the research on sleep reported in the literature mainly looks into how to automate the analysis of the sleep data and does not address the problem of the efficient encoding and secure transmissions of the collected health data. This article reviews the key enabling communication technologies and research challenges for the design of efficient mHealth systems. An end-to-end mHealth system architecture enabling the remote assessment and monitoring of patient’s sleep disorders is then proposed and described as a case study. Finally, various mHealth data serialization formats and machine-to-machine (M2M) communication protocols are evaluated and compared under realistic operating conditions. Full article
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)
Open AccessArticle An Active Cooperation-Aware Spectrum Allocation Mechanism for Body Sensor Networks
Sensors 2015, 15(2), 2812-2831; doi:10.3390/s150202812
Received: 15 October 2014 / Accepted: 13 January 2015 / Published: 28 January 2015
PDF Full-text (829 KB) | HTML Full-text | XML Full-text
Abstract
A cognitive radio-based spectrum allocation scheme using an active cooperative-aware mechanism is proposed in this paper. The scheme ensures that the primary user and secondary users cooperate actively for their own benefits. The primary user releases some spectrum resources to secondary users [...] Read more.
A cognitive radio-based spectrum allocation scheme using an active cooperative-aware mechanism is proposed in this paper. The scheme ensures that the primary user and secondary users cooperate actively for their own benefits. The primary user releases some spectrum resources to secondary users to actively stimulate them to actively join the cooperative transmission of the primary user, and secondary users help the primary user to relay data in return, as well as its self-data transmission at the same time. The Stackelberg game is used to evenly and jointly optimize the utilities of both the primary and secondary users. Simulation results show that the proposed active cooperation-aware mechanism could improve the body sensor network performance. Full article
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)
Open AccessArticle An Activity Recognition Model Using Inertial Sensor Nodes in a Wireless Sensor Network for Frozen Shoulder Rehabilitation Exercises
Sensors 2015, 15(1), 2181-2204; doi:10.3390/s150102181
Received: 8 July 2014 / Revised: 2 December 2014 / Accepted: 12 January 2015 / Published: 19 January 2015
Cited by 4 | PDF Full-text (3390 KB) | HTML Full-text | XML Full-text
Abstract
This paper proposes a model for recognizing motions performed during rehabilitation exercises for frozen shoulder conditions. The model consists of wearable wireless sensor network (WSN) inertial sensor nodes, which were developed for this study, and enables the ubiquitous measurement of bodily motions. [...] Read more.
This paper proposes a model for recognizing motions performed during rehabilitation exercises for frozen shoulder conditions. The model consists of wearable wireless sensor network (WSN) inertial sensor nodes, which were developed for this study, and enables the ubiquitous measurement of bodily motions. The model employs the back propagation neural network (BPNN) algorithm to compute motion data that are formed in the WSN packets; herein, six types of rehabilitation exercises were recognized. The packets sent by each node are converted into six components of acceleration and angular velocity according to three axes. Motor features such as basic acceleration, angular velocity, and derivative tilt angle were input into the training procedure of the BPNN algorithm. In measurements of thirteen volunteers, the accelerations and included angles of nodes were adopted from possible features to demonstrate the procedure. Five exercises involving simple swinging and stretching movements were recognized with an accuracy of 85%–95%; however, the accuracy with which exercises entailing spiral rotations were recognized approximately 60%. Thus, a characteristic space and enveloped spectrum improving derivative features were suggested to enable identifying customized parameters. Finally, a real-time monitoring interface was developed for practical implementation. The proposed model can be applied in ubiquitous healthcare self-management to recognize rehabilitation exercises. Full article
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)
Open AccessArticle In-to-Out Body Antenna-Independent Path Loss Model for Multilayered Tissues and Heterogeneous Medium
Sensors 2015, 15(1), 408-421; doi:10.3390/s150100408
Received: 15 October 2014 / Accepted: 18 December 2014 / Published: 29 December 2014
Cited by 1 | PDF Full-text (1458 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we investigate multilayered lossy and heterogeneous media for wireless body area networks (WBAN) to develop a simple, fast and efficient analytical in-to-out body path loss (PL) model at 2.45 GHz and, thus, avoid time-consuming simulations. The PL model is [...] Read more.
In this paper, we investigate multilayered lossy and heterogeneous media for wireless body area networks (WBAN) to develop a simple, fast and efficient analytical in-to-out body path loss (PL) model at 2.45 GHz and, thus, avoid time-consuming simulations. The PL model is an antenna-independent model and is validated with simulations in layered medium, as well as in a 3D human model using electromagnetic solvers. Full article
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)
Open AccessArticle QoS-Aware Error Recovery in Wireless Body Sensor Networks Using Adaptive Network Coding
Sensors 2015, 15(1), 440-464; doi:10.3390/s150100440
Received: 12 July 2014 / Accepted: 3 December 2014 / Published: 29 December 2014
Cited by 2 | PDF Full-text (882 KB) | HTML Full-text | XML Full-text
Abstract
Wireless body sensor networks (WBSNs) for healthcare and medical applications are real-time and life-critical infrastructures, which require a strict guarantee of quality of service (QoS), in terms of latency, error rate and reliability. Considering the criticality of healthcare and medical applications, WBSNs [...] Read more.
Wireless body sensor networks (WBSNs) for healthcare and medical applications are real-time and life-critical infrastructures, which require a strict guarantee of quality of service (QoS), in terms of latency, error rate and reliability. Considering the criticality of healthcare and medical applications, WBSNs need to fulfill users/applications and the corresponding network’s QoS requirements. For instance, for a real-time application to support on-time data delivery, a WBSN needs to guarantee a constrained delay at the network level. A network coding-based error recovery mechanism is an emerging mechanism that can be used in these systems to support QoS at very low energy, memory and hardware cost. However, in dynamic network environments and user requirements, the original non-adaptive version of network coding fails to support some of the network and user QoS requirements. This work explores the QoS requirements of WBSNs in both perspectives of QoS. Based on these requirements, this paper proposes an adaptive network coding-based, QoS-aware error recovery mechanism for WBSNs. It utilizes network-level and user-/application-level information to make it adaptive in both contexts. Thus, it provides improved QoS support adaptively in terms of reliability, energy efficiency and delay. Simulation results show the potential of the proposed mechanism in terms of adaptability, reliability, real-time data delivery and network lifetime compared to its counterparts. Full article
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)
Open AccessArticle Effective Low-Power Wearable Wireless Surface EMG Sensor Design Based on Analog-Compressed Sensing
Sensors 2014, 14(12), 24305-24328; doi:10.3390/s141224305
Received: 11 October 2014 / Revised: 29 November 2014 / Accepted: 5 December 2014 / Published: 17 December 2014
Cited by 3 | PDF Full-text (902 KB) | HTML Full-text | XML Full-text
Abstract
Surface Electromyography (sEMG) is a non-invasive measurement process that does not involve tools and instruments to break the skin or physically enter the body to investigate and evaluate the muscular activities produced by skeletal muscles. The main drawbacks of existing sEMG systems [...] Read more.
Surface Electromyography (sEMG) is a non-invasive measurement process that does not involve tools and instruments to break the skin or physically enter the body to investigate and evaluate the muscular activities produced by skeletal muscles. The main drawbacks of existing sEMG systems are: (1) they are not able to provide real-time monitoring; (2) they suffer from long processing time and low speed; (3) they are not effective for wireless healthcare systems because they consume huge power. In this work, we present an analog-based Compressed Sensing (CS) architecture, which consists of three novel algorithms for design and implementation of wearable wireless sEMG bio-sensor. At the transmitter side, two new algorithms are presented in order to apply the analog-CS theory before Analog to Digital Converter (ADC). At the receiver side, a robust reconstruction algorithm based on a combination of ℓ1-ℓ1-optimization and Block Sparse Bayesian Learning (BSBL) framework is presented to reconstruct the original bio-signals from the compressed bio-signals. The proposed architecture allows reducing the sampling rate to 25% of Nyquist Rate (NR). In addition, the proposed architecture reduces the power consumption to 40%, Percentage Residual Difference (PRD) to 24%, Root Mean Squared Error (RMSE) to 2%, and the computation time from 22 s to 9.01 s, which provide good background for establishing wearable wireless healthcare systems. The proposed architecture achieves robust performance in low Signal-to-Noise Ratio (SNR) for the reconstruction process. Full article
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)
Open AccessArticle Secure Publish-Subscribe Protocols for Heterogeneous Medical Wireless Body Area Networks
Sensors 2014, 14(12), 22619-22642; doi:10.3390/s141222619
Received: 16 October 2014 / Revised: 30 October 2014 / Accepted: 19 November 2014 / Published: 28 November 2014
Cited by 7 | PDF Full-text (1056 KB) | HTML Full-text | XML Full-text
Abstract
Security and privacy issues in medical wireless body area networks (WBANs) constitute a major unsolved concern because of the challenges posed by the scarcity of resources in WBAN devices and the usability restrictions imposed by the healthcare domain. In this paper, we [...] Read more.
Security and privacy issues in medical wireless body area networks (WBANs) constitute a major unsolved concern because of the challenges posed by the scarcity of resources in WBAN devices and the usability restrictions imposed by the healthcare domain. In this paper, we describe a WBAN architecture based on the well-known publish-subscribe paradigm. We present two protocols for publishing data and sending commands to a sensor that guarantee confidentiality and fine-grained access control. Both protocols are based on a recently proposed ciphertext policy attribute-based encryption (CP-ABE) scheme that is lightweight enough to be embedded into wearable sensors. We show how sensors can implement lattice-based access control (LBAC) policies using this scheme, which are highly appropriate for the eHealth domain. We report experimental results with a prototype implementation demonstrating the suitability of our proposed solution. Full article
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)
Figures

Open AccessArticle Early Classification of Pathological Heartbeats on Wireless Body Sensor Nodes
Sensors 2014, 14(12), 22532-22551; doi:10.3390/s141222532
Received: 14 October 2014 / Revised: 12 November 2014 / Accepted: 19 November 2014 / Published: 27 November 2014
PDF Full-text (444 KB) | HTML Full-text | XML Full-text
Abstract
Smart Wireless Body Sensor Nodes (WBSNs) are a novel class of unobtrusive, battery-powered devices allowing the continuous monitoring and real-time interpretation of a subject’s bio-signals, such as the electrocardiogram (ECG). These low-power platforms, while able to perform advanced signal processing to extract [...] Read more.
Smart Wireless Body Sensor Nodes (WBSNs) are a novel class of unobtrusive, battery-powered devices allowing the continuous monitoring and real-time interpretation of a subject’s bio-signals, such as the electrocardiogram (ECG). These low-power platforms, while able to perform advanced signal processing to extract information on heart conditions, are usually constrained in terms of computational power and transmission bandwidth. It is therefore essential to identify in the early stages which parts of an ECG are critical for the diagnosis and, only in these cases, activate on demand more detailed and computationally intensive analysis algorithms. In this work, we present a comprehensive framework for real-time automatic classification of normal and abnormal heartbeats, targeting embedded and resource-constrained WBSNs. In particular, we provide a comparative analysis of different strategies to reduce the heartbeat representation dimensionality, and therefore the required computational effort. We then combine these techniques with a neuro-fuzzy classification strategy, which effectively discerns normal and pathological heartbeats with a minimal run time and memory overhead. We prove that, by performing a detailed analysis only on the heartbeats that our classifier identifies as abnormal, a WBSN system can drastically reduce its overall energy consumption. Finally, we assess the choice of neuro-fuzzy classification by comparing its performance and workload with respect to other state-of-the-art strategies. Experimental results using the MIT-BIH Arrhythmia database show energy savings of as much as 60% in the signal processing stage, and 63% in the subsequent wireless transmission, when a neuro-fuzzy classification structure is employed, coupled with a dimensionality reduction technique based on random projections. Full article
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)
Open AccessArticle An Energy Efficient MAC Protocol for Multi-Hop Swallowable Body Sensor Networks
Sensors 2014, 14(10), 19457-19476; doi:10.3390/s141019457
Received: 4 June 2014 / Revised: 21 August 2014 / Accepted: 10 October 2014 / Published: 17 October 2014
Cited by 3 | PDF Full-text (1595 KB) | HTML Full-text | XML Full-text
Abstract
Swallowable body sensor networks (BSNs) are composed of sensors which are swallowed by patients and send the collected data to the outside coordinator. These sensors are energy constraint and the batteries are difficult to be replaced. The medium access control (MAC) protocol [...] Read more.
Swallowable body sensor networks (BSNs) are composed of sensors which are swallowed by patients and send the collected data to the outside coordinator. These sensors are energy constraint and the batteries are difficult to be replaced. The medium access control (MAC) protocol plays an important role in energy management. This paper investigates an energy efficient MAC protocol design for swallowable BSNs. Multi-hop communication is analyzed and proved more energy efficient than single-hop communication within the human body when the circuitry power is low. Based on this result, a centrally controlled time slotting schedule is proposed. The major workload is shifted from the sensors to the coordinator. The coordinator collects the path-loss map and calculates the schedules, including routing, slot assignment and transmission power. Sensor nodes follow the schedules to send data in a multi-hop way. The proposed protocol is compared with the IEEE 802.15.6 protocol in terms of energy consumption. The results show that it is more energy efficient than IEEE 802.15.6 for swallowable BSN scenarios. Full article
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)
Figures

Open AccessArticle Comparison and Characterization of Android-Based Fall Detection Systems
Sensors 2014, 14(10), 18543-18574; doi:10.3390/s141018543
Received: 25 June 2014 / Revised: 22 September 2014 / Accepted: 23 September 2014 / Published: 8 October 2014
Cited by 8 | PDF Full-text (1447 KB) | HTML Full-text | XML Full-text
Abstract
Falls are a foremost source of injuries and hospitalization for seniors. The adoption of automatic fall detection mechanisms can noticeably reduce the response time of the medical staff or caregivers when a fall takes place. Smartphones are being increasingly proposed as wearable, [...] Read more.
Falls are a foremost source of injuries and hospitalization for seniors. The adoption of automatic fall detection mechanisms can noticeably reduce the response time of the medical staff or caregivers when a fall takes place. Smartphones are being increasingly proposed as wearable, cost-effective and not-intrusive systems for fall detection. The exploitation of smartphones’ potential (and in particular, the Android Operating System) can benefit from the wide implantation, the growing computational capabilities and the diversity of communication interfaces and embedded sensors of these personal devices. After revising the state-of-the-art on this matter, this study develops an experimental testbed to assess the performance of different fall detection algorithms that ground their decisions on the analysis of the inertial data registered by the accelerometer of the smartphone. Results obtained in a real testbed with diverse individuals indicate that the accuracy of the accelerometry-based techniques to identify the falls depends strongly on the fall pattern. The performed tests also show the difficulty to set detection acceleration thresholds that allow achieving a good trade-off between false negatives (falls that remain unnoticed) and false positives (conventional movements that are erroneously classified as falls). In any case, the study of the evolution of the battery drain reveals that the extra power consumption introduced by the Android monitoring applications cannot be neglected when evaluating the autonomy and even the viability of fall detection systems. Full article
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)
Open AccessArticle Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals
Sensors 2014, 14(10), 17915-17936; doi:10.3390/s141017915
Received: 17 June 2014 / Revised: 17 September 2014 / Accepted: 19 September 2014 / Published: 26 September 2014
Cited by 14 | PDF Full-text (4847 KB) | HTML Full-text | XML Full-text
Abstract
Driving drowsiness is a major cause of traffic accidents worldwide and has drawn the attention of researchers in recent decades. This paper presents an application for in-vehicle non-intrusive mobile-device-based automatic detection of driver sleep-onset in real time. The proposed application classifies the [...] Read more.
Driving drowsiness is a major cause of traffic accidents worldwide and has drawn the attention of researchers in recent decades. This paper presents an application for in-vehicle non-intrusive mobile-device-based automatic detection of driver sleep-onset in real time. The proposed application classifies the driving mental fatigue condition by analyzing the electroencephalogram (EEG) and respiration signals of a driver in the time and frequency domains. Our concept is heavily reliant on mobile technology, particularly remote physiological monitoring using Bluetooth. Respiratory events are gathered, and eight-channel EEG readings are captured from the frontal, central, and parietal (Fpz-Cz, Pz-Oz) regions. EEGs are preprocessed with a Butterworth bandpass filter, and features are subsequently extracted from the filtered EEG signals by employing the wavelet-packet-transform (WPT) method to categorize the signals into four frequency bands: α, β, θ, and δ. A mutual information (MI) technique selects the most descriptive features for further classification. The reduction in the number of prominent features improves the sleep-onset classification speed in the support vector machine (SVM) and results in a high sleep-onset recognition rate. Test results reveal that the combined use of the EEG and respiration signals results in 98.6% recognition accuracy. Our proposed application explores the possibility of processing long-term multi-channel signals. Full article
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)
Open AccessArticle Smart Multi-Level Tool for Remote Patient Monitoring Based on a Wireless Sensor Network and Mobile Augmented Reality
Sensors 2014, 14(9), 17212-17234; doi:10.3390/s140917212
Received: 17 July 2014 / Revised: 9 September 2014 / Accepted: 11 September 2014 / Published: 16 September 2014
Cited by 3 | PDF Full-text (3709 KB) | HTML Full-text | XML Full-text
Abstract
Technological innovations in the field of disease prevention and maintenance of patient health have enabled the evolution of fields such as monitoring systems. One of the main advances is the development of real-time monitors that use intelligent and wireless communication technology. In [...] Read more.
Technological innovations in the field of disease prevention and maintenance of patient health have enabled the evolution of fields such as monitoring systems. One of the main advances is the development of real-time monitors that use intelligent and wireless communication technology. In this paper, a system is presented for the remote monitoring of the body temperature and heart rate of a patient by means of a wireless sensor network (WSN) and mobile augmented reality (MAR). The combination of a WSN and MAR provides a novel alternative to remotely measure body temperature and heart rate in real time during patient care. The system is composed of (1) hardware such as Arduino microcontrollers (in the patient nodes), personal computers (for the nurse server), smartphones (for the mobile nurse monitor and the virtual patient file) and sensors (to measure body temperature and heart rate), (2) a network layer using WiFly technology, and (3) software such as LabView, Android SDK, and DroidAR. The results obtained from tests show that the system can perform effectively within a range of 20 m and requires ten minutes to stabilize the temperature sensor to detect hyperthermia, hypothermia or normal body temperature conditions. Additionally, the heart rate sensor can detect conditions of tachycardia and bradycardia. Full article
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)
Open AccessArticle Wearability Assessment of a Wearable System for Parkinson’s Disease Remote Monitoring Based on a Body Area Network of Sensors
Sensors 2014, 14(9), 17235-17255; doi:10.3390/s140917235
Received: 14 June 2014 / Revised: 3 September 2014 / Accepted: 3 September 2014 / Published: 16 September 2014
Cited by 10 | PDF Full-text (2531 KB) | HTML Full-text | XML Full-text
Abstract
Wearable technologies for health monitoring have become a reality in the last few years. So far, most research studies have focused on assessments of the technical performance of these systems, as well as the validation of the clinical outcomes. Nevertheless, the success [...] Read more.
Wearable technologies for health monitoring have become a reality in the last few years. So far, most research studies have focused on assessments of the technical performance of these systems, as well as the validation of the clinical outcomes. Nevertheless, the success in the acceptance of these solutions depends not only on the technical and clinical effectiveness, but on the final user acceptance. In this work the compliance of a telehealth system for the remote monitoring of Parkinson’s disease (PD) patients is presented with testing in 32 PD patients. This system, called PERFORM, is based on a Body Area Network (BAN) of sensors which has already been validated both from the technical and clinical point for view. Diverse methodologies (REBA, Borg and CRS scales in combination with a body map) are employed to study the comfort, biomechanical and physiological effects of the system. The test results allow us to conclude that the acceptance of this system is satisfactory with all the levels of effect on each component scoring in the lowest ranges. This study also provided useful insights and guidelines to lead to redesign of the system to improve patient compliance. Full article
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)

Review

Jump to: Research

Open AccessReview Survey of WBSNs for Pre-Hospital Assistance: Trends to Maximize the Network Lifetime and Video Transmission Techniques
Sensors 2015, 15(5), 11993-12021; doi:10.3390/s150511993
Received: 14 October 2014 / Accepted: 18 May 2015 / Published: 22 May 2015
Cited by 2 | PDF Full-text (634 KB) | HTML Full-text | XML Full-text
Abstract
This survey aims to encourage the multidisciplinary communities to join forces for innovation in the mobile health monitoring area. Specifically, multidisciplinary innovations in medical emergency scenarios can have a significant impact on the effectiveness and quality of the procedures and practices in [...] Read more.
This survey aims to encourage the multidisciplinary communities to join forces for innovation in the mobile health monitoring area. Specifically, multidisciplinary innovations in medical emergency scenarios can have a significant impact on the effectiveness and quality of the procedures and practices in the delivery of medical care. Wireless body sensor networks (WBSNs) are a promising technology capable of improving the existing practices in condition assessment and care delivery for a patient in a medical emergency. This technology can also facilitate the early interventions of a specialist physician during the pre-hospital period. WBSNs make possible these early interventions by establishing remote communication links with video/audio support and by providing medical information such as vital signs, electrocardiograms, etc. in real time. This survey focuses on relevant issues needed to understand how to setup a WBSN for medical emergencies. These issues are: monitoring vital signs and video transmission, energy efficient protocols, scheduling, optimization and energy consumption on a WBSN. Full article
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)
Figures

Open AccessReview A Survey on M2M Systems for mHealth: A Wireless Communications Perspective
Sensors 2014, 14(10), 18009-18052; doi:10.3390/s141018009
Received: 29 July 2014 / Revised: 5 September 2014 / Accepted: 17 September 2014 / Published: 26 September 2014
Cited by 28 | PDF Full-text (1407 KB) | HTML Full-text | XML Full-text
Abstract
In the new era of connectivity, marked by the explosive number of wireless electronic devices and the need for smart and pervasive applications, Machine-to-Machine (M2M) communications are an emerging technology that enables the seamless device interconnection without the need of human interaction. [...] Read more.
In the new era of connectivity, marked by the explosive number of wireless electronic devices and the need for smart and pervasive applications, Machine-to-Machine (M2M) communications are an emerging technology that enables the seamless device interconnection without the need of human interaction. The use of M2M technology can bring to life a wide range of mHealth applications, with considerable benefits for both patients and healthcare providers. Many technological challenges have to be met, however, to ensure the widespread adoption of mHealth solutions in the future. In this context, we aim to provide a comprehensive survey on M2M systems for mHealth applications from a wireless communication perspective. An end-to-end holistic approach is adopted, focusing on different communication aspects of the M2M architecture. Hence, we first provide a systematic review ofWireless Body Area Networks (WBANs), which constitute the enabling technology at the patient’s side, and then discuss end-to-end solutions that involve the design and implementation of practical mHealth applications. We close the survey by identifying challenges and open research issues, thus paving the way for future research opportunities. Full article
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)
Figures

Journal Contact

MDPI AG
Sensors Editorial Office
St. Alban-Anlage 66, 4052 Basel, Switzerland
sensors@mdpi.com
Tel. +41 61 683 77 34
Fax: +41 61 302 89 18
Editorial Board
Contact Details Submit to Sensors
Back to Top