Body Sensor Network Applications Research Articles

Powering of IOTs through single jersey wearable tribo-electric nano generator

A simple, lightweight, and easy to develop Single Jersey Wearable Tribo-Electric Nano Generators (SJ-WTENG) were constructed using Cotton and Acrylic fabrics (Triboelectric series materials). Fabrics were also coated with Maghemite (γ-Fe2O3) nanoparticles (13 nm) to increase the electrical conductance of the samples. Compression and Vertical contact-separation modes were adopted for studying the performance of the developed samples. Along with a Single WTENG sample, the outputs of two samples connected in series were also measured. To study the effect of developed Maghemite (γ-Fe2O3) nanoparticle coating, non-coated fabrics WTENGs were also constructed and tested. The maximum voltage reached with the Maghemite (γ-Fe2O3) nanoparticle-coated SJ-WTENG samples was a time-varying signal of 7.68 volts peak to peak volts with an approximate frequency of 50.5 Hertz. A shotky diode-based full bridge rectifier was used to get the DC voltage. The rectified DC signal was observed to be 5 volts which was enough to light up an LED with a threshold voltage of 1.7 volts DC as well as charge 3.7 volts, 3.6Ah Li-ion battery pack. Results confirmed that the application of Maghemite (γ-Fe2O3) nanoparticles was useful in augmenting the output of the proposed SJ-WTENG design. The proposed system can be used to power the battery powered IOT (Internet of Things) devices, widely used in medical and body sensor network applications.

The use of IoT in Health Care System: A Perspective

Health care becomes one of serious issues all over the planet, particularly in maturing individuals, where it costs hugewellbeing spreads and assets. The IoT can possibly further develop wellbeing and prosperity through more prominenteffectiveness and further developed care in existing medical services settings, by empowering more noteworthy utilization ofremote telehealth arrangement, and empowering people to screen their own wellbeing everyday, further develop solace andbetter oversee conditions, analyze ailments all the more rapidly and advance therapy systems. Expanding market interest forsuperior execution and convenient processing gadgets requires energy proficient gadgets. Whenever an organization ofgadgets is thought of, as on account of the Internet of Things, it is vital to consider low power plan at the sensor/actuator levelas well as in the sensor organization. Human body correspondence has shown to be a proficient method of correspondence forclose to handle body sensor network applications. The recent fads in medical services are bit by bit advancing with theassistance of IoT which might make us more wellbeing cognizant. As far as deterrent consideration, IoT wellness gadgetslike Fit pieces, and development sensors currently incorporated into many new advanced cells, empower numerous people toscreen and track themselves, which for the most part advances better ways of life. This paper audits the ideas, applicationsand different existing advances for medical care.

Secure Internet of Things based hybrid optimization techniques for optimal centroid routing protocol in wireless sensor network

SummaryThe recent survey depicts internet growth has been reached to billion of people and Internet of Things (IoT) as another milestone. IoT is wired or wireless communication technologies to establish a communication channel between devices and services available over the Internet. The wireless sensor networks (WSNs) is one of the most essential technologies assisted in IoT for real‐time applications. The energy efficiency is a major issue in IoT and it becomes more complex due to large scalability and the WSNs cannot be applied directly to the IoT. Moreover, routing is a very challenging aspect that takes place in such platform because of it is intrinsic properties. In this article, we propose an optimal centroid‐based routing protocol (OCRP) for WSN assisted IoT based on hybrid optimization techniques, which aim is to enhance the energy efficiency and network lifetime. IoT network is composed by serving nodes and end users. In OCRP, a multi‐objective swarm optimization (MSO) algorithm is used to perform the clustering, which reduce the chaotic in nature of energy consumption. Then, multiservice queue based ant optimization algorithm is proposed for next neighborhood selection for inter‐cluster routing. Finally, our OCRP protocol is experimented with different simulation in network simulator (NS‐2) tool with wireless body sensor network (WBSN) application. The simulation results show the effectiveness of OCRP protocol in terms of energy consumption, average data transmission rate and network lifetime.

A transfer fusion framework for body sensor networks (BSNs): Dynamic domain adaptation from distribution evaluation to domain evaluation

Information fusion is a scenario-driven, long-term, challenging task in body sensor networks (BSNs). Recent strategies, such as supervised or semi-supervised learning, rely heavily on fusion-decision models that are supported by prior knowledge or experience. Such techniques typically need a large amount of labeled physiological data and require the target data to have the same distribution as the prior data, which is challenging to achieve in practice. Domain adaptation (DA) is considered a viable solution for the cross-domain problem in BSNs. However, the correlation between the fusion rules and DA was rarely considered in existing studies. The independence of the domain deviation for each sensing source was ignored. This study aims to provide a universal solution for cross-domain information fusion scenarios in BSNs. Firstly, a transfer fusion framework (TF) was proposed to simultaneously solve the problems of the information fusion and multidomain deviation in BSNs by adjusting the correlation between the DA and fusion. Secondly, a DA algorithm based on dynamic domain evaluation (DDE) was proposed for the DA stage of the TF framework. In addition, we provided a feasible solution for quantitatively evaluating the domain weights. Extensive experiments on emotion recognition, fall detection, daily activity recognition, and hand movement recognition have verified the adaptability and promising performance of TF and DDE for cross-domain information fusion in BSNs. The average classification accuracy of the TF is 8.39% and 6.71% higher than that of the conventional fusion transfer framework and the baseline method, respectively. In all transfer tasks, DDE shows the best performance, which is 4.66% higher than the classification accuracy of the optimal comparison method. This study can provide technical support for BSN applications based on cross-domain information fusion.

Wearable Spoof Surface Plasmon Polariton Transmission Line

In this work, we present a study of Spoof Surface Plasmon Polari-ton (SSPP) supported by a meandered Transmission Line (TL) dedicated to wireless body sensor network applications. First, dispersion curves evidence the existence of a surface wave propagation, called odd mode according to the symmetry of the magnetic field. This mode can be wirelessly excited with of a dipole antenna parallel-positioned above the meandered transmission line. Experimental part is validated with a SSPP TL fabricated on a Kapton substrate and compared with a wearable SSPP TL produced by embroidering a metallic yarn on a textile substrate. Second, transmission measurements for both SSPP TLs are also pre-sented and compared. The difference of performances achieved between involved technologies is explained by the conductivity value of the metallic yarn. Finally, the use of embroidered SSPP TL shows an improvement of the transmis-sion compared with the transmission in free space. This study is investigated in simulation and experiments by determining the dispersion curves and the transmission for two SSPP TLs.

Two User Adaptation-Derived Features for Biometrical Classifications of User Identity in 3D-Sensor-Based Body Gesture Recognition Applications

For human gesture recognition applications, 3D-sensor-based approaches have received considerable attention and are crucial for future applications of an advanced body sensor network (BSN). A practical gesture recognition system is to apply active gesture patterns of a gesture-making user for body action classifications. 3D-sensor-based gesture recognition, categorized as biometric recognition in BSN applications, is greatly lack of the extensible cognition ability due to substandard recognition accuracy on the gesture-making user identity. To overcome this problem, this paper proposes an active gesture-based user identity recognition approach using a robust feature design, called user adaptation (UA) features, derived from a UA process. Two different UA features, namely Eigen Centroid-UA and Eigen Transform-UA features, were developed in this paper to accurately represent the adaptive learning tendency of gesture recognition for a specific gesture-making user. Compared with traditional 3D sensor gesture-based identity recognition approaches that employ only the feature of fixed body skeleton information without any UA designs, the presented UA-feature can exhibit fine adaptation learning continuously to the specific action user; therefore, superior identity recognition accuracy will be constantly ensured. To demonstrate the efficiency and effectiveness of the developed robust UA features in this paper, experiments on gesture-making user identification by Gaussian mixture model applying the proposed Eigen Centroid-UA feature and verification by support vector machine applying the proposed Eigen Transform-UA feature were conducted.

Recent Advances in Fabrication Methods for Flexible Antennas in Wearable Devices: State of the Art.

Antennas are a vital component of the wireless body sensor networks devices. A wearable antenna in this system can be used as a communication component or energy harvester. This paper presents a detailed review to recent advances fabrication methods for flexible antennas. Such antennas, for any applications in wireless body sensor networks, have specific considerations such as flexibility, conformability, robustness, and ease of integration, as opposed to conventional antennas. In recent years, intriguing approaches have demonstrated antennas embroidered on fabrics, encapsulated in polymer composites, printed using inkjets on flexible laminates and a 3-D printer and, more interestingly, by injecting liquid metal in microchannels. This article presents an operational perspective of such advanced approaches and beyond, while analyzing the strengths and limitations of each in the microwave as well as millimeter-wave regions. Navigating through recent developments in each area, mechanical and electrical constitutive parameters are reviewed, and finally, some open challenges are presented as well for future research directions.

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

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.

Secure and intelligent architecture for cloud-based healthcare applications in wireless body sensor networks

Secure and intelligent architecture for cloud-based healthcare applications in wireless body sensor networks

Secure and intelligent architecture for cloud-based healthcare applications in wireless body sensor networks

Wireless Sensor Networks (WSNs) are becoming a significant enabling technology for a wide variety of applications. Advances in WSN have facilitated the realisation of pervasive health monitoring for both homecare and hospital environments. Sensor nodes are capable of sensing, processing, and communicating one or more vital signs, and they can be used in Wireless Body Sensor Networks (WBSNs) for health monitoring. Many studies were performed and are on-going in order to develop flexible, reliable, secure, real-time, and power-efficient WBSNs suitable for healthcare applications. This paper concentrates on the development of intelligent secure architecture for cloud based healthcare applications in wireless sensor networks. This paper describes the applications, issues and challenges of BSN in healthcare, energy efficient body sensor network architecture using aggregation, secure data collection, storage and data sharing using an authentication algorithm and a novel prediction model which act as an expert system for disease management.

A stretchable piezoelectric elastic composite

In this work, a stretchable piezoelectric elastic composite has been developed by incorporating PbZr0.52Ti0.48O3 (PZT) particles into polymer matrix. The output voltage and current can reach 8 V and 0.4 μA, respectively. Likewise, the steady piezoelectric signal can respond to different strain rates. The measurement on human body shows that the piezoelectric composite can be used to capture kinetic energy and monitor limb motion for body sensor network application. This work is significant for the conformal assembly of piezoelectric devices used in artificial intelligence system.

Energy efficiency comparison between data rate control and transmission power control algorithms for wireless body sensor networks

This article presents comparison between data rate or rate control, that is, video transmission rate control algorithm and transmission power control algorithms for two different cases. First, energy consumption due to high peak variable data rates in video transmission. Second, energy depletion due to high transmission power consumption and dynamic nature of wireless on-body channel. The former one focuses on constant (fixed) transmission power level and variable data rate (“severe” conditions), for example, medical monitoring of the emergency patients. The latter considers variable transmission power level and constant (fixed) data rate (“less severe” conditions), for example, electrocardiography measurement for patients in wireless body sensor networks. Besides, energy efficiency comparison analysis of battery-driven or video transmission rate control algorithm and transmission power control–driven or power control algorithm is presented. Finally, proposed algorithms are analyzed and categorized as energy-efficient and battery-friendly for medical applications in wireless body sensor networks.

Burst communication by using self-adaptive buffer allocation with energy-efficient in-body sensor networks

The revolution of wireless sensory devices in healthcare services is monitoring of human movements. Such type of motion analysis uses human's inertial information. This study is achieved by using Body Sensor Network (BSN) in which many wireless sensor nodes are placed on different parts of the body. Disease is recognised by shared processing of sensor information from multiple locations of the body. Even though this platform has high potential in healthcare, the commercialisation of these systems is difficult because of power requirements and wearability problems. In this paper, a self-adaptive greedy buffer allocation and scheduling algorithm (SGBAS) is presented for optimisation of communication model for BSN applications which uses self-adaptive buffers for limitation of communication to short bursts, power usage and transmitting sensed data to the backend servers periodically. The optimisation of QoS is achieved by introducing buffers. This project provides a self-adaptive heuristic scheme which is formulated to reduce transmissions among sensor nodes. For early diagnosis, it sends the data at right time for the analysis of healthcare professionals. Several experimental evaluation and simulations is to be done to assess the proposed SGBAS technique for allocation of buffers in real time under various simulation setups. Experiments demonstrate the effectiveness of SGBAS power reduction techniques.

Design and Implementation of a Compressed Sensing Encoder: Application to EMG and ECG Wireless Biosensors

Among the existing applications of wireless body sensor networks (WBSNs), a wearable health monitoring system (WHMS) is the most important. In a typical WHMS, miniature wireless biosensors, attached to or implanted in the human body, collect bio-signals such as the electrocardiogram (ECG), blood pressure or electromyogram (EMG) to provide real time and continuous health monitoring. In this paper, we present a compressed sensing (CS)-based approach to compress and recover the sensed bio-signals from the wireless biosensors of a WBSN. The CS encoding process has a low computational complexity and is suitable for use in power-constrained systems such as WHMS. We propose a simple deterministic measurement matrix, which is easy to implement in hardware. We design a digital CS encoder implementing the proposed measurement matrix and use it to compress the bio-signals in EMG and ECG wireless biosensors. The simulations and experimental results have shown that the EMG and ECG signals are compressed and recovered without perceptible loss if the compression ratios are, respectively, less than or equal to 75 and 87.5%. The obtained results have also confirmed the simplicity of the proposed measurement matrix since the CS encoder does not affect the memory usage or the processing time of the microcontrollers embedded in the wireless biosensors. Additionally, the CS encoder decreases by up to 75 and 87.5% the energy consumption of the transceivers for the EMG and ECG wireless biosensors.

UHF front‐end feeding RFID‐based body sensor networks by exploiting the reader signal

AbstractThis paper presents an integrated, high‐sensitivity UHF radio frequency identification (RFID) power management circuit for body sensor network applications. The circuit consists of a two‐stage RF‐DC Dickson's rectifier followed by an integrated five‐stage DC‐DC Pelliconi's charge pump driven by an ultralow start‐up voltage LC oscillator. The DC‐DC charge pump interposed between the RF‐DC rectifier and the output load provides the RF to load isolation avoiding losses due to the diodes reverse saturation current. The RF‐DC rectifier has been realized on FR4 substrate, while the charge pump and the oscillator have been realized in 180 nm complementary metal oxide semiconductor (CMOS) technology. Outdoor measurements demonstrate the ability of the power management circuit to provide 400 mV output voltage at 14 m distance from the UHF reader, in correspondence of −25 dBm input signal power. As demonstrated in the literature, such output voltage level is suitable to supply body sensor network nodes.

Wireless Body Sensor Networks

In this paper, the authors provide a detailed overview and technical discussion and analysis of the latest research trends in securing body sensor networks. The core of this work aims at: (1) identifying the resource limitations and energy challenges of this category of wireless sensor networks, (2) considering the life-critical applications and emergency contexts that are encompassed by body sensor network services, and (3) studying the effect of these peculiarities on the design and implementation of rigorous and efficient security algorithms and protocols. The survey discusses the main advancements in the design of body sensor network cryptographic services (key generation and management, authentication, confidentiality, integrity, and privacy) and sheds the light on the prominent developments achieved in the field of securing body sensor network data in Cloud computing architectures. The elastic virtualization mechanisms employed in the Cloud, as well as the lucrative computing and storage resources available, makes the integration of body sensor network applications, and Cloud platforms a natural choice that is packed with various security and privacy challenges. The work presented in this paper focuses on Cloud privacy and integrity mechanisms that rely on tamper-proof hardware and energy-efficient cryptographic data structures that are proving to be well-suited for operation in untrusted Cloud environments. This paper also examines two crucial design patterns that lie at the crux of any successful body sensor network deployment which are represented in: (1) attaining the right balance between the degree, complexity, span, and strength of the cryptographic operations employed and the energy resources they consume. (2) Achieving a feasible tradeoff between the privacy of the human subject wearing the body sensor network and the safety of this subject. This is done by a careful analysis of the medical status of the subject and other context-related information to control the degree of disclosure of sensitive medical data. The paper concludes by presenting a practical overview of the cryptographic support in the main body sensor network development frameworks such and TinyOS and SPINE and introduces a set of generalized guideline patterns and recommendations for designing and implementing cryptographic protocols in body sensor network environments.

A neural algorithm for the non-uniform and adaptive sampling of biomedical data

Background and objectiveBody sensors are finding increasing applications in the self-monitoring for health-care and in the remote surveillance of sensitive people. The physiological data to be sampled can be non-stationary, with bursts of high amplitude and frequency content providing most information. Such data could be sampled efficiently with a non-uniform schedule that increases the sampling rate only during activity bursts. MethodsA real time and adaptive algorithm is proposed to select the sampling rate, in order to reduce the number of measured samples, but still recording the main information. The algorithm is based on a neural network which predicts the subsequent samples and their uncertainties, requiring a measurement only when the risk of the prediction is larger than a selectable threshold. ResultsFour examples of application to biomedical data are discussed: electromyogram, electrocardiogram, electroencephalogram, and body acceleration. Sampling rates are reduced under the Nyquist limit, still preserving an accurate representation of the data and of their power spectral densities (PSD). For example, sampling at 60% of the Nyquist frequency, the percentage average rectified errors in estimating the signals are on the order of 10% and the PSD is fairly represented, until the highest frequencies. The method outperforms both uniform sampling and compressive sensing applied to the same data. ConclusionThe discussed method allows to go beyond Nyquist limit, still preserving the information content of non-stationary biomedical signals. It could find applications in body sensor networks to lower the number of wireless communications (saving sensor power) and to reduce the occupation of memory.

A Configurable Energy-Efficient Compressed Sensing Architecture With Its Application on Body Sensor Networks

The past decades have witnessed a rapid surge in new sensing and monitoring devices for well-being and healthcare. One key representative in this field is body sensor networks (BSNs). However, with advances in sensing technologies and embedded systems, wireless communication has gradually become one of the dominant energy-consuming sectors in BSN applications. Recently, compressed sensing (CS) has attracted increasing attention in solving this problem due to its enabled sub-Nyquest sampling rate. In this paper, we investigate the quantization effect in CS architecture and argue that the quantization configuration is a critical factor of the energy efficiency for the entire CS architecture. To this end, we present a novel configurable quantized compressed sensing (QCS) architecture, in which the sampling rate and quantization are jointly explored for better energy efficiency. Furthermore, to combat the computational complexity of the configuration procedure, we propose a rapid configuration algorithm, called RapQCS. According to the experiments involving several categories of real biosignals, the proposed configurable QCS architecture can gain more than 66% performance-energy tradeoff than the fixed QCS architecture. Moreover, our proposed RapQCS algorithm can achieve over $150\times$ speedup on average, while decreasing the reconstructed signal fidelity by only 2.32%.

A 300-mV 220-nW event-driven ADC with real-time QRS detection for wearable ECG sensors.

This paper presents an ultra-low-power event-driven analog-to-digital converter (ADC) with real-time QRS detection for wearable electrocardiogram (ECG) sensors in wireless body sensor network (WBSN) applications. Two QRS detection algorithms, pulse-triggered (PUT) and time-assisted PUT (t-PUT), are proposed based on the level-crossing events generated from the ADC. The PUT detector achieves 97.63% sensitivity and 97.33% positive prediction in simulation on the MIT-BIH Arrhythmia Database. The t-PUT improves the sensitivity and positive prediction to 97.76% and 98.59% respectively. Fabricated in 0.13 μm CMOS technology, the ADC with QRS detector consumes only 220 nW measured under 300 mV power supply, making it the first nanoWatt compact analog-to-information (A2I) converter with embedded QRS detector.

A framework for collaborative computing and multi-sensor data fusion in body sensor networks

Body Sensor Networks (BSNs) have emerged as the most effective technology enabling not only new e-Health methods and systems but also novel applications in human-centered areas such as electronic health care, fitness/welness systems, sport performance monitoring, interactive games, factory workers monitoring, and social physical interaction. Despite their enormous potential, they are currently mostly used only to monitor single individuals. Indeed, BSNs can proactively interact and collaborate to foster novel BSN applications centered on collaborative groups of individuals. In this paper, C-SPINE, a framework for Collaborative BSNs (CBSNs), is proposed. CBSNs are BSNs able to collaborate with each other to fulfill a common goal. They can support the development of novel smart wearable systems for cyberphysical pervasive computing environments. Collaboration therefore relies on interaction and synchronization among the CBSNs and on collaborative distributed computing atop the collaborating CBSNs. Specifically, collaboration is triggered upon CBSN proximity and relies on service-specific protocols allowing for managing services among the collaborating CBSNs. C-SPINE also natively supports multi-sensor data fusion among CBSNs to enable joint data analysis such as filtering, time-dependent data integration and classification. To demonstrate its effectiveness, C-SPINE is used to implement e-Shake, a collaborative CBSN system for the detection of emotions. The system is based on a multi-sensor data fusion schema to perform automatic detection of handshakes between two individuals and capture of possible heart-rate-based emotion reactions due to the individuals’ meeting.