Survey on wireless body area sensor networks for healthcare applications: Signal processing, data analysis and feedback

Wireless sensor networks (WSNs) technologies are considered as one of the key of the research areas in computer science and healthcare application industries.The wireless body area sensor networks (WBASNs) is a wireless network used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements.The paper surveys the state-of-the-art on WBASNs discussing the major components of research in this area including physiological sensing, data preprocessing, detection and classification of human related phenomena. We provide comparative studies of the technologies and techniques used in such systems

Graphene-Based Acousto-Optic Sensors with Vibrating Resonance Energy Transfer and Applications

Graphene as a two-dimensional planar material has numerous advantages for realizing high-performance nano-electromechanical systems (NEMS) such as nanoscale sensors including strain sensors, optical modulators or energy harvesters. Large Young’s modulus (1 TPa for single layer graphene), ultra-low weight, low residual stress and large breaking strength properties are important properties as two-dimensional (2D) ultrathin resonators. Graphene resonators are recently utilized for low complexity design of nanoscale acousto-optic sensors based on a novel theoretical model describing vibrating Förster resonance energy transfer (VFRET) mechanism. Proposed system combines the advantages of graphene with quantum dots (QDs) as donor and acceptor pairs with broad absorption spectrum, large cross-sections, tunable emission spectra, size-dependent emission wavelength, high photochemical stability and improved quantum yield. Device structure supporting wide-band resonance frequencies including acoustic and ultrasound ranges promises high-performance applications for challenging environments. Remote sensors and acousto-optic communication channels are formed for in-body applications, wireless body area sensor networks (WBASNs), space and interplanetary systems, microfluidics and visible light communication (VLC)-based architectures

SURVEY ON WIRELESS BODY AREA SENSOR NETWORKS FOR HEALTHCARE APPLICATIONS: SIGNAL PROCESSING, DATA ANALYSIS AND FEEDBACK

ABSTRACT. Wireless sensor networks (WSNs) technologies are considered as one of the key of the research areas in computer science and healthcare application industries. The wireless body area sensor networks (WBASNs) is a wireless network used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements. The paper surveys the state-of-the-art on WBASNs discussing the major components of research in this area including physiological sensing, data preprocessing, detection and classification of human related phenomena. We provide comparative studies of the technologies and techniques used in such systems

Wireless body area sensor networks signal processing and communication framework: Survey on sensing, communication technologies, delivery and feedback

Problem statement: The Wireless Body Area Sensor Networks (WBASNs) is a wireless network used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements.This study surveys the state-of-the-art on Wireless Body Area Networks, discussing the major components of research in this area including physiological sensing and preprocessing, WBASNs communication techniques and data fusion for gathering data from sensors.In addition, data analysis and feedback will be presented including feature extraction, detection and classification of human related phenomena.Approach: Comparative studies of the technologies and techniques used in such systems will be provided in this study, using qualitative comparisons and use case analysis to give insight on potential uses for different techniques.Results and Conclusion: Wireless Sensor Networks (WSNs) technologies are considered as one of the key of the research areas in computer science and healthcare application industries.Sensor supply chain and communication technologies used within the system and power consumption therein, depend largely on the use case and the characteristics of the application.Authors conclude that Life-saving applications and thorough studies and tests should be conducted before WBANs can be widely applied to humans, particularly to address the challenges related to robust techniques for detection and classification to increase the accuracy and hence the confidence of applying such techniques without physician intervention

Prioridade Dinâmica de Mensagens Aplicada a Redes de Sensores Corporais Sem-Fio

The Wireless Body Area Networks (WBANs) are a special case ofthe Wireless Sensor Networks (WSNs) and, in general, are responsiblefor gathering and transmitting biometric data from a givenpatient. One of the features of the WSNs is the self–configuration;i.e., the capability that a given parameter can be adjusted regardingsome occurrence in execution time. As stated, the objective of theWBANs is the gathering, through several sensors, of biometric dataof a patient. In general, because of possible daily activities and/orpathologies, some sensors can be more demanded (or required) incertain periods of the day. In these situations, information of thesementioned sensors are more relevant and require larger data deliverypriority. Thus, we propose QoSBody-AODV, a variation of theAODV routing protocol, which performs the dynamic adjustmentof the message’s priority, turning the network functioning to besensitive to the physical activities performed by the patient, as wellas pathological information

Capacity investigation of on-off keying in noncoherent channel settings at low SNR

On–off keying (OOK) has repossessed much new research interest to realize green communication for establishing autonomous sensor networks. To realize ultra-low power wireless design, we investigate the minimum energy per bit required for reliable communication of using OOK in a noncoherent channel setting where envelope detection is applied at the receiver. By defining different OOK channels with average transit power constraints, the achievability of the Shannon limit for both cases of using soft and hard decisions at the channel output is evaluated based on the analysis of the capacity per unit-cost at low signal-to-noise ratio. We demonstrate that in phase fading using hard decisions cannot destroy the capacity only if extremely asymmetric OOK inputs are used with a properly chosen threshold. The corresponding pulse-position modulation scheme is explicitly studied and demonstrated to be a Shannon-type solution. Moreover, we also consider a slow Rayleigh fading scenario where the transmitter and receiver have no access to channel realizations.Throughput per unit-cost results are developed to explore the trade-off between power efficiency and channel quality for noncoherent OOK using soft and hard decisions

A Novel Neuro-Fuzzy Model to Detect Human Emotions Using Different Set of Vital Factors with Performance Index Measure

A novel optimization algorithm is proposed for detecting human emotions(responses) using artificial intelligence techniques such as exhaustive search, fuzzy logic and neural networks. Previous models for detecting human emotions have used fourteen measurable physical and physiological input factors to detect twenty two human emotions. This paper presents an optimization method to reduce the number of input factors required to detect a set of emotions. The proposed method utilizes twelve optimization procedures (cases) each one has unique error values, and different input factors. Optimization is sought to reduce the cost and complexity of implementing human emotion detection systems. A performance measure index is used to evaluate the effectiveness of the proposed model. This study shows that using less than half of the factors (6-8 factors) is the most cost effective set of input parameters for the human emotions detection system

A Novel Neuro-Fuzzy Model to Detect Human Emotions Using Different Set of Vital Factors with Performance Index Measure

A novel optimization algorithm is proposed for detecting human emotions(responses) using artificial intelligence techniques such as exhaustive search, fuzzy logic and neural networks. Previous models for detecting human emotions have used fourteen measurable physical and physiological input factors to detect twenty two human emotions. This paper presents an optimization method to reduce the number of input factors required to detect a set of emotions. The proposed method utilizes twelve optimization procedures (cases) each one has unique error values, and different input factors. Optimization is sought to reduce the cost and complexity of implementing human emotion detection systems. A performance measure index is used to evaluate the effectiveness of the proposed model. This study shows that using less than half of the factors (6-8 factors) is the most cost effective set of input parameters for the human emotions detection system

Energy-efficient clock generation for communication and computing systems using injection locking

The design of high-performance, high-speed clock generation and distribution becomes challenging in terms of phase noise, jitter and power consumption, due to the fast development of communication and computing systems. Injection locking is a promising clocking technique since it can significantly improve the energy efficiency, suppress the phase noise of the ring oscillator, enable a fast startup and conveniently generate multiple time-interleaved phases. A quasi-linear model of injection-locked ring oscillator (ILRO) is utilized to mathematically formulate the frequency and time domain characteristics of the system, as well as the phase noise shaping and jitter tracking behavior. The settling behavior of ILRO is also exploited and shows a strong dependence on the locking range and the initial phase difference of the injected and the resultant oscillation signals. A forwarded-clock synchronization based on injection locking is designed for a 10 Gb/s photonic interconnect according to the specific features of optical links. A single clock recovery can be used for all the four channels, resulting in a large amount of power and area saving. The applications of sub-harmonic and super-harmonic injection locking in wireless communications for frequency multiplying and division are also discussed

Ensemble approach on enhanced compressed noise EEG data signal in wireless body area sensor network

The Wireless Body Area Sensor Network (WBASN) is used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements. One of the important applications of WBASN is patients’ healthcare monitoring of chronic diseases such as epileptic seizure. Normally, epileptic seizure data of the electroencephalograph (EEG) is captured and compressed in order to reduce its transmission time. However, at the same time, this contaminates the overall data and lowers classification accuracy. The current work also did not take into consideration that large size of collected EEG data. Consequently, EEG data is a bandwidth intensive. Hence, the main goal of this work is to design a unified compression and classification framework for delivery of EEG data in order to address its large size issue. EEG data is compressed in order to reduce its transmission time. However, at the same time, noise at the receiver side contaminates the overall data and lowers classification accuracy. Another goal is to reconstruct the compressed data and then recognize it. Therefore, a Noise Signal Combination (NSC) technique is proposed for the compression of the transmitted EEG data and enhancement of its classification accuracy at the receiving side in the presence of noise and incomplete data. The proposed framework combines compressive sensing and discrete cosine transform (DCT) in order to reduce the size of transmission data. Moreover, Gaussian noise model of the transmission channel is practically implemented to the framework. At the receiving side, the proposed NSC is designed based on weighted voting using four classification techniques. The accuracy of these techniques namely Artificial Neural Network, Naïve Bayes, k-Nearest Neighbour, and Support Victor Machine classifiers is fed to the proposed NSC. The experimental results showed that the proposed technique exceeds the conventional techniques by achieving the highest accuracy for noiseless and noisy data. Furthermore, the framework performs a significant role in reducing the size of data and classifying both noisy and noiseless data. The key contributions are the unified framework and proposed NSC, which improved accuracy of the noiseless and noisy EGG large data. The results have demonstrated the effectiveness of the proposed framework and provided several credible benefits including simplicity, and accuracy enhancement. Finally, the research improves clinical information about patients who not only suffer from epilepsy, but also neurological disorders, mental or physiological problems