Body Sensor Networks

Their purpose is to monitor patient 's physiological data (or BSN data) values. Sensors continuously monitor and collect patient's data and send it to a remote server through a network. This server can be called Database Server (DBS). DBS collect and stores the received patient's medical data which can be later used for any medical emergency by the Healthcare provider. Further, patient's data may be used to educate medical students, to provide data for medical research and analysis. Since the patient's physiological data are highly sensitive and BSN is very susceptible to attacks, therefore, it must be ensured that patient identity should not be exposed and altered as well as patient's data should not fall into hands of unauthorized users. Hence, maintaining privacy of patient's data over the network is an important aspect. So communication between BSN and DBS has to be secure. A strong security mechanism should be applied to maintain patient's privacy and confidentiality. This paper proposes information security of physiological data which flow through network which is highly susceptible to attack and unauthorized access. Paper proposing physiological value based Encryption and Mutual Authentication (PVEMA) mechanism to enable mutual authentication and data encryption for a patient's physiological data. The work used practical approach of Elliptic Curve Cryptography (ECC), Message Authentication Code and Symmetric Encryption Scheme for maintaining the confidentiality, authenticity and integrity of patient's data through previous stored physiological value.

we propose a computing framework for mobile healthcare emergency with Smartphone resources including computing power and energy can be opportunistically gathered to process the computing intensive personal health information. During mobile healthcare emergency with minimal disclosure with the pervasiveness of wireless body sensor network (BSN). Mobile healthcare extends the operation of healthcare provider in to pervasive environment for better health monitoring of diabetes and asthma has attracted considerable interest recently. In specific PHI privacy disclosure and the high reliability of PHI process and transmission in m-health emergency Mobile healthcare is the integration of mobile computing and health monitoring. It is the application of mobile computing technologies for improving communication among patients. As mobile devices have become an inseparable part our life it can integrate healthcare more to our everyday life. It enables the delivery of accurate medical informatio...

The constant growth of the elderly population in the world creates new challenges and opportunities in health care systems. New technological solutions have to be found in order to meet the needs and demands of our aging society. The welfare and quality of life of the elderly population must be a priority. Continuous physical activity will play an important role, due to the increase of the retirement age. However, physiotherapy can be expensive, even when the desire movements are autonomous and simple, also requires people to move to rehabilitation centres. Within this context, this paper describes the development and preliminary tests of a wireless sensor network, based on wearable inertial and magnetic sensors, applied to the capture of human motion. This will enable a personalized home-based rehabilitation system for the elderly or people in remote physical locations.

Nowadays, the technology advancements of sensors, low power mixed-signal/RF circuits, wireless communication and Wireless Sensor Networks (WSNs) have enabled the design of compact, low power, high performance and low cost solutions for a wide range of applications, surveillance, building monitoring, sports/fitness, and of particular interesting are applications for health care. Novel sensors for human biomedical signal together with wireless connectivity and low power solutions are creating new opportunities for wearable devices which allow continuous monitoring together with freedom of movement of the users. This paper presents a low-power wearable sensor networks platform for on-body physiological measurements and wireless data communications. The platform hosts novel high sensitivity electric potential dry surface sensors that can be used in either contact or non-contact mode to measure ECG and EMG signals. Heart rate and respiration rate is performed runtime directly on the node. This approach reduces the amount of data than need to be transmitted, from raw measurement to analyzed data. In doing so the duty cycle of the radio has been reduced and the power consumption of the node optimized. Experimental measurements show the acquisition and processing of data from sensors and the low power consumption achieved with the node in different modalities.

It is quite often that we hear fatal traffic accidents due to driver's distraction. Car manufactures and researchers are therefore putting their efforts into car safety protection mechanism. However, the application of car safety protection mechanism is frequently hindered by its limitations, such as drivers' privacy or the high cost of its deployment and each of which leads to rare applications of car safety protection specifically in the field of nonautonomous cognitive distraction. This research proposal intends to apply the sensor of Galvanic Skin Response (GSR) to measure drivers' non-autonomous cognitive distraction due to the blood glucose variation of diabetes. SVM-RFE will be adopted as the major algorithm to create an alert mechanism with the artificial intelligence concept of supervised machine learning. The researched humanmachine sense interaction mechanism can be able to embed into the car computer so that it can detect drivers' physiological changes during diabetes outbreak and then raise advisable alert and intervention accordingly.

Staff shortages and an increasingly aging population are straining the ability of emergency departments to provide high quality care. At the same time, there is a growing concern about the hospitals' ability to provide effective care during disaster events. For these reasons, tools that automate patient monitoring have the potential to greatly improve efficiency and quality of health care. Towards this goal, we have developed MEDiSN, a wireless sensor network for monitoring patients' physiological data in hospitals and during disaster events. MEDiSN comprises Physiological Monitors (PMs) which are custom-built, patient-worn motes that sample, encrypt, and sign physiological data and Relay Points (RPs) that self-organize into a multi-hop wireless backbone for carrying physiological data. Moreover, MEDiSN includes a back-end server that persistently stores medical data and presents them to authenticated GUI clients. The combination of MEDiSN's two-tier architecture and optimized rate control protocols allows it to address the compound challenge of reliably delivering large volumes of data while meeting the application's QoS requirements. Results from extensive simulations, testbed experiments, and multiple pilot hospital deployments show that MEDiSN can scale from tens to at least five hundred PMs, effectively protect application packets from congestive and corruptive losses, and deliver medically actionable data.

The shortcoming of conventional bus architecture has been overcome by adopting Network on Chip (NoC) in multi core System on Chip (SoC). Many works have been anticipated for NoC, based on network topology, switching methodology and routing techniques. This paper proposes a Modified TACIT algorithm for secure routing in NoC architectures. The key generation is based on four types of HASH function (4H-key) that find hard hitting to the intruder. The algorithm is realized under various block sizes as the 8-bit, 64-bit and 128-bit. The device utilization of the proposed algorithm is determined and the results show that, the maximum frequency achieved by the proposed algorithm (for 8-bit block size) is 640.968 MHz with the total memory usage of 78045 Kb.

Resumen. El incremento en el uso de dispositivos móviles ha generado un crecimiento de puntos de acceso, públicos y privados, esto es aprovechado por los sistemas mHealth, los cuales utilizan la infraestructura de los servicios móviles para brindar servicios de salud, realizando aplicaciones como el monitoreo de señales biológicas. Una de estas aplicaciones puede ser la medición de la señal de electroencefalografía (EEG), la cual consiste en adquirir la señal eléctrica de la corteza cerebral de forma no invasiva. Este artículo propone un sistema capaz de obtener múltiples registros por medio de módulos con 19 sensores de señal EEG y un envío de datos usando el protocolo Wi-Fi hacia un servidor por medio de socket TCP. El sistema desarrollado permite la realización de hasta 12 estudios en forma paralela y de manera remota, como puede ser el hogar del paciente, una clínica rural, etc., con una eficiencia del 99.21% de datos trasmitidos correctamente.

For decades, the wireless medical sensor network (WMSN) has shown great prospective in refining health care quality. The vast ranges of medical applications that lead to computer-assisted treatment from all over the health monitoring are based on WMSNs technology. These applications introduce emergency medical response systems with its numerous advantages and facilities. However, these technologies have privacy and security challenges that need to be analyzed to make it preferable and socially acceptable. This survey paper depicts the current state-of-the-art wireless sensor network (WSN) technologies being used in medical applications. In this paper, we have focused on system architecture, routing, security, and privacy issues with various medical applications from various research work.

The large increase in Australia's aging population promises to be major economic and social issue for local, state and federal Government bodies. The healthcare budget is expected to increase dramatically and the burden on healthcare services will require not only a massive injection of capital funds but an increase in qualified care givers. The authors believe that assistive healthcare monitoring is one viable and cost-effective solution to alleviate the burden of the healthcare system today and in the future. The authors in this paper describe their third Wireless Sensor Network (WSN) prototype called MoteCare and anticipate the implementation of the next, more advanced prototype. Details of the improvements are covered followed by an objective system evaluation and conclusion.

In this paper the authors describe the implementation and validation of a prototype of an environmental and health monitoring system based on a Wireless Sensor Network (WSN). The solution proposed for our system combines environmental and medical sensors in order to monitor both the surrounding area of the patient and the patient's health status simultaneously. This feature would allow a comprehensive understanding of the patient's condition by the specialist caring for the subject. Another key feature of the system is the development of an architecture which provides an easy, viable, cheap and effective way for connecting our environmental and medical sensor network of MicaZ motes to the outside world using Simple Network Management Protocol (SNMP) version 3. A series of experimental scenarios were developed and implemented in a laboratory setting; firstly for evaluating the reactivity of the monitoring system to changes and secondly for understanding the reliability of the data obtained for benchmarking purposes. The conclusion considers the implementation of future improvements to the health monitoring network by introducing new sensors and location tracking capabilities, and by integrating alarm triggering algorithms and advanced security techniques.

Bomb disposal manned missions are inherently safetycritical. Wireless Sensor Network (WSN) technology potentially offers an opportunity to increase the safety of the operatives involved in such missions through detailed physiological parameters monitoring and fusion of "health" information. Wearing heavy armour during bomb disposal manned missions may have side effects that, due to reduction of the body's ability to regulate core temperature in the enclosed environment of the suit, lead to uncompensable heat stress and thus impair the technician's physical or mental ability. Experimental trials have shown no obvious relationship between temperature of any single skin site and the core temperature nor between single point temperature and subjective thermal sensation (usually associated with comfort). Also, core temperature alone may not yield indicators of danger sufficiently early. This paper proposes to integrate a body network of temperature sensors into the bomb disposal suit. The paper describes an in-network sensor data fusion and modelling approach that estimates the overall thermal sensation for the suit wearer, in real time, based on the multi-point temperature data. The case is made for performing the modelling in-network on the basis of reducing communications with the remote mission control point and to better support actuation of an in-suit cooling system. It is also argued that thermal sensation indicators are more useful to present at an on-line remote monitoring station than individual temperatures. The appropriateness of the developed Body Sensor Network (BSN) application is supported by experimental validation.

Revolutionary networking concepts and unprecedented mix of technical challenges have made Wireless Sensor Networks (WSN) one of the major research trends of the 21th century. However, only very recently did such systems start to appear as products on the market. In this new context, this paper aims at providing a general status on WSNs, with a special focus on the device aspects, independent of application peculiarities. We first investigate why, despite years of research and development and technical maturity, WSN products and solutions are yet neither fully adopted nor widely deployed. We then review the state-of-theart technologies that build most of today’s available products and finally briefly discuss some advanced techniques for future generations of WSN systems.

Ambient-assisted living (AAL) is promising to become a supplement of the current care models, providing enhanced living experience to people within context-aware homes and smart environments. Activity recognition based on sensory data in AAL systems is an important task because 1) it can be used for estimation of levels of physical activity, 2) it can lead to detecting changes of daily patterns that may indicate an emerging medical condition, or 3) it can be used for detection of accidents and emergencies. To be accepted, AAL systems must be affordable while providing reliable performance. These two factors hugely depend on optimizing the number of utilized sensors and extracting robust features from them. This paper proposes a generic feature engineering method for selecting robust features from a variety of sensors, which can be used for generating reliable classification models. From the originally recorded time series and some newly generated time series [i.e., magnitudes, first derivatives, delta series, and fast Fourier transformation (FFT)-based series], a variety of time and frequency domain features are extracted. Then, using two-phase feature selection, the number of generated features is greatly reduced. Finally, different classification models are trained and evaluated on an independent test set. The proposed method was evaluated on five publicly available data sets, and on all of them, it yielded better accuracy than when using hand-tailored features. The benefits of the proposed systematic feature engineering method are quickly discovering good feature sets for any given task than manually finding ones suitable for a particular task, selecting a small feature set that outperforms manually determined features in both execution time and accuracy, and identification of relevant sensor types and body locations automatically. Ultimately, the proposed method could reduce the cost of AAL systems by facilitating execution of algorithms on devices with limited resources and by using as few sensors as possible.

Abstract: In modern concepts for development of Personal health systems (PHS) one of the main directions in research is the employment of mobile communications, Internet and Web technologies. On the other hand, the advances in bio-sensors technologies and applications enable continuous efficient monitoring of some physiological parameters of a patient. In this paper the general model of development of a PHS ’ architecture, consisting of three main components – Body sensor network, Patient server and Point-of-care server is described. The paper presents an overview of current stage in possible architectures for realization of PHS, discussing various models, strategies and specifics. It is focused on discussions that summarize the trends for realization of Web-based multi-tier PHS. An example for implementation of wireless intelligent bio-sensor for monitoring of heart and respiration rates is presented.

and more specifically in wireless networks and mobile computing have driven new directions in the development of e-Health sector. New emerging concepts like mobile health (m-Health) and Personal Health Systems (PHS) are expected to revolutionize the way healthcare services are delivered. In the paper a description on the specifics of the PHS's building blocks is presented. An example multi-tiered architectural model for realization of PHS is included and a description of characteristics and functions of the tiers is suggested.

Paper presents a Web-based Personal Health System (PHS) for remote monitoring of ECG signals. It presents a multi-tiered architecture for building PHS and integrating them with enterprise and health information systems. Tiers encompass Body Area Network (BAN) with medical sensors, Personal Server and/or Home gateway, Medical servers and Web Portal. A use case scenario for remote ECG monitoring is described in the paper for proving the effectiveness of the proposed architecture. The uploaded ECG data can be viewed and analyzed remotely by the specialists. The patient (at patient site) also can log in the medical portal and can upload data file with its ECG.

The future healthcare systems' growth and development have greater dependence on Wireless Body Area Network (WBAN). Notwithstanding, the utilization of untrustworthy methods has made it defenseless against various attacks. These attacks can be kept under check by utilizing different security procedures. For this reason a security protocol named Se-Small is utilized. The procedure includes signing the primary packet by the base station before it is transmitted. Subsequently this sign is transmitted to the rest of the data item from which hash value is produced. This procedure reduces overhead and transmission delay in the system. The wireless network essentially utilizes three distinct structures for packet transmission. They incorporate -Tree, Star and Linear chain. This paper uncovers the utilization of flawlessly displayed and distributed structure where the three essential structures have been assembled for productive transmission of packets in WBAN. For this reason Fish-Bone structure is utilized. Failure in any of the cluster head has likewise been compensated by giving a substitute component. Mobile node is utilized here to gather the information from master heads. For proficient transmission buffer length of mobile node is kept high.

This paper investigates the utility of unsupervised machine learning and data visualisation for tracking changes in user activity over time. This is done through analysing unlabelled data generated from passive and ambient smart home sensors, such as motion sensors, which are considered less intrusive than video cameras or wearables. The challenge in using unlabelled passive and ambient sensors data for activity recognition is to find practical methods that can provide meaningful information to support timely interventions based on changing user needs, without the overhead of having to label the data over long periods of time. The paper addresses this challenge to discover patterns in unlabelled sensor data using kernel density estimation (KDE) for pre-processing the data, together with t-distributed stochastic neighbour embedding and uniform manifold approximation and projection for visualising changes. The methodology is developed and tested on the Aruba CASAS smart home dataset a...

As because of modern emerging technologies, low power integrated circuits and wireless communication has enabled a new generation of sensors network. The incorporation of these sensors networks in Health care is very popular and plays a vital role in breath breaking situations. The deployment of monitoring hardware incorporated with various wireless standards plays a key role in regard to interoperability, invasion privacy, sensors validation data consistency and interference related issues. The goal of our paper is to make a comparative study in realm of modern wireless trends such as Bluetooth, Wi-fi, Zigbee and Wibree and related facets.

Obstructive sleep apnea (OSA) is a serious sleep disorder which is characterized by frequent obstruction of the upper airway, often resulting in oxygen desaturation. The serious negative impact of OSA on human health makes monitoring and diagnosing it a necessity. Currently, polysomnography is considered the gold standard for diagnosing OSA, which requires an expensive attended overnight stay at a hospital with considerable wiring between the human body and the system. In this paper, we implement a reliable, comfortable, inexpensive, and easily available portable device that allows users to apply the OSA test at home without the need for attended overnight tests. The design takes advantage of a smatrphone's built-in sensors, pervasiveness, computational capabilities, and user-friendly interface to screen OSA. We use three main sensors to extract physiological signals from patients which are (1) an oximeter to measure the oxygen level, (2) a microphone to record the respiratory effort, and (3) an accelerometer to detect the body's movement. Finally, we examine our system's ability to screen the disease as compared to the gold standard by testing it on 15 samples. The results showed that 100% of patients were correctly identified as having the disease, and 85.7% of patients were correctly identified as not having the disease. These preliminary results demonstrate the effectiveness of the developed system when compared to the gold standard and emphasize the important role of smartphones in healthcare.

Improving the safety of the traffic is a social interest. Accidents are not only caused by poor technical conditions of the vehicles, but also by tired, indisposed, or bad state-of-minded drivers. The managing of human factors needs the control, recording and monitoring of the most important vital parameters of the driver. The paper presents such a system, which is based on ECG recording and needs no or little cooperation of the driver.

A WBAN is predicted as a valuable technology being used in many applications, especially in medical application for monitoring and detection of possible problems of patients. Some of the issues are addressed in WBANs, in which the critical issues need to be solved are energy saving and security along with inter-WBANs interference. To evade the inter-WBANs interference along with the control the transmission power on WBAN system, this paper presents a cooperative based power control game algorithm which is utilized with the social interaction information model. In order to manage the WBAN with the security measures to transmit the patient data throughout the system an Elliptic Curve Cryptography (ECC) algorithm is used for authentication before initiating the transmission. The performance of the proposed approach is evaluated along with the increased number of nodes while the occurrence of an interference problem is also rigorous. The proposed approach provides the better results whe...

Wireless Sensor Networks (WSN) consists of many small sensor nodes that utilizes the sensors to collect sensor data and sends the data to the base station. Security in WSN has been a subject well researched for last few years and still many related researches are going on. Authentication with reconfiguration capability is a very important trait for the security of a network. The overall objective of this research is to develop a wireless sensor network which is reconfigurable and secure. I propose a reconfigurable authentication mechanism with on-line key changing capability using encryption. I discuss the types of attacks the proposed authentication can prevent and describe step by step implementation of this proposal in hardware. I compare AES with RC4 algorithm in my network setup and show how they react to RSSI, Latency and Throughput in variable parameters.

We present a framework for the automated generation of power-efficient state detection in wearable sensor nodes. The core of the framework is a decision tree classifier, which dynamically adjusts the activation and sampling rate of the sensors (termed groggy wakeup), such that only the data necessary to determine the system state is collected at any given time. This classifier can be tuned to trade-off accuracy and power in a structured fashion. Use of a sensor set which measures the phenomena of interest in multiple fashions and with various accuracies further improves the savings by increasing the possible choices for the above decision process. An application based on a wearable gait monitor provides quantitative results. Comparing the decision tree classifier to a Support Vector Machine, it is shown that groggy wakeup allows the system to achieve the same detection accuracy for less average power. A simulation of real-time operation demonstrates that our multi-tiered system detects states as accurately as a single-trigger (binary) wakeup system, drawing substantially less power with only a negligible increase in latency.

This paper presents a framework for power-efficient detection in embedded sensor systems. State detection is structured as a decision tree classifier that dynamically orders the activation and adjusts the sampling rate of the sensors (termed groggy wakeup), such that only the data necessary to determine the system state is collected at any given time. This classifier can be tuned to trade-off accuracy and power in a structured, parameterized fashion. An embedded instantiation of these classifiers, including real-time sensor control, is described. An application based on a wearable gait monitor provides quantitative support for this framework. The decision tree classifiers achieved roughly identical detection accuracies to those obtained using support vector machines while drawing three times less power. Both simulation and real-time operation of the classifiers demonstrate that our multi-tiered classifier determines states as accurately as a single-trigger (binary) wakeup system while drawing as little as half as much power and with only a negligible increase in latency.

Wireless body area Networks is used to measure the ambient conditions related to the environment surrounding the sensors and transforms them into an electrical signal, which is transmitted to the sink and then processed further. The sensor nodes are constrained by energy supply and bandwidth and also there is a loss of data packets or intrusion of the data packets in the network. Various techniques have been proposed to improve the energy consumption rate, such as clustering, efficient routing, and data aggregation. In this paper, the clustering technique is used to reduce the energy and also Code Block Binding algorithm is used to secure the data that is sent to the sink for processing. The simulation results proved that the energy cost using the clustering based TDMA are reduced with security mechanism and the network lifetime is increased. Also, the CBB-TDMA algorithm is used which proved the PDR is 97% when compared to the other protocols which resulted only in 61% to 92%.

Falls are dangerous for the aged population as they can adversely affect health. Therefore, many fall detection systems have been developed. However, prevalent methods only use accelerometers to isolate falls from activities of daily living (ADL). This makes it difficult to distinguish real falls from certain fall-like activities such as sitting down quickly and jumping, resulting in many false positives. Body orientation is also used as a means of detecting falls, but it is not very useful when the ending position is not horizontal, e.g. falls happen on stairs. In this paper we present a novel fall detection system using both accelerometers and gyroscopes. We divide human activities into two categories: static postures and dynamic transitions. By using two tri-axial accelerometers at separate body locations, our system can recognize four kinds of static postures: standing, bending, sitting, and lying. Motions between these static postures are considered as dynamic transitions. Linear acceleration and angular velocity are measured to determine whether motion transitions are intentional. If the transition before a lying posture is not intentional, a fall event is detected. Our algorithm, coupled with accelerometers and gyroscopes, reduces both false positives and false negatives, while improving fall detection accuracy. In addition, our solution features low computational cost and real-time response.

Falls are dangerous for the aged population as they can adversely affect health. Therefore, many fall detection systems have been developed. However, prevalent methods only use accelerometers to isolate falls from activities of daily living (ADL). This makes it difficult to distinguish real falls from certain fall-like activities such as sitting down quickly and jumping, resulting in many false positives. Body orientation is also used as a means of detecting falls, but it is not very useful when the ending position is not horizontal, e.g. falls happen on stairs. In this paper we present a novel fall detection system using both accelerometers and gyroscopes. We divide human activities into two categories: static postures and dynamic transitions. By using two tri-axial accelerometers at separate body locations, our system can recognize four kinds of static postures: standing, bending, sitting, and lying. Motions between these static postures are considered as dynamic transitions. Linear acceleration and angular velocity are measured to determine whether motion transitions are intentional. If the transition before a lying posture is not intentional, a fall event is detected. Our algorithm, coupled with accelerometers and gyroscopes, reduces both false positives and false negatives, while improving fall detection accuracy. In addition, our solution features low computational cost and real-time response.

Many vehicle accidents were recorded because of the drowsiness or emotional instability of the drivers. There are many fatal cases and minor cases are being recorded every day and these numbers are on a gradual increase. Some vehicle manufacturers design several safety measures such as air bags, powerful roll cages, automated braking systems to mitigate the damage of the accidents. But always prevention is better than cure. There are occasions which accidents occur due to the malfunction of the vehicle, but most of the accidents are because of the human errors done by the driver. And these human errors can be happened by the driver inside the vehicle or any driver who is driving on the road. There can be a chance of preventing such accidents if the danger could be communicated earlier to the driver. Therefore, not only detecting the current state of the driver but also communicate such a situation to nearby vehicles is very important. The proposed solution of this report is a hardware-based system which explore the ability to detect the drowsiness of the driver using image processing technologies to prevent vehicle accidents by alarming the driver and as well as communicating the danger to the nearby vehicle using light fidelity technology.

This paper presents the fabrication, characterization and the performance of a wearable, robust, flexible and disposable barcode system based on novel ionic liquid polymer gels (ionogels) for monitoring in real time mode the pH of the sweat generated during an exercise period. Up to now sweat analysis has been carried out using awkward methods of collecting sweat followed by laboratory analysis. The approach presented here can provide immediate feedback regarding sweat composition. The great advantage of sweat analysis is the fact that it is a completely non-invasive means of analyzing the wearer's physiological state and ensuring their health and wellbeing.

A frequent cause of auto accidents is disregarding the proximal traffic of an ego-vehicle during lane changing. Presumably, in a split-second-decision situation we may prevent an accident by predicting the intention of a driver before her action onset using the neural signals data, meanwhile building the perception of surroundings of a vehicle using optical sensors. The prediction of an intended action fused with the perception can generate an instantaneous signal that may replenish the driver's ignorance about the surroundings. This study examines electromyography (EMG) signals to predict intention of a driver along perception building stack of an autonomous driving system (ADS) in building an advanced driving assistant system (ADAS). EMG are classified into left-turn and rightturn intended actions and lanes and object detection with camera and Lidar are used to detect vehicles approaching from behind. A warning issued before the action onset, can alert a driver and may save her from a fatal accident. The use of neural signals for intended action prediction is a novel addition to camera, radar and Lidar based ADAS systems. Furthermore, the study demonstrates efficacy of the proposed idea with experiments designed to classify online and offline EMG data in real-world settings with computation time and the latency of communicated warnings.

Mobile e-health is currently a develop technology where people nowadays can connect to the healthcare centre through medical body area network sensors and mobile devices. Since the data transmitted through the mobile e-health system are extremely sensitive and private, a strong authentication needs to be implemented in the network system. Due to high demand in e-health monitoring system, e-health authentication has become the main concern in the network system since it will transmit patient data to the healthcare server. In addition, the e-health server will store all of the patient vital signs for further analysis by the healthcare personnel. This paper presents a secure and strong identity based authentication protocol for mobile e-health monitoring system using IBE_Trust algorithm and Pulse_Sensor mobile application that will eliminate a lot of security weaknesses in mobile e-health. Through security analysis, we are able to demonstrate that this authentication scheme is more secure and efficient compared to the current e-health authentication protocol.

The development of systems capable of recognizing and categorising emotions is of interest to researchers in various scientific areas including artificial intelligence. The traditional notion that emotions and rationality are two separate realms has gradually been challenged. The work of neurologists has shown the strong relationship between emotional episodes and the way humans think and act. Furthermore, emotions not only regulate human decisions but could also contribute to a more satisfactory response to the environment, i.e., faster and more precise actions. In this paper an analysis of physiological signals employed in real-time emotion detection is presented in the context of Intelligent Inhabited Environments (IIE). Two studies were performed to investigate whether physical exertion has a significant effect on bodily signals stemming from emotional episodes with subjects having various degrees of affect intensity: 1) a statistical analysis using the Wilcoxon Test, and 2) a cluster analysis using the Davies-Bouldin Index. Preliminary results demonstrated that the heart rate and skin resistance consistently showed similar changes regardless of the physical stimuli while blood volume pressure did not show a significant change. It was also found that neither physical stress nor affect intensity played a role in the separation of neutral and non-neutral emotional states.

Intra-body communication (IBC) is a novel key research area that will foster personalized medicine by allowing in situ and real time monitoring in daily life. In this work, the energy efficient galvanic coupling (GC) technology is used to send data through intra-body links. A novel sound card-based GC testbed is designed and implemented, whose main features are: (i) low equipment requirements since it only employs two ordinary PCs with sound card support and Matlab software, (ii) high flexibility since all the parameters setting may be easily modified through the PC control panel and Matlab programs, (iii) real time physiological data transmissions, and (iv) almost error free communication by developing specific physical (PHY) layer techniques, which are implemented and tested with a real chicken tissue in the experimental evaluation. A signal to noise ratio (SNR) calculation is also proposed with the twofold purpose to be used for frequency offset compensation and as metric to evaluate the proposed architecture. The developed GC testbed may be easily replicated by the interested research community to carry out simulation-based experiments, thus fostering new research in this field. Moreover, the Matlab source code of the proposed GC transceiver is freely available online on Code Ocean.

To drive safely, the driver must be aware of the surroundings, pay attention to the road traffic, and be ready to adapt to new circumstances. Most studies on driving safety focus on detecting anomalies in driver behavior and monitoring the cognitive capabilities of drivers. In our study, we proposed a classifier for basic activities in driving a car, based on a similar approach that could be applied to the recognition of basic activities in daily life, that is, using electrooculographic (EOG) signals and a one-dimensional convolutional neural network (1D CNN). Our classifier achieved an accuracy of 80% for the 16 primary and secondary activities. The accuracy related to primary activities in driving, including crossroad, parking, roundabout was 97.9%, 96.8%, 97.4%, and 99.5%, respectively. The F1 score for secondary driving actions (0.99) was higher than for primary driving activities (0.93–0.94). Furthermore, using the same algorithm, it was possible to distinguish four secondary a...

Technology has revolutionized medical practices by enabling more convenient and non-intrusive monitoring of patient's health, leading to next generation ubiquitous healthcare (u-healthcare). The exploitation of the Internet protocol version 6 addressing space along with the miniaturization of electronic devices has fostered providing interoperability and connectivity of wearable sensor devices in wireless body area networks to the Internet of Things. In this paper, we propose to integrate the IPv6 over low power wireless personal area network (6LoWPAN) to the u-healthcare monitoring system architecture. The main objective is to study the feasibility of the 6LoWPAN-enabled platform in real-world scenarios dealing with medical data. The performance evaluation of this platform is carried out initially through simulations using OMNet++ and then supported by an experimental study using sensor motes and a customized micro-computing unit. Performance metrics such as throughput, end-to-end delay, packet error rate, and energy consumption are investigated under acute health conditions, where patient's health information has to be sent continuously and at maximum rate to the care provider. The obtained results show that the proposed 6LoWPAN solution fulfills the main quality of service requirements of u-healthcare applications.

1.1 Introduction 1.2 An overview of healthcare systems 1.3 Ambient assisted living and enhanced living environments 1.4 Conclusions Acknowledgements References Biographies 2 Enhanced living environments from the viewpoint of socioecological psychology Tamás Martos and Viola Sallay Abstract 2.1 Introduction 2.2 Socioecological psychology as a framework 2.3 Concept of 'human niche construction' as a universal human phenomenon 2.4 Home as a niche 2.4.1 Emotional processes in the home: lessons learnt with the Emotional Map of the Home Interview 2.5 Legacy of Self-determination theory 2.5.1 Core concepts of SDT 2.6 Putting it all together: socioecological psychological aspects of ELE 2.6.1 ELE as niche construction 2.6.2 ELE and home niches: the potential places of technology in a complex system 2.6.3 Constructing ELE solutions in home niches: the importance of psychological need support 2.7 Conclusions Acknowledgements References Further reading Biographies

Recent advances in wireless communication technologies and "MEMS" (micro-electromechanical systems) (Arshak et al., 2005) fields have enabled the development of microcomponents that incorporate sensing devices and wireless communications into a single miniature circuit, with a reasonable cost. These

The wireless body area network (WBAN) is a branch of the wireless sensor network (WSN) intended for tracking essential patients’ physiological signals and transferring this knowledge to the coordinator. During the routing of data, WBANs encounter critical routing problems like WSNs. Moreover, the particular constraints of WBANs make it more challenging that they need to be addressed. This survey article categorizes and briefly analyzes a range of current routing methods utilized in WBANs. The routing protocol is essential to the creation of any efficient and reliable wireless body area network due to a limited size of battery in the body sensor nodes. A comparison study of numerous routing protocols has been made in this paper, which is helpful in selecting the optimal routing protocol for the application being targeted. The article describes the WBAN architecture and addresses numerous challenges in the context of successful WBAN routing. In this paper, several existing WBAN routin...

Driving distraction is a topic of great interest in the transport safety-research community, because it is now a primary cause of road accidents. A recent report has revealed that distraction is more alarming than previously thought, and a suitable measurement to effectively detect distraction is required. Most agree that driving distraction actually comprises the simultaneous interaction of two or more types of distraction. The purpose of this paper is, therefore, to determine the promising method for measuring visual cognitive distraction. We discuss the five common measurement methods for visual and cognitive driving distraction, which include driving performance, driver physical measures, driver biological measures, subjective reports, and hybrid measures. Hybrid measurement of driver's physical measures (e.g., eye movement) and driver's biological measures (e.g., electroencephalogram) is better than other methods at detecting types of visual cognitive distraction. This new perspective on measurement methods will help the field of transport safety to determine the best means of detecting and measuring the effect of visual cognitive distraction.

The efficiency of sensor networks strongly depends on the routing protocol used. In this paper, we analyze three different types of routing protocols: LEACH, PEGASIS, and VGA. Sensor networks are simulated using Sensoria simulator. Several simulations are conducted to analyze the performance of these protocols including the power consumption and overall network performance. The simulation results, using same limited sensing range value, show that PEGASIS outperforms all other protocols while LEACH has better performance than VGA. Furthermore, the paper investigates the power consumption for all protocols. On the average, VGA has the worst power consumption when the sensing range is limited, while VGA is the best when the sensing range is increased.

In the struggle against population aging, chronic diseases, and a lack of medical facilities, the emergence of Wireless Body Area Networks (WBANs) technology has ushered in optimism. WBANs use a variety of wearable and implanted biosensor nodes to constantly monitor physiological parameters such as oxygen saturation (SpO2), electrocardiogram (ECG), electromyography (EMG), electroencephalogram (EEG), blood pressure, respiration rate, body temperature, and pulse rate. Importantly, these vital signs are communicated to a doctor over a public network, who can diagnose ailments remotely and efficiently. Among these communications, the security and privacy of patients are the prime concerns while transferring data over an open wireless channel from biosensor nodes to a Medical Server (MS) through a Base Station (BS) for efficient medical diagnosis. Finding an effective security strategy for patients which rely on WBANs to monitor their health information is a huge challenge due to the con...

Telehealth systems benefit from the rapid growth of mobile communication technology for measuring physiological signals. Development and validation of a tablet PC enabled noninvasive body sensor system for rural telehealth application are discussed in this paper. This system includes real time continuous collection of physiological parameters (blood pressure, pulse rate, and temperature) and fall detection of a patient with the help of a body sensor unit and wireless transmission of the acquired information to a tablet PC handled by the medical staff in a Primary Health Center (PHC). Abnormal conditions are automatically identified and alert messages are given to the medical officer in real time. Clinical validation is performed in a real environment and found to be successful. Bland-Altman analysis is carried out to validate the wrist blood pressure sensor used. The system works well for all measurements.

Resumen. El incremento en el uso de dispositivos móviles ha generado un crecimiento de puntos de acceso, públicos y privados, esto es aprovechado por los sistemas mHealth, los cuales utilizan la infraestructura de los servicios móviles para brindar servicios de salud, realizando aplicaciones como el monitoreo de señales biológicas. Una de estas aplicaciones puede ser la medición de la señal de electroencefalografía (EEG), la cual consiste en adquirir la señal eléctrica de la corteza cerebral de forma no invasiva. Este artículo propone un sistema capaz de obtener múltiples registros por medio de módulos con 19 sensores de señal EEG y un envío de datos usando el protocolo Wi-Fi hacia un servidor por medio de socket TCP. El sistema desarrollado permite la realización de hasta 12 estudios en forma paralela y de manera remota, como puede ser el hogar del paciente, una clínica rural, etc., con una eficiencia del 99.21% de datos trasmitidos correctamente.

This paper presents the survey/review on various aspects of WBAN technologies that are being used in healthcare applications. Wireless Body Area Network (WBAN) is the network of sensors worn on the body, used to describe a network of devices connected to wireless communication system. In particular, we have inspected monitoring and sensing, system architectures and applications of Wireless Body Area Network for healthcare system. We conclude by discussions on open research issues, their potential solutions and future trends useful for applications in healthcare system. WBAN opens up a whole new field of sensor networking and intelligent technology for healthcare applications but the problem lies in varying specifications and operating platforms.