Medical Body Sensor Networks Research Articles

FAST AND RELIABLE PACKET TRANSMISSION MAC PROTOCOL FOR WIRELESS BODY SENSOR NETWORKS

Medical monitoring wireless body sensor networks (WBSNs) monitor the occurrence of medical abnormal conditions so that medical staff can diagnose and treat patients quickly and accurately. In this case, not only abnormal data but also related data may be needed for better judgment by medical staff. In this paper, we propose a MAC protocol that can quickly and reliably transmit not only abnormal data but also related data. The proposed FR-MAC protocol reduces the packet transmission delay and prolongs the network lifetime compared to existing similar MAC protocol.

Secure and Reliable ML-based Disease Detection for a Medical Wireless Body Sensor Networks

The recent development of the Internet of Things (IoT) has enabled a significant technology that aids quick healthcare solutions through the use of smart wearables sensors. Indeed, undesirable events and network threats can appear in any physiological recording in Wireless Body Sensor Networks (WBSN), leading to a misdiagnosis. These events and threats are recognizable by experienced medical staff, thereby it is necessary to identify them before making any diagnosis. In this paper, a secure and energy efficient approach is proposed. For disease detection, our research provide insight into several physiological signals, including the ElectroCardioGram (ECG), ElectroMyoGram (EMG), and Blood Pressure (BP), where the security is achieved by the application of the Advanced Encryption Symmetric (AES) and the Secure Hash Algorithm (SHA). Similarly, to obtain a reasonable range of reliability, a classification procedure based on supervised Machine Learning (ML) techniques is used. The simulation results proved the accuracy and sensitivity of the system by 97% and 92%, respectively by enhancing a high level of security. Moreover, a suitable prototype is developed for medical staff to ensure the applicability of our proposal.

Efficient data aggregation technique for medical wireless body sensor networks

AbstractA central issue in Wireless Body Sensor Networks (WBSNs) is the large amount of measurement data for monitoring vital parameters, which need to be continuously measured, immediately processed and timely transmitted. This requires a big storage space and computing effort leading to a high-power consumption. Reducing the amount of transmitted data contributes significantly to an extension of the sensor operation time. In this contribution, we focus exactly at this aspect. We propose a data aggregation method based on Artificial Neural Networks (ANN) combining multiple physiological signals, which are the ElectroCardioGram (ECG), ElectroMyoGram (EMG) and Blood Pressure (BP), in one signal before transmission. The simulation and implementation results reveal a reduction of energy consumption to 87.32 %, ensuring a high accuracy level (80.53 %) and a relatively execution time (48.47 ms).

Channel Modeling and Characterization for VLC-Based Medical Body Sensor Networks: Trends and Challenges

Optical Wireless Communication (OWC) refers to transmission in unguided propagation media through the use of optical carriers, i.e., visible, Infrared (IR), and Ultraviolet (UV) bands. In this paper, we focus on indoor Visible Light Communication (VLC)-based Medical Body Sensor Networks (MBSNs) which allow the Light Emitting Diodes (LEDs) to communicate between on-body sensors/subdermal implants and on-body central hubs/monitoring devices while also serving as a luminaire. Since the Quality-of-Service (QoS) of the communication systems depends heavily on realistic channel modeling and characterization, this paper aims at presenting an up-to-date survey of works on channel modeling activities for MBSNs. The first part reviews existing IR-based MBSNs channel models based on which VLC channel models are derived. The second part of this review provides details on existing VLC-based MBSNs channel models according to the mobility of the MBSNs on the patient’s body. We also present a realistic channel modeling approach called site-specific ray tracing that considers the skin tissue for the MBSNs channel modeling for realistic hospital scenarios.

Medical Healthcare System Based on Wireless Body Area Networks: The Importance of Anomaly Detection

In this research paper, a modern framework is presented to detect anomaly in medical wireless body sensor network systems that are incorporated in distant observation of patient’s vital signs. The suggested framework effects analysis of data in a sequential manner using a mini gateway utilized as a root station to discover abnormal alterations and to deal with inaccurate computations in gathered medical information minus advance awareness of irregular occurrences or consistent data patterns. The suggested perspective relies on Principal Component Analysis (PCA) utilized in spatial analysis and dimension reduction for gathered medical details. The key goal is distinguishing defective calculations from clinical dangers for reduction of false alarms prompted by incorrect computations or ill-behaved sensors. The result from the experiments on real medical datasets reveal that the suggested PCA perspective is able to attain good discovery perfection with lesser falt alarm rate when contrasted with other perspectives that fail to minimize the excessive dimension of gathered more information so in multivariate Wireless Body Sensor Networks (WBSN) implementations, and dynamic streaming nature of sensor data, mostly in medical implementations.

Performance evaluation of energy efficient routing with or without relay in medical body sensor network

Wearable or implantable sensor based remote health monitoring and support systems are gaining huge popularity because of its accuracy, affordability, mobility support and remote nature. For seamless monitoring, energy efficiency of Medical Body Sensor Network (MBSN) must be high to stretch out network lifetime. For single hop MBSN with STAR topology, sensor nodes send signal directly to the sink node whereas in multi-hop MBSN sensor nodes may act as a forwarder besides data acquisition. This drain out energy of sensor nodes in concern rapidly in case of repeated data forwarding which causes death of node, break of a route or path between sources and sink leading to unsuccessful data transmission. If it is a critical data then it may have life threatening impact on patient. Exclusive forwarder nodes, termed as relay node may be deployed in convenient positions inside the network for intelligent routing which will reduce burden of sensor nodes in terms of energy consumption. Here the nodes considered in the network are divided into different clusters. Relay nodes are placed at the position of cluster heads applying k- means algorithm which analyzes effectiveness of this framework in terms of energy efficiency. Comparison of energy consumptions in three different routing scenarios are considered: through relay node only, direct source to sink without relay node and using shortest path between source to sink irrespective of relay node position. The simulation results show that for varying network configurations in terms of number and position of nodes on human body, scenario of routing with relay nodes only proves to be energy efficient in comparison.

Wireless power transfer in the presence of a conducting interface: Analytical solution

A comprehensive model of wireless power transfer (WPT) between a pair of inductively coupled loop resonators in the vicinity of a conducting slab is developed by combining electromagnetic (EM) theory with an equivalent circuit representation of the system. The model incorporates all EM interactions occurring between the coils both directly and through the slab. The design of an optimal WPT regime for two coils in the presence of a conducting slab with variable conductivity is demonstrated by exploiting the model to predict the coil detuning produced by the slab. It is further shown that the model also constitutes an effective tool to estimate the losses in the conducting material solely in terms of the parameters of the generalised equivalent circuit and to analyse the power flow in a WPT system. The validity of the analytical results is proved by comparison with rigorous full-wave simulations. The proposed analytical method can have particular relevance in the field of wireless charging of electric vehicles and WPT for supplying implanted medical devices and body sensor networks, where it can be instrumental for the design and optimisation of the system to be compliant with existing RF exposure safety levels.

A Bayesian network model for data losses and faults in medical body sensor networks

Medical body sensor network (BSN) is a promising and flexible platform for person monitoring under natural physiological status. Due to limited resources, noise and unreliable links, sensor faults and data losses are common in BSNs. Most available works adopted schemes originated from traditional wireless sensor networks (WSNs) to detect faults and reconstruct data. However, these works either focused only on fault detection or failed to achieve a satisfactory reconstruction accuracy due to the lack of information redundancy in BSNs. In light of this, a Bayesian network based data reconstruction scheme is proposed in this paper, which rebuilds data using conditional probabilities of body sensor readings to recover missing data and sensor faults, rather than the redundant information collected from a large number of sensors. Note that the limited number of sensors in BSNs significantly reduces the complexity of Bayesian learning and thus enables efficient structure and parameter estimation of Bayesian network. Experiments on extensive online data sets have been conducted and our results show that the performance of our scheme outperforms all available data reconstruction schemes.

Threshold Tuning-Based Wearable Sensor Fault Detection for Reliable Medical Monitoring Using Bayesian Network Model

As the medical body sensor network (BSN) is usually resource limited and vulnerable to environmental effects and malicious attacks, faulty sensor data arise inevitably which may result in false alarms, faulty medical diagnosis, and even serious misjudgment. Thus, faulty sensory data should be detected and removed as much as possible before being utilized for medical diagnosis-making. Most available works directly employed fault detection schemes developed in traditional wireless sensor network (WSN) for body sensor fault detection. However, BSNs adopt a very limited number of sensors for vital information collection, lacking the information redundancy provided by densely deployed sensor nodes in traditional WSNs. In light of this, a Bayesian network model-based sensor fault detection scheme is proposed in this paper, which relies on historical training data for establishing the conditional probability distribution of body sensor readings, rather than the redundant information collected from a large number of sensors. Furthermore, the Bayesian network-based scheme enables us to minimize the inaccuracy rate by optimally tuning the threshold for fault detection. Extensive online dataset has been adopted to evaluate the performance of our fault detection scheme, which shows that our scheme possesses a good fault detection accuracy and a low false alarm rate.

Fault diagnosis of body sensor networks using hidden Markov model

In this paper, we focus on medical body sensor networks collecting physiological signs to monitor the health of patients. We propose a Hidden Markov Model (HMM) based method for fault diagnosis of measured data transmitted from sensors. We firstly verify the Markov property of temporal data sequences from medical databases. Then we improve the Baum-Welch algorithm at two aspects to estimate parameters of HMMs by history training data, and use the Viterbi algorithm to determine whether the new sensor reading is faulty. Finally, we do experiments on both real and synthetic medical datasets to study the performance of the fault diagnosis method. The result shows that the proposed approach possesses a good detection accuracy with a low false alarm rate.

An Energy-Efficient MAC Protocol for Medical Emergency Monitoring Body Sensor Networks.

Medical emergency monitoring body sensor networks (BSNs) monitor the occurrence of medical emergencies and are helpful for the daily care of the elderly and chronically ill people. Such BSNs are characterized by rare traffic when there is no emergency occurring, high real-time and reliable requirements of emergency data and demand for a fast wake-up mechanism for waking up all nodes when an emergency happens. A beacon-enabled MAC protocol is specially designed to meet the demands of medical emergency monitoring BSNs. The rarity of traffic is exploited to improve energy efficiency. By adopting a long superframe structure to avoid unnecessary beacons and allocating most of the superframe to be inactive periods, the duty cycle is reduced to an extremely low level to save energy. Short active time slots are interposed into the superframe and shared by all of the nodes to deliver the emergency data in a low-delay and reliable way to meet the real-time and reliable requirements. The interposition slots can also be used by the coordinator to broadcast network demands to wake-up all nodes in a low-delay and energy-efficient way. Experiments display that the proposed MAC protocol works well in BSNs with low emergency data traffic.

Recent Advances on Body-Heat Powered Medical Devices

Implantable medical devices, wearable medical devices and body sensor networks are rapidly developing research areas of recent years. These devices need self-powered uninterruptible power supplies to accomplish their functions properly. One of the most efficient energy harvesting methods for medical applications is thermoelectric generators. Thermoelectric generators use temperature difference between two points to produce electrical energy. This new technology provides energy autonomy. Thus without affecting the daily life of patient's, light weight and esthetic medical device can be established and this would increase patients quality of life. In this study, autonomous medical devices fully powered by human body-heat are reviewed. Considering the results, body-heat powered thermoelectric generators will be an inseparable part of autonomous medical devices in the near future.

Packet-level scheduling for implant communications using forward error correction in an erasure correction mode for reliable u-healthcare service

In u-healthcare services based on wireless body sensor networks, reliable connection is very important as many types of information, including vital signals, are transmitted through the networks. The transmit power requirements are very stringent in the case of in-body networks for implant communication. Further- more, the wireless link in an in-body environment has a high degree of path loss (e.g., the path loss exponent is around 6.2 for deep tissue). Because of such inherently bad settings of the communication nodes, a multi-hop network topology is preferred in order to meet the transmit power requirements and to increase the battery lifetime of sensor nodes. This will ensure that the live body of a patient receiving the healthcare service has a reduced level of specific absorption ratio (SAR) when exposed to long-lasting radiation. We propose an efficient method for delivering delay-intolerant data packets over multiple hops. We consider forward error correction (FEC) in an erasure correction mode and develop a mathematical formulation for packet-level scheduling of delay-intolerant FEC packets over multiple hops. The proposed method can be used as a simple guideline for applications to setting up a topology for a medical body sensor network of each individual patient, which is connected to a remote server for u-healthcare service applications.

Authenticated symmetric-key establishment for medical body sensor networks

This study concerns security issues of the emerging Wireless Body Sensor Network (WBSN) formed by biomedical sensors worn on or implanted in the human body for mobile healthcare applications. A novel authenticated symmetric-key establishment scheme is proposed for WBSN, which fully exploits the physiological features obtained by network entities via the body channel available in WBSN but not other wireless networks. The self-defined Intrinsic Shared Secret (ISS) is used to replace the pre-deployment of secrets among network entities, which thus eliminates centralized services or authorities essential in existing protocols, and resolves the key transport problem in the pure symmetric-key cryptosystem for WBSN as well. The security properties of the proposed scheme are demonstrated in terms of its attack complexity and the types of attacks it can resist. Besides, the scheme can be implemented under a light-weight way in WBSN systems. Due to the importance of the ISS concept, the analysis on using false acceptance/false rejection method to evaluate the performance of ISS for its usage in the scheme is also demonstrated.