Smart Body Sensor Object Networking

This paper discusses smart body sensor objects （BSOs）, including their networking and internetworking. Smartness can be incorpo-rated into BSOs by embedding virtualization, predictive analytics, and proactive computing and communications capabilities. A few use cases including the relevant privacy and protocol requirements are also presented. General usage and deployment eti-quette along with the relevant regulatory implications are then discussed.

Mid-Range Wireless Power Transfer and Its Application to Body Sensor Networks

It has been reported that, through the evanescent near fields, the strongly coupled magnetic resonance is able to achieve an efficient mid-range Wireless Power Transfer (WPT) beyond the characteristic size of the resonator. Recent studies on of the relay effect of the WPT allow more distant and flexible energy transmission. These new developments hold a promise to construct a fully wireless Body Sensor Network (wBSN) using the new mid-range WPT theory. In this paper, a general optimization strategy for a WPT network is presented by analysis and simulation using the coupled mode theory. Based on the results of theoretical and computational study, two types of thin-film resonators are designed and prototyped for the construction of wBSNs. These resonators and associated electronic components can be integrated into a WPT platform to permit wireless power delivery to multiple wearable sensors and medical implants on the surface and within the human body. Our experiments have demonstrated the feasibility of the WPT approach.

Secured ECG Signal Transmission Using Optimized EGC with Chaotic Neural Network in WBSN

In wireless body sensor network(WBSN),the set of electrocardiogram(ECG)data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient.While transmit-ting these collected data some adversaries may capture and misuse it due to the compromise of security.So,the major aim of this work is to enhance secure trans-mission of ECG signal in WBSN.To attain this goal,we present Pity Beetle Swarm Optimization Algorithm(PBOA)based Elliptic Galois Cryptography(EGC)with Chaotic Neural Network.To optimize the key generation process in Elliptic Curve Cryptography(ECC)over Galoisfield or EGC,private key is chosen optimally using PBOA algorithm.Then the encryption process is enhanced by presenting chaotic neural network which is used to generate chaotic sequences or cipher data.Results of this work show that the proposed cryptogra-phy algorithm attains better encryption time,decryption time,throughput and SNR than the conventional cryptography algorithms.

MobiHealthcare System: Body Sensor Network Based M-Health System for Healthcare Application

M-health, which is known as the practice of medical and public health supported by mobile devices such as mobile phones and PDAs for delivering medical and healthcare services, is currently being heavily developed to keep pace with the continuously rising demand for personalized healthcare. To this end, the MobiHealthcare system, which provides a personalized healthcare based on body sensor network, is developed. The system includes various body sensors to collect physiological signals specifically for different requirements, a cell phone to facilitate the joint processing of spatially and temporally collected medical data from different parts of the body for resource optimization and systematic health monitoring, a server cluster with great data storage capacity, powerful analysis capabilities to provide data storage, data mining and visualization. Compared with existing M-Health system, the MobiHealthcare system is characteristics of low coupling and powerful parallel computing capabilities. Various healthcare applications have been implemented in the proposed system to demonstrate its effectiveness in providing a powerful platform.

A Survey on Context-Aware Sensing for Body Sensor Networks

Context awareness in Body Sensor Networks (BSNs) has the significance of associating physiological user activity and the environment to the sensed signals of the user. The context information derived from a BSN can be used in pervasive healthcare monitoring for relating importance to events and specifically for accurate episode detection. In this paper, we address the issue of context-aware sensing in BSNs, and survey different techniques for deducing context awareness.

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 appli-cations. 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 au-thorities essential in existing protocols,and resolves the key transport problem in the pure symmet-ric-key cryptosystem for WBSN as well. The security properties of the proposed scheme are demon-strated 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.

A Review of Wireless Body Area Networks for Medical Applications

Recent advances in Micro-Electro-Mechanical Systems (MEMS) technology, integrated circuits, and wireless communication have allowed the realization of Wireless Body Area Networks (WBANs). WBANs promise unobtrusive ambulatory health monitoring for a long period of time, and provide real-time updates of the patient’s status to the physician. They are widely used for ubiquitous healthcare, entertainment, and military applications. This paper reviews the key aspects of WBANs for numerous applications. We present a WBAN infrastructure that provides solutions to on-demand, emergency, and normal traffic. We further discuss in-body antenna design and low-power MAC protocol for a WBAN. In addition, we briefly outline some of the WBAN applications with examples. Our discussion realizes a need for new power-efficient solu-tions towards in-body and on-body sensor networks.

A novel nest-based scheduling method for mobile wireless body area networks

Wireless Body Area Networks(WBANs)comprise various sensors to monitor and collect various vital signals,such as blood pressure,pulse,heartbeat,body temperature,and blood sugar.A dense and mobile WBAN often suffers from interference,which causes serious problems,such as wasting energy and degrading throughput.In reality,not all of the sensors in WBAN need to be active at the same time.Therefore,they can be divided into different groups so that each group works in turn to avoid interference.In this paper,a Nest-Based WBAN Scheduling(NBWS)algorithm is proposed to cluster sensors of the same types in a single or multiple WBANs into different groups to avoid interference.Particularly,we borrow the graph coloring theory to schedule all groups to work using a Time Division for Multimodal Sensor(TDMS)group scheduling model.Both theoretical analysis and experimental results demonstrate that the proposed NBWS algorithm performs better in terms of frequency of collisions,transmission delay,system throughput,and energy consumption compared to the counterpart methods.

基于LoRa的生猪体征监测系统设计与实现

为克服现有生猪体征监测模块功能单一、传输距离受限、功耗大等问题,设计一种以低功耗智能耳标为采集终端的生猪体征监测与异常诊断系统。该系统以无线微控制器为硬件核心,结合猪只体温、心率、运动感知传感器,LoRa和GPRS技术组成的无线传感网络和云服务器构建生猪体征信息采集、健康评估、异常诊断监测平台,提高生猪养殖过程的智能化管理水平。试验表明,该系统运行稳定,能够实时采集、传输、显示、查询生猪体征参数和异常行为,体温相对误差小于1%,心率相对误差小于5%,通信网络丢包率小于5%,传输距离大于3 km,行为辨识结果大于90%,满足中小型规模化生猪养殖场的实际使用需求。可为生猪异常诊断、早期疾病检测治和疫情预防提供科学依据。

体域网BSN的研究进展及面临的挑战

作为WSN的分支和物联网的重要组成部分,BSN通过应用泛在计算、智能信息处理和新型网络化等技术来提高人民医疗保健的水平,并因此日益受到研究人员和企业的关注.已有的研究综述分别在BSN的生物传感器、无线通信架构和数据安全等方面给出了充分讨论.将着重在数据融合、情景感知和系统技术这3个方面总结BSN领域的技术挑战、研究现状和发展趋势.并给出了BSN基本概念和研究状况分析,讨论了BSN系统架构、代表性应用及项目研究,阐述了BSN未来研究展望和应用前景,提出了限制BSN发展的实际问题.虽然BSN还面临诸多挑战,但必然会朝着多种技术相结合的智能化方向发展,并成为未来医疗健康监护的必然选择.

基于BSN和CHMMs的人体日常动作识别方法研究

应用人体传感器网络(body sensor networks,BSN)识别人体日常动作可以有效地提高对老年人、慢性病人,以及术后病人等特殊人群的医疗监护质量.为此建立了一个基于BSN的人体日常动作监督平台,应用采集到的加速度信号识别9个常见的人体日常动作.针对动作识别过程中存在的多传感器数据融合问题,提出一种基于耦合隐马尔可夫模型(coupled hidden Markov models,CHMMs)的动作识别方法.实验结果显示,与已有动作识别方法相比,提出的基于CHMMs的动作识别方法的识别正确率有明显的提高.

基于BSN识别双人交互动作方法的研究

基于体感网对人体动作进行识别的很多研究都是针对单人动作,很少有研究讨论双人交互动作的识别。针对双人交互动作中两人肢体行为的特点,提出了一种隐马尔可夫模型和马尔可夫逻辑网相结合的方法。其中,单人原子行为通过建立隐马尔可夫模型来进行识别,在两人交互行为的语义建模中,建立一阶逻辑知识库,并通过训练马尔可夫逻辑网来最终实现两人交互行为的决策。实验结果表明,与基于特征层数据融合的一些方法相比,该方法获得了更高的识别精度,能够有效地识别出双人交互动作。

基于BSN和神经网络的人体日常动作识别方法

基于人体传感器网络(BSN)对人体动作的识别,在远程医疗服务中具有重要应用.搭建了一个基于BSN的人体动作监测平台,实验中通过固定在人体腰部和大腿上的两个加速度传感器节点,来采集人体日常生活中的7个动作所产生的加速度信号.特征提取包含传感器节点在3个轴上信号的时域和频域信息,并采用神经网络和分层的方法融合信息对7个动作进行分类和识别.实验结果表明,应用所搭建的BSN平台和识别方法,采用两个传感器节点识别人体日常生活中的7个动作具有很高的正确率.

BSN中基于wi-DFD的轻量级信源编码算法

节点资源受限一直是BSN所面临的主要挑战,其中无线数据传输又占有主要的能量消耗,信源编码是降低BSN中无线通信能耗的有效方法之一.提出了基于wi-DFD模型的用于压缩传输数据的轻量级信源编码算法(简称ISC算法).该方法采用数据融合和自适应技术,找到原始感知数据中的具有重要意义的特征点,并根据这些特征点在已有消息数据包格式的基础上设计新的负载数据压缩包格式,传输该信源压缩包不仅降低了无线传输数据量,所获得的特征点还有助于提高了系统分析能力和识别精度.在已有的中医远程无线医疗BSN系统中进行了实测数据实验,实验结果表明该方法明显减少了无线传输数据量,有效降低无线传输能耗.

Using Wearable Sensors for Remote Healthcare Monitoring System

Recent technological advances in wireless communications and wireless sensor networks have enabled the design of low-cost, intelligent, tiny, and lightweight medical sensor nodes that can be strategically placed on human body, create a wireless body area network (WBAN) to monitor various physiological vital signs for a long period of time and providing real-time feedback to the user and medical staff. WBANs promise to revolutionize health monitoring. In this paper, medical sensors were used to collect physiological data from patients and transmit it to Intelligent Personal digital Assistant (IPDA) using ZigBee/IEEE802.15.4 standard and to medical server using 3G communications. We introduced priority scheduling and data compression into the system to increase transmission rate of physiological critical signals which improve the bandwidth utilization. It also extends the life time of hand-held personal server by reducing power consumption during transmission.

Body sensor networks for ubiquitous healthcare

Body sensor networks provide a platform for ubiquitous healthcare, driving the diagnosis in hospital static environment to the daily life dynamic context. We realized the importance of sensing of activities, which is not only a dimension of human health but also important context information for diagnosis based on the physiologic data. This paper presents our ubiquitous healthcare system, uCare. It consists of uCare devices and a server system. Currently, the uCare system is designed for cardiovascular disease (CVD) examination and management. The uCare device has been tested in a trial in Beijing Hospital. The uCare system will be further tested in elderly care at home and exercise management in training to measure heart dynamics during training.

TuLP: A Family of Lightweight Message Authentication Codes for Body Sensor Networks

A wireless sensor network （WSN） commonly whilst a body sensor network （BSN） must be secured with requires lower level security for public information gathering, strong authenticity to protect personal health information. In this paper, some practical problems with the message authentication codes （MACs）, which were proposed in the popular security architectures for WSNs, are reconsidered. The analysis shows that the recommended MACs for WSNs, e.g., CBC- MAC （TinySec）, OCB-MAC （MiniSec）, and XCBC-MAC （SenSee）, might not be exactly suitable for BSNs. Particularly an existential forgery attack is elaborated on XCBC-MAC. Considering the hardware limitations of BSNs, we propose a new family of tunable lightweight MAC based on the PRESENT block cipher. The first scheme, which is named TukP, is a new lightweight MAC with 64-bit output range. The second scheme, which is named TuLP-128, is a 128-bit variant which provides a higher resistance against internal collisions. Compared with the existing schemes, our lightweight MACs are both time and resource efficient on hardware-constrained devices.

Optimized Identification with Severity Factors of Gastric Cancer for Internet of Medical Things

The Internet of Medical Things (IoMT) emerges with the visionof the Wireless Body Sensor Network (WBSN) to improve the health monitoringsystems and has an enormous impact on the healthcare system forrecognizing the levels of risk/severity factors (premature diagnosis, treatment,and supervision of chronic disease i.e., cancer) via wearable/electronic healthsensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy playsa very significant role in the detection of gastric cancer. Convolutional NeuralNetwork (CNN) has been widely used to diagnose gastric cancer based onvarious feature extraction models, consequently, limiting the identificationand categorization performance in terms of cancerous stages and gradesassociated with each type of gastric cancer. This paper proposed an optimizedAI-based approach to diagnose and assess the risk factor of gastric cancerbased on its type, stage, and grade in the endoscopic images for smarthealthcare applications. The proposed method is categorized into five phasessuch as image pre-processing, Four-Dimensional (4D) image conversion,image segmentation, K-Nearest Neighbour (K-NN) classification, and multigradingand staging of image intensities. Moreover, the performance of theproposed method has experimented on two different datasets consisting ofcolor and black and white endoscopic images. The simulation results verifiedthat the proposed approach is capable of perceiving gastric cancer with 88.09%sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively.

Design and Implementation of a Wearable Body Area Sensor Network for Distributed FES System

A wearable body area sensor network(WBASN) was designed and implemented to monitor movement information of stroke patients in real time. The sensor system was combined with a previously developed distributed functional electrical stimulation(d FES) system, which is a promising technology for motor rehabilitation of stroke patients. Movement information could be useful in outcome assessment of rehabilitation, or for closed-loop adaptive stimulation during rehabilitation. In addition,a short-latency, low-power communication protocol was developed to meet the clinical requirements of energy efficiency and high rate of data feed-through. The prototype of the WBASN was tested in preliminary human experiments. Experimental results demonstrate the feasibility of the proposed wearable body area sensor network in monitoring arm movements on healthy subjects.

基于无线体域网技术的老人健康监护系统的设计

随着我国进入老龄化,老年人的健康监护越来越受到人们的关注,传统的健康监护模式已不能满足需求.而传统的面向家庭用的监护系统比较少,而且功能比较单一、操作性比较复杂、实时性差、价格比较昂贵.近年来伴随着MEMS技术、集成电路技术、无线通信技术的不断进步,无线体域网系统(wireless body sensor network)得到长足发展.基于无线体域网技术的应用系统也越来越受到人们的重视.设计了一种基于无线体域网技术的老年人健康监护系统.介绍了基于无线体域网技术的老年人健康监护系统的系统总体架构,重点介绍了无线体域网节点的设计以及无线体域网的系统构成.无线体域网节点包括无线心电与脉搏节点、无线血氧与体温节点.并介绍了各个生理参数的检测方法与检测电路.最后给出了生理参数的检测结果以及智能终端上的处理显示结果.