PERSONAL MACHINE TO MACHINE (M2M)

HEALTHCARE SYSTEM WITH MOBILE

DEVICE IN GLOBAL NETWORKS

Abstract

system that is both flexible and scalable. Based on the IPv6 protocol,the system can be used over a low-power wireless personal area network(6LoWPAN). Since a hierarchical network structure offers excellent acces-sibility, the system is applicable both to local and international healthcareservices. To further enhance scalability and reliability, the proposed sys-tem combines 6LoWPAN with mobile techniques, depending on whetherthe sensor is located inside or outside the range of a wireless sensor net-work (WSN). Employing wearable low-power sensors, the system measureshealth parameters dynamically. For wireless transmission, these sensorsare connected to an M2M node either through the internet or throughan external IPv4/IPv6-enabled network. The applicability of the IEEE802.15.4 and 6LoWPAN protocols to wide area networks were verified inpractical tests using an M2M gateway. To assess the physical health ofan individual, the system uses heart rate variability analysis in time andfrequency domains. Acquired data are first stored on a server for analysis.Results of the analysis are then automatically sent to Android-based mo-bile devices carried by the individual or appointed healthcare providers.In this way, mobile techniques are used to support remote health moni-toring services. This personal M2M healthcare system has the capacityto accurately process a large amount of biomedical signals. Moreover,due to its ability to use mobile technology, the system allows patients toconveniently monitor their own health status, regardless of location.

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1 Introduction

1.1 Overview and motivation

Information and communications technologies are transforming our social inter-actions, our lifestyles and our workplaces. One of the most promising applica-tions of information technology is healthcare and wellness management.

Healthcare is moving from reactive responses to acute conditions to a proac-tive approach characterized by early detection, prevention and long-term health-care management. In this framework, health condition monitoring and wellnessmanagement are seen as significant contributors to individual healthcare andwellbeing. This is particularly important in developed countries with a sig-nificant aging population, where information technology can be employed tosignificantly improve the management of chronic conditions and, thereby, over-all quality of life. Continuous or even occasional recording of biomedical signalsis particularly critical for the diagnosis and treatment of cardiovascular diseases.

For example, continuous recording of an electrocardiogram (ECG) or pho-toplethysmogram (PPG) by a wearable sensor provides a realistic view of apatients heart condition by tracking such factors as high blood pressure, stress, anxiety, diabetes and depression , during normal daily routines. Further, au-tomated analysis of such recorded biomedical signals supports doctors in theirdaily work and allows the development of warning systems. This brings severalbenefits, such as decreased healthcare costs, by increasing health observabil-ity, collaboration among doctors and doctor-to-patient efficiency . Moreover,continuous monitoring serves to increase early detection of abnormal healthconditions and diseases, offering a way of improving patients quality of life .

Nowadays, more attention is focused on the prevention and early detectionof diseases as well as on optimal management of chronic conditions. These func-tions are often augmented by new location-independent technologies. In orderto fully realize a pervasive or ubiquitous environment, personal area networks(PAN) must be connected to internet protocol (IP)-based networks.Such inte-gration enables resource sharing within networks, maximizing the utilizationof available resources. In addition, communication with the individual nodesin a network requires an efficient addressing mechanism. A working group ofthe Internet Engineering Task Force (IETF), known as 6LoWPAN, is currentlyworking to standardize internet protocol version 6 (IPv6) to facilitate data trans-mission over IP-based wireless networks . Due to their inherent properties, thesenetworks provide better coverage, fault tolerance and more robust behavior.

As the prime beneficiary of these new strategies, IPbased wireless networksare a catalyst for accelerating M2M service innovation, helping to identify hid-den growth opportunities in M2M services. The evolution of M2M hinges onthe development of wireless sensor networks (WSN) based on IPv6 techniques .A key advance is the adoption of IPv6 with its efficient addressing mechanism,replacing the current IEEE 802.15.4 standard. Extending the new standard toevery node/device enhances the quality of data transmission and expands thecoverage of healthcare services. Thus, a global M2M network based on a WSNand incorporating healthcare applications can be used to augment the tradi-tional healthcare system. A network of this type has the potential to providenew advantages, including accessibility and ease of measurement, for patientswith a global IPv6 address and a mobile communication device . In addition,

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new generation mobile phones have an important impact on the development ofsuch healthcare systems, as they seamlessly integrate a wide variety of networks(3G, Bluetooth, wireless LAN and GSM) through access points (APs), therebyproviding an opportunity to transmit recorded biomedical signals to a centralserver in a hospital . As a result, continuous monitoring of biomedical signalswill no longer be restricted to the home environment.

This thesis presents a new attempt of extending an IP-based sensor networksupporting 6LoWPAN and mobile communication into a global personal M2Mhealthcare system. Utilizing IPv6 over IEEE 802.15.4 represents a special matchof the two technologies, allowing the use of mobile devices for global access tohealthcare monitoring applications. As each 6LoWPAN node is assigned a globalIPv6 address, external hosts can directly communicate with these nodes, offeringhigher accessibility and an epoch-making network extension. Thus, the systemserves to improve the coverage of healthcare services through the flexibility andscalability provided by IPv6 addressing.

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2 Challenges and contributions

A number of challenges face the developers of WSNs, including support for M2Mtechnology to allow direct exchange of information between machines, which, in19 turn, enables systems to manage themselves without human intervention .A typical IEEE 802.15.4-based sensor network protocol uses a non-IP networklayer protocol in limited areas, where transmission control protocol/internetprotocol (TCP/IP) is not applicable. However, future applications of WSNconsist of thousands of nodes, and IP-based networks may be connected toother networks via the internet . This can be achieved by the effective use ofIPv6 standards. The emergence of 6LoWPAN can be treated as the beginningof a new generate of M2M networks. An equally important development hastaken place in mobile services. Rapid developments in mobile technologies allowmobile devices to monitor and communicate with embedded devices. Moreover,due to their ease of use and falling costs, mobile devices are increasingly usedin a range of applications, thereby contributing to producing great changes intodays lifestyle. During the past decade, the development of wireless mobile andinformation technologies (IT) has helped to extend the concept of ubiquitouscoverage to new segments of society.

Thus, many applications that were initially available at a fixed location onlyhave been transformed into ubiquitous applications, capable of being used wire-lessly and flexibly at anytime, anywhere. The same trend has been observedin the medical field. Over the years, many telemedicine and healthcare relatedsocieties and authorities have turned to wireless technologies to overcome thepoor mobility of desktop PC-based healthcare monitoring systems. As a result,the possibility to monitor biomedical signals using a mobile device is no longeran unachievable dream. Recent technological advances and the ever-increasingnumber of patients suffering from chronic diseases inspired the idea of this dis-sertation, where wireless technologies are applied to help patients improve theirsituation . These technologies allow drawing inferences in real-time from a rangeof behavioural data made available via a mobile device, such as a phone. Usersthen receive feedback relating to these behaviours, enabling them to make bettereveryday lifestyle choices and, ultimately, to better manage their health. Al-though many healthcare systems of this type are already in existence, they tendto have certain weaknesses. One of these is measurement area. Measurementsystems are often too large to carry and signal communication cables are notalways easy to move. Consequently, measurement signals can only be recordednear the device itself. Another problem is measurement coverage. To facilitatethe detection of the biomedical signals, measurements are generally conductedon the patients body, causing inconvenience. Moreover, specific parts of bodyare often 20 fixed and cannot be moved or used during the measurement, re-stricting the patients motions and movements.

An additional major problem is that sensor devices are vulnerable to noise,and wire cables are particularly prone to wear and damage. Analog circuit andcable movements are a significant source of noise, and analog data transmissionis responsive to changes in peripheral conditions. Though the concept of usingglobal M2M based healthcare system in the context of WSN adds to the networknew properties such as mobility and interoperability, the literature and our gen-eral understandings of global M2M healthcare system has been greatly affectedby the legacy of the theory and test-bed based projects. Until now, the theory

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and testbed based projects concerning purely global M2M healthcare system hasbeen limited. Motivated by these facts, this dissertation has been focused on es-tablishing a healthcare-oreiented M2M network combined with their widespreaduse in wireless technology to closely monitor changes in patient condition usingcomfortable biomedical sensors providing relevant biomedical information. Thismeans the proposed system support the highest level of wireless communicationbased healthcare integration system as a result of the specific well-designedmethodologies. The proposed system can improve the expansion of healthcareservice coverage through 6LoWPAN and mobile technology. Mobile devices,such as those used by the system, are almost universally IP-enabled, currentlymaking up the largest body of devices belonging to the Internet of Things (IoT).Thus, the main objective of this dissertation can be summarized as follows: topropose an approach to healthcare services based on conducting wireless mea-surements via 6LoWPAN and mobile networks using wearable health sensors.

The inherent advantages of the approach include improving mobile person-alized sensing and extending the network scale to allow the timely and reliablecommunication of measured data. This dissertation can be thought to makefour special contributions:

1. 6LoWPAN is studied in order to develop a WSN based on the IPv6 tech-nique. In fact, the 6LoWPAN technique provides a new attempt of the ex-tension of an IP-based sensor network environment at the different globalcoverage areas for the healthcare application with the IPv6 technique. Akey advance with the adoption of IPv6 is its efficient addressing mech-anism, which replaces the current IEEE 802.15.4 standard. The M2Mgateway provides reliable IPv6 communication to transmit a patientsbiomedical signals to a 21 doctor or server via the Internet. UtilizingIPv6 over the IEEE 802.15.4 is a special match of the two technologies.Extending the new standard to every node/device enhances the quality ofdata transmission and expands the coverage of healthcare services.

2. A global M2M network is implemented for the network performances un-der several different scenarios in healthcare applications. Therefore, exter-nal hosts directly communicate with the M2M nodes because each M2Mnode is assigned a global IPv6 address, thereby supporting higher acces-sibility and epoch-making network extension. Practical tests of networkperformance with different scenarios are conducted in global areas. Theproposed system includes measurement of health parameters, wireless andwire communications, health condition monitoring in a server and a mobilewith a global reach.

3. A mobile healthcare monitoring system is developed for a novel M2Mhealthcare solution based on Android mobile devices. An Android-basedsmartphone is used as monitoring terminal due to its smart functions andcomputer-like features. After testing the monitoring application by theAndroid emulator, the monitoring application is easily installed on theAndroid mobile via a direct cable from PC or at the Android marketsstore wirelessly. Utilizing a smartphone, the system has the capacity toprovide healthcare services not only in the home or hospital environments,but practically anywhere .

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4. Such health parameters as ECG, PPG and activity data can be recordedand analyzed, and their accuracy and usefulness for diagnosis can be de-termined. In the analysis of HRV signals, there are two different kindsof methods, which are time domain and frequency domain analysis. Todetect stress factor, this dissertation interprets HR distribution graph,SDNN, RMSSD, pNN50, and SI by analyzing the HRV signals in the timedomain. From the frequency domain analysis, negative emotions such asanxiety and hostility are verified by using the PSD analysis with spectralcomponents such as VLF, LF, HF, ratio of LF/HF, and ln values of LFand HF. These analysis results can then be used to offer further insightinto the natural cause and progression of diseases and to enhance the ac-curacy of early symptom detection To break down the overall objectiveinto smaller and manageable parts, three research questions have beenarranged.

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3 Literature review and related work

3.1 Wireless sensor network for a healthcare system

WSN is one of the fastest growing technologies in ubiquitous networking today.Standardization efforts, such as IEEE 802.15.4 , are geared to reduce costs,provide device customizability for diverse applications and create standards forinteroperability. The IEEE 802.15.4 standard was developed to address a de-mand for low-power and low-cost in low-rate wireless personal area networks(LR-WPAN). Dealing with low data rates, IEEE 802.15.4 offers very long bat-tery life (months or even years) and very low complexity. The IEEE standard802.15.4 defines the physical layer (PHY) and medium access control (MAC)sub-layer specifications for LR-WPAN in the 2.4 GHz and 868/915 MHz bands.A free license to use the industrial, scientific and medical (ISM) 2.4 GHz band isavailable worldwide, while the ISM 868 MHz and 915 MHz bands are only avail-able in Europe and North America, respectively. A total of 27 channels withthree different data rates are allocated in IEEE 802.15.4, including 16 channelswith a data rate of 250 Kbps in the 2.4 GHz band, 10 channels with a data rateof 40 Kb/s in the 915 MHz band and 1 channel with a data rate of 20 Kb/sin the 868 MHz band. Channel sharing is achieved using carrier-sense multipleaccess (CSMA), and acknowledgments are provided for reliability. Addressingmodes for 64-bit (long) and 16-bit (short) addresses are provided with unicastand broadcast capabilities.

The main characteristics of WSN devices are small physical size, low-powerconsumption, limited processing power, short-range communication capabilityand small storage capacity. [Paper I] A number of studies and projects havefocused on novel ubiquitous healthcare systems utilizing WSN technology tosimplify methods of monitoring and treating patients. A case in point is theMobiHealth project, which developed a system for ambulant patient monitoringover public wireless networks based on a body area network (BAN) . Another ex-ample is the Ubiquitous Monitoring Environment for Wearable and ImplantableSensors project (UbiMon) [33] at Imperial College London, which aims to pro-vide a continuous and unobtrusive monitoring system for patients to capturetransient, but life-threatening events. CodeBlue [34] was designed to operateacross a wide range of devices, including low-power motes, PDAs and PCs, andit addresses the special robustness and security requirements of medical caresettings.

In ubiquitous healthcare applications, the most significant limitations ofwireless networks are slow data transfer rates and lack of a single connectivitystandard that enables devices to communicate with one another and to exchangedata. Other limitations include wireless devices that are still in their infancyand therefore slower in speed than desktop computers, high initial costs involvedin setting up wireless systems and lack of real-time connectivity due to devicemobility. Typical IEEE 802.15.4-based sensor network protocols have a non-IPnetwork layer protocol in some limited area, where the TCP/IP protocol is notused. However, future applications of WSN consist of thousands of nodes, andIPbased networks may be connected to other networks via the internet . Thisrequires the use of an effective addressing mechanism needed for communica-tion between the individual nodes in a WSN. One such mechanism is offered bythe IPv6 standard. Thus, the emergence of 6LoWPAN can be treated as the

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beginning of a new generation of M2M networks.

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4 IPv6 over LoWPAN (6LoWPAN)

The IETF working group was officially organized in 2005 to define data trans-mission in 6LoWPAN. Note that even though IEEE 802.15.4 devices are beingconsidered for internet connectivity, the IPv6 technique has already emerged asa more powerful candidate for cheap, low-power microcontrollers and low-powerwireless radio technologies in futuristic networks. The vast majority of simpleembedded devices still make use of 8-bit and 16-bit microcontrollers with verylimited memory, as they are low-power, small and cheap. At the same time, thephysical trade-offs of wireless technology have resulted in short-range, low powerwireless radios with limited data rates, frame sizes and duty cycles.Released in2003, the IEEE 802.15.4 standard was the biggest factor leading to 6LoWPANstandardization. The popularity of this new standard gave the internet commu-nity much needed encouragement to standardize an IP adaptation for wirelessembedded links. This section discusses the characteristics of 6LoWPAN andexamines 6LoWPANs in terms of protocol stacks. 6LoWPAN is used for trans-mission of IPv6 packets based on the IEEE 802.15.4 communication protocoland IPv6 . Designed to replace IPv4, the IPv6 protocol offers scalability fordecades to come. In essence, the IPv6 technique expands the IP address spacefrom 32 to 128 bits to overcome the older standards address-space limitation.Moreover, IPv6 increases the maximum transmission unit (MTU) from 576 to1,280 bytes, in 27 response to the large link bandwidth size required for transmis-sion. Implemented on top of the IEEE 802.15.4 layer, the 6LoWPAN protocolstack transmits packets using a higher-level protocol, thus increasing flexibility.Fig. 1 shows the characteristics of the 6LoWPAN protocol stack with threegeneral parts: an M2M node, an M2M gateway and an IP network host. Inorder for 6LoWPAN to work with an IP network, the 6LoWPAN protocol stacksupports a number of features, such as compression, fragmentation and meshaddressing of packet headers. Also, the 6LoWPAN protocol stack contains anadditional interface for supporting communication based on the user datagramprotocol (UDP) from gateway to server over the internet. Health parameter-related information is carried in the adaptation layer, required by and defined inthe 6LoWPAN protocol stack. Fig. 2 shows the adaptation layer in 6LoWPAN.An adaptation mechanism allows interoperability between the IPv6 domain andthe IEEE 802.15.4 protocol and enables header compression along with fragmen-tation and mesh addressing features. Significantly, the adaptation layer offersnew functionality by allowing new packet formats. RFC 4919 and RFC 4944propose an adaptation layer to allow the transmission of IPv6 datagrams overIEEE 802.15.4 networks. A key concept applied throughout the 6LoWPANadaptation layer is that it uses stateless compression to omit the header fieldsof the adaptation layer, network layer and transport layer. Common values oc-cur due to frequent use of a subset of IPv6 functionality, such as UDP, TCP andinternet control message protocol version 6 (ICMPv6). Health parameter datais added in the adaptation layer, 28 which is required by the 6LoWPAN stack.This stack supports many features, including the compression, fragmentationand mesh addressing of packet headers. In IP-enabled networks, IPv6 packetsflow via the IEEE 802.15.4 protocol and various layers. The IEEE 802.15.4frame format consists of a preamble, start of frame, frame length and addressfield. The adaptation layer handles routing packets and compression of IPv6headers. 6LoWPAN represents an efficient extension of IPv6 into the wireless

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Figure 1: Characteristics of the 6LoWPAN protocol stack [II, published withthe permission of American Scientific Publishers.

embedded domain, enabling end-to-end IP networking and features for a widerange of embedded applications There is a huge range of applications that couldutilize an approach based on embedded wireless internet connectivity. Today,these applications are implemented using a variety of proprietary technologies,which are difficult to integrate into larger networks or internet-based services.Among the benefits of using IP in these applications, thereby integrating theminto the IoT are the following:

1. IP-based devices can be easily connected to other IP-enabled networkswithout the need for translation gateways or proxies.

2. IP networks allow the use of existing network infrastructures.

3. IP-based technologies have existed for decades, are very well known andhave been proven to work and scale. The socket application programminginterface (API) is one of the best-known and widely used APIs in theworld.

4. IP technology is specified in an open and free way, with standard processesand documents available to anyone. As a result, IP technology encouragesinnovation and is better understood by a greater number of users

5. Tools for managing, commissioning and diagnosing IP-based networks ex-ist already

Until now, only powerful embedded devices and networks have been able toparticipate natively in the internet. Direct communication with traditional IPnetworks demands many IPs, often requiring an operating system to deal withthe attendant complexity and maintainability. Further, traditional IP sets highdemands for embedded devices in terms of security, web services, managementand frame size. These requirements have in practice limited the IoT to deviceswith a powerful processor, an operating system with a full TCP/IP stack and

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an IP capable communication link. Typical embedded internet devices todayinclude industrial devices with Ethernet interfaces, M2M gateways with cellularmodems and advanced smartphones. A large majority of embedded applicationsinvolve limited devices, with low-power wireless and wired network communi-cations. Aspects of wireless embedded devices and networks that are particu-larly challenging for IP include power, duty-cycle, multicast, mesh topologies,bandwidth, frame size and reliability. Recently, many researchers are focusingon IP-related issues: the WirelessHART communication standard , released inSeptember 2009, includes 6LoWPAN technology and, in 2008, the InternationalSociety of Automation (ISA) began standardization of a wireless industrial au-tomation system called SP100.11a (also known as the ISA100.11a network layer), which is based on the 6LoWPAN standard, providing IPv6 connectivity to allwireless field devices. In addition, the IP minimization technique with low-power microcontrollers and wireless technologies includes the `IP project fromtheSwedish Institute of Computer Science, as well as the NanoIP project toimplement an alternative networking stack for control, automation and sensornetworks from the Centre for Wireless Communications. In addition, the IP forSmart Objects (IPSO) Alliance was founded in 2008 to promote the use of theIP500 Alliance , which is working to develop a recommendation for 6LoWPANcommunications. Several European projects specialize in future internet (FI)research, for example, the EU 4WARD project , in cooperation with the Eu-ropean Future Internet Assembly (FIA). Although most of the research relatedto FI does not consider embedded devices and networks, this aspect is startingto gain interest. The EU SENSEI project [48], for example, specializes in mak-ing wireless sensor and embedded networks a part of the global internet, bothcurrent and future. One of the subjects of the project is how wireless embed-ded networks and 6LoWPAN-type functionality can be made an integral part ofthe FI. Many studies are being conducted in various industrial segments, suchas transportation and automotive, smart home, environmental monitoring andbuilding management either to improve business performance and functionalityor to introduce new services. However, most of these projects are not related tohealthcare applications.

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4.1 Android mobile technology

Rapid technological development, ease of use and falling costs have made mo-bile devices increasingly popular, producing great changes in todays lifestyle.During the past decade, the development of wireless mobile and informationtechnologies (IT) has helped to extend the concept of ubiquitous coverage tonew segments of society. Thus, many applications that were initially availableat a fixed location only have been transformed into ubiquitous applications, tobe used wirelessly and flexibly at anytime, anywhere. The same trend has beenobserved in the medical field. Over the years, many telemedicine and healthcarerelated societies and authorities have turned to wireless technologies to overcomethe poor mobility of desktop PC-based healthcare monitoring systems. As a re-sult, the possibility to monitor biomedical signals using a mobile device is nolonger an unachievable dream. In this work, an Android-based smartphoneis used as monitoring terminal due to its smart functions and computer-likefeatures. Compared to other smartphone operation systems, the Android de-vice has many advantages, such as openness. Moreover, all applications areequal, there are no boundaries between applications and its development isfast. Furthermore, the Android smartphone is currently one of the most pop-ular smartphones on the market.Android, provided by Google and the OpenHandset Alliance (OHA), is an open-source software stack for mobile devicesthat includes an operating system, middleware and key applications. OHA is agroup of approximately technology and mobile companies, which combine theirefforts toward the goal of accelerating innovations in mobile networks and to-ward offering a better mobile experience to the customer. Android is built onan open Linux kernel that includes such core system services as security, mem-ory management, process management, network stack and drivers. Further, thekernel acts as an abstraction layer between hardware and the software stackand can be extended to incorporate new cutting edge technologies. Using aLinux kernel as a hardware abstraction layer, allows Android to be ported toa wide variety of platforms. Eclipse is a multi-language software developmentenvironment comprising an integrated development environment (IDE) and anextensible plug-in system. For the development of applications, a software de-velopment kit (SDK) is provided with the necessary tools and API. Eclipse SDKis meant for Java developers, who can extend its abilities by installing plug-inswritten for the Eclipse platform, such as development toolkits for other program-ming languages, as well as create their own plug-in modules. All applicationsare written in the Java programming language. The application layer includesa set of core applications preinstalled on every Android device, including email,maps, contacts, web browser, phone dialler, calendar, text message and AndroidMarket. Further, Android applications can utilize the functionalities of otherapplications and services. A service is an application component without a userinterface that runs in the background for an indefinite period of time.

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5 Machine-to-machine (M2M) technology

5.1 M2M communications

M2M is presently a major subset of the Internet of Things (IoT) concept . Thisdecade is widely predicted to see a rise in M2M communication over wired andwireless links. The origin of M2M communication is cloudy; it is not easy to ex-actly determine when cellular technology first began to learn to connect directlyto other computer systems. As it is, due to the limitations of wired communi-cation infra-structure, such as the presence of wires and inflexibility in networkdeployment, wireless networking systems are now considered as a solution forM2M technology. Typically, the M2M device class is characterized by verylow-power consumption and little or no human intervention. In many cases,they autonomously communicate with each other or with a central controller.Particular focus is paid to the power requirements of M2M devices as well asthe complications that arise when communicating between local networks andacross the internet. With the dramatic proliferation of embedded devices, M2Mcommunication has become the dominant communication paradigm in manyapplications which concentrate on data exchange among networked machines,applications andservices . Examples of embedded devices and systems usingIP today arrange from mobile phones, personal health devices, home automa-tion, industrial automation and smart metering to environmental monitoringsystems. This will lead to an unprecedented increase in data traffic involvingmachines communicating with other machines without human interaction. Forinstance, researchers predict that by 2015 there will be 25 billion wirelessly con-nected devices about three connected devices per person that do not requireany human intervention. Looking to the future, Cisco Inc. predicts there willbe 50 billion devices connected to the internet by 2020 . It is important tonote that these estimates do not take into account rapid advances in internetor device technology; the numbers presented are based on what is known to betrue today. Additionally, the number of connected devices per person may seemlow. This is because the calculation is based on the entire world population,much of which is not yet connected to the internet. By reducing the populationsample to people actually connected to the internet, the number of connecteddevices per person rises dramatically. For example, we know that approximately2 billion people use the internet today. Insert these figures into the equation andthe number of connected devices per person jumps to 6.58 in 2020. Early workon a wireless M2M system with global network connectivity includes the effortof the European Telecommunication Standards Institute (ETSI), which set upa working group in 2009 for standardizing M2M, including an endto- end IParchitecture compatible with 6LoWPAN . Another important initiative is theadvanced metering infrastructure of the smart grid, which offers the greatestgrowth potential for systems requiring no human intervention, and is designedfor characterizing power requirements and energy distribution. A possible so-lution to improving the scalability of smart grid communications has also beenpresented . EISLAB at the Lulea University of Technology, Sweden, has intro-duced an information architecture based on service-oriented architecture (SOA)for future collaborative cloud-based automation systems. This architecture of-fers several useful features, such as real-time management, distributed eventmanagement and service specifications . This system provides interoperable

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network services to complex and potentially very large multi-disciplinary, het-erogeneous, networked distributed systems. Research along these lines serves topresent a coherent view of the necessary architecture, taking account of tech-nical specifications and other considerations and defining preliminary services.M2M technology is evolving into a high-performance, high-efficiency, highprofitmethod used to monitor and control user assets, machinery and productionprocesses, among others, while offering benefits such as increased reliability andcost savings. The unlimited potential of wireless M2M systems means that themarket will be ready to experience explosive growth over the next few years.It is evident that the biggest challenges to M2M include remote managementof M2M devices and data transmission in the absence of direct human-machineinteraction. Considering the large number of M2M systems expected to be de-ployed in highly distributed networks and the requirement for low-cost devicesand implementations, a global implementation of healthcare monitoring systemswill soon be feasible and practical.

5.2 Mobile-based M2M healthcare system

Mobile communication devices can now provide efficient and convenient services,such as remote information interchange and resource access through mobile de-vices, allowing users to work ubiquitously. With the astronomical growth ofthe cell phone ownership rate, mobile healthcare supported by mobile and wire-less technologies emerges as a cost-effective care solution with a better overallhealth outcome. A feasible mobile device for ubiquitous healthcare must becheap to produce, ultra-compact, lightweight and its power consumption mustbe low. In addition to broad communication capabilities, it must support suchfunctions as health condition monitoring and display of biomedical signals. Itis now possible to draw inferences in real-time from a range of behavioural datamade available via mobile phones. Feedback can then be offered relating tothese behaviours, enabling people to make better everyday lifestyle choices and,ultimately, to better manage their health. 6LoWPAN was introduced to enablethe creation of a global IP-based M2M network, potentially involving trillionsof devices. Such a network enables the implementation of healthcare applica-tions with direct exchange of information between machines and the option ofcontrolling devices without human assistance. Fig. 2 shows a block diagram ofa personal M2M healthcare system for health condition monitoring in a globalenvironment enabled by the flexibility and scalability of 6LoWPAN and mobilecommunication

1. System architecture, Shown in Fig 3 is the overall architecture of the pro-posed personal M2M healthcare system used to monitor patient healthstates. Utilizing 6LoWPAN and mobile communication, the system offersexcellent flexibility and scalability. For global network communication,6LoWPAN is connected to external IP-enabled networks by M2M gate-ways. In its typical role, a 6LoWPAN network operates on the edge ofthe system, acting as a stub network. For practical measurements, anM2M node, with integrated health sensors, collects and sends biomedicalsignals. This M2M node consists of a micro controller unit (MCU), radiofrequency (RF) transceiver chip and battery and transfers the measuredbiomedical signals to an M2M gateway connected to the internet. At the

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Figure 2: Block diagram of overall system architecture [VII, published with thepermission of IEEE]

Figure 3: System architecture of a personal M2M healthcare solution [VII,published with the permission of IEEE].

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server, a server program monitors and analyzes the transmitted biomedicalsignals and network topologies. Then, the program verifies the recordedsensing values and analysis results, combining them with relevant personaldata before sending this information to an Android mobile for user accesswithin any internet supported area

2. Benefits of M2M networks. Among valuable concepts worth consideringin relation to an M2M-based personalized healthcare system are flexibleoperation, integration, interoperability and interference, use of existing in-frastructure, functionality, cost and government mandates. Table 1 sum-marizes a comparison of an M2M system and a conventional system toindicate the range and significance of the advantages offered by M2M.The integration of mobile-based M2M networks in healthcare applicationswill improve the quality and efficiency of treatment in various ways. Thesystem presented here assumes that 6LoWPAN-based M2M systems willbe used in the general hospital, office and home environments while pa-tients move within these facilities. In addition, when patients move out-side 6LoWPAN facilities, mobile services, provided by a mobile networkor the internet, kick in to support health monitoring. Various potentialapplications for patient monitoring, and benefits, such as measurementquality improvement, error reduction, record accuracy, cost reduction andlocation accuracy will be significantly improved by these technologies.

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6 System design and implementation

This chapter describes the backbone of the proposed personal healthcare system,comprising several modules and programming techniques for M2M devices forwireless communication. Also the implementation of the overall system will bediscussed. Essential modules are the ECG sensor, PPG sensor, M2M node, M2Mgateway and smartphone device. Each module serves its own purpose, while atthe same time collaborating with other modules to ensure the integrity andreliability of the whole system structure through such programming techniquesas TinyOS programming, gateway tunnelling and server programming. Theaim of this work is to set up a fully-integrated wearable system, able to acquirehealth data globally. Biomedical signals are measured utilizing 6LoWPAN andrecorded data are first transmitted through an M2M gateway to a server PC foranalysis. Analyzed health data are then sent to the smartphone device. Thusimplemented, the proposed personal M2M healthcare system can be globallyapplied to improve accessibility via flexible networks and to enhance scalabilityusing multiple M2M gateways connected to comfortable wearable sensors.

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Figure 4: Wearable health sensors with a PCB board Sensors.

6.1 Hardware design

Core hardware of this system comprises the modules, i.e., the ECG sensor, PPGsensor, M2M node and M2M gateway. For low-power operation, the wearablesensors and M2M nodes used in the system maximize their lifetime by the useof a 3 V battery with a power consumption of less than 50 mW

1. Health sensors, Generally, biomedical signals are ideal for predicting andmonitoring patient health conditions. In the proposed system, two biomed-ical signals, ECG and PPG, form the basis for diagnostic analysis. Brieflyput, ECG signals are recorded as electrical activity of the heart over time,while PPG signals are measured as changes in light absorption to moni-tor blood volumes within peripheral skin tissue. Wearable health sensors,shown in Fig. 4, were designed to enable the measurement of biomedi-cal signals. The PPG sensor was designed to obtain PPG waveforms andoxygen saturation data from the patients finger by calculating the ratioof red and infrared light on the device surface, which varies dependingon how much light is absorbed by tissue. [Paper III] This PPG sensorcontains analog signal processing, amplifiers, filters and analog-to-digitalconverters (ADC). Since raw signals are small and distorted, signal pro-cessing is initially determined by the communication system. Raw signalsdemand a 24 Hz low-pass filter (LPF) to reduce high frequency noise anda 0.5 to 10 Hz band-pass filter (BPF) to prevent the direct current (DC)component from enhancing the alternating current (AC) component. Fil-tered signals are fed into the MCU of the M2M node through a universalasynchronous receiver/transmitter (UART) port containing the sampledPPG signals at 75 Hz . Attached to a T-shirt, the ECG sensor employsconductive fabric electrodes. Two of these, measuring 8 cm in size, areon the inside of the shirt over the chest area to enable skin contact. ECG

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signals from both chest electrodes are obtained by an analog signal con-ditioning circuit, which contains amplifiers as well as a low-pass and ahigh-pass filter (HPF) circuit. Next, raw signals are filtered and amplifiedthrough an analog signal conditioning circuit. The LPF and HPF circuitsare designed to eliminate analog noise signals with a high SNR. The finalcut-off frequency is from 0.5 Hz to 34 Hz and total gain is 500 (27 dB).Specifications

2. M2M devices. The M2M devices of the personal healthcare system aredesigned to measure and transmit biomedical signals to a flexible and scal-able M2M network. M2M nodes connected to health sensors are placed onthe patients body and are mainly responsible for collecting and transmit-ting signals sampled at 100 Hz for ECG and at 75 Hz for PPG to the M2Mgateway. Also the M2M nodes connected to wearable sensors are placedon the patients body. They are used to collect such health parameters asECG signals, PPG signals and oxygen saturation values, which they thentransmit to a server for monitoring and analysis. All M2M nodes comewith their own unique IP address that is connected to the M2M gateway.Each of these nodes consists of a very-low power microcontroller, externalmemory and a transceiver unit packed in a small board. The M2M gate-way forms a link between two different networks, one running IPv6 overIEEE 802.15.4, while the other is an IP-based network. Moreover, theM2M gateway performs global address translation to either short 16-bitaddresses or to 64-bit IEEE extended unique identifier (EUI) MAC ad-dresses. TinyOS-based M2M nodes are allocated their own IP addressesby the M2M gateway to enable the transmission of IPv6 packets. The6LoWPAN protocol stack manages this transmission of packets using the6LoWPAN ad hoc on-demand distance vector (LOAD) routing protocolsdeveloped by the IETF group. This routing protocol guarantees flexibilityin the face of topology changes in WSNs.

3. Android mobile device. After emulator testing, the server program sendsreceived data wirelessly to the patients Android mobile device. Variousalgorithms are combined into mobile application software created withthe Java Android language to handle all server processes. Query pro-cesses handle communication between the server and the Android mo-bile device for a graphic real-time display of biomedical signals on thescreen. The mobile monitoring program was implemented and tested onan Android mobile phone (Samsung Galaxy S, Korea) [64] running a 1GHz ARM processor (Cortex A8, Hummingbird) and Android operatingsystem (OS) version Through wired or wireless internet connections, theserver connected todifferent types of mobile devices and performed a rangeof development tests on them.

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6.2 Software design

In terms of architecture, software for the personal M2M healthcare system fallsinto three parts: TinyOS programming in the M2M nodes, tunnelling processesin the M2M gateway for network access and server application program. Theseparts will be discussed in the following sections.

1. TinyOS programming, The proposed M2M nodes run on TinyOS, an opensource operating system mainly for wireless embedded sensors, which wasdeveloped by the University of California, Berkeley, in co-operation withIntel Research. TinyOS performs services and procedures using a set ofconfigured library components and custommade components, designed tominimize the utilization of resources by the TinyOS kernel scheduler .For TinyOS programming, TinyOS 2.x version is used to enable deviceidentification (ID) based on unique IPv6 addresses (IPv4/IPv6 dual) us-ing Kubuntu (Canonical Ltd., UK) or Cygwin (Cygnus Solutions, USA)which is a Windows-based Linux operating system. Kubuntu is an op-erating system built by a worldwide team of expert developers, and it isused for TinyOS programming in this dissertation. All M2M nodes areprogrammed with total 42,774 bytes of compiling size on M2M nodesmemory to collect measured biomedical signals and create data packetsfor wireless communication. As shown in, all packets transmitted throughthe internet are verified by the server monitoring program, which receivesthe signals in 100-byte packets through the M2M gateway using Tera Term(terminal emulator). On receiving these packets, the monitoring programstores the recorded health data values in a database and dynamically plotsthe measured biomedical signals.

2. Gateway tunneling. By enabling the use of the IPv6 technique, 6LoW-PAN provides an extension to IP-based sensor networks. This serves toenhance the coverage area of such applications as the personal healthcareapplication presented here. The applications M2M gateway provides reli-able IPv6 communication for transmitting biomedical signals to a doctoror server via the internet. Utilizing IPv6 over the IEEE 802.15.4 rep-resents a special match of the two technologies. As each M2M node isassigned a unique IPv6 address, they can directly communicate with eachother, offering higher accessibility and an epoch-making network exten-sion. For IPv6 communication, each IPv6 address must be defined at theM2M gateway and server PC. As IP networks can usually be accessedby IPv4 addresses, the IPv6 to IPv4 tunneling process, which changesIPv4 addresses to IPv6 addresses at the M2M gateway, is required to ac-cess the server PC through the internet. In an experiment illustrated inTable 4, an IPv6 address, in this derived from the corresponding IPv4 ad-dress 203.250.121.207 (Pukyong National University, Korea) by the IPv6to IPv4 tunnelling process, is assigned to a register in the server PC. Alsothe M2M gateway is assigned an IPv6 address, namely,corresponding tothe IPv4 address of 130.231.59.102 (University of Oulu, Finland). This isnecessary, because each individual M2M node must be given a lower pre-fix address, such as 2002:82e7:3b66:100:22:ff:fe00:5 in 6LoWPAN by theauto-configuration function

3. Server program. Measured biomedical signals are sent through the in-

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Figure 5: Screen capture of the monitoring program on the server [VII, publishedwith the permission of IEEE]

ternet by the M2M gateway to the server PC for further processing. Amonitoring and analysis graphical user interface (GUI) was designed withthe C Sharp language to monitor, store and process the received data, asshown in Fig. 7. On receiving a data packet through the M2M devices,the monitoring programme processes it, extracting useful data, as illus-trated in Fig. 8. On reception, the senders IPv6 address is identified toensure that the aggregated data is from the correct M2M device source.Then, the received data are scanned to ensure that the data packet iscomplete. This monitoring program performs its function continuously,monitoring not only biomedical signals, such as PPG signals and oxygensaturation data acquired by the wearable health sensors, but also infor-mation about the M2M devices, including current communication settingsand IPv6 addresses in real-time. All packets transmitted through theinternet are verified by the server monitoring program. It performs anaccurate recognition process even if the patient is unconscious. Havingreceived all transmitted packets via UDP communication, the monitoringprogram stores the values of the measured biomedical signals in a databaseand plots them dynamically. This monitoring program also displays therecorded values along with other relevant information, such as ECG andPPG waveforms, heart rate and the IPv6 addresses of the M2M node,M2M gateway and server IP. In addition, it queries the user to press but-tons to initiate or end monitoring activities. Moreover, it has the capacityto provide good accessibility to a client PC with an IPv4 address. Thus,any internet-connected client, such as a desktop, laptop or tablet PC, or amobile device, can access the measurement data, provided that they havea fixed IPv4 address

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Figure 6: Flowchart of the data process on the server [VII, published with thepermission of IEEE]

7 Results

7.1 Experimental setup

Practical tests were conducted to observe the performance of the personal M2Mhealthcare system and to evaluate its measurement and analysis methods. Tothat end, the designed hardware and software were subjected to experimentaltesting involving a global area network. Ten test subjects without heart diseasewere recruited for short-term tests lasting three minutes and long-term testslasting 30 minutes. They were asked to overwork and exert themselves to thepoint that they were physically stressed. Their physical responses were mea-sured in real-time using PPG and ECG sensors connected to M2M nodes. EachM2M node was connected to the wearable health shirt which recorded biomedi-cal signals from sensors placed on their bodies and further transferred the thuscollected data to the M2M gateway. Acting as the medium between the inter-net and 6LoWPAN networks, the M2M gateway delivered packets containingthe measured biomedical signals using UDP communication. The experimen-tal setup was based on an international scenario involving the University ofOulu in Finland and Pukyong National University in Korea. One 6LoWPANtest environment at the University of Oulu was set to measure ECG signals.These signals were sampled at 100 Hz using embedded ECG sensors and savedin buffers in 20-byte packets for wireless communication. A second 6LoWPANtest environment was set up in another location at the University of Oulu tomeasure PPG signals sampled at 75 Hz. The PPG sensor performed data ac-quisition with a packet size of 5 bytes contained PPG signals, SpO2 and HRvalues. Both wearable sensors were placed so as to be compatible with thehealthcare system on the subjects bodies. Each wearable sensor contained anM2M node with an IPv6 address for ID and high-gain ADC channels for highsignal precision. The main function of the M2M nodes was to collect the mea-sured biomedical signals from the wearable sensors and then forward the data to

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Figure 7: Structure of 6LoWPAN packets [V, published with the permission ofAmerican Scientific Publishers]

the server PC via the M2M gateway. Positioned in Pukyong National Univer-sity, Korea, the server PC was used to assess the test subjects health conditionand to monitor the performance of wavedependent data. Also information con-cerning 6LoWPAN networks was used to simulate real-time situations. UsingIP addresses, the server PC can accurately determine position data, even ifthe person being measured is in an unconscious state. This was done to in anattempt to create an environment that resembles the real situation at home,office or hospital, where patients may be stationary or moving about indoors oroutdoors within direct or indirect range of a 6LoWPAN network. One scenarioalso covered the case when data from a number of patients have to be accessedat the same time. Fig. 7 shows the structure of 6LoWPAN packets. Apart fromthe packet header, these packets comprise a total 29 bytes. The first two bytesare the node ID, which is needed to distinguish each individual 6LoWPAN node.The next two bytes illustrate sensor type, with 0x0060 denoting a PPG sensorand 0x0061 an ECG sensor. The remaining 25 bytes are the payload biomedicaldata

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8 Analysis and evaluation of health condition

Heart rate variability (HRV) is usually calculated by analyzing a time series ofbeat-to-beat or normal-to-normal (NN) intervals from ECG signals. Alterna-tively, they can be derived from pulse wave signals measured by PPG . Twomajor methods for the analysis of HRV signals are time domain analysis andfrequency domain analysis. In addition, HRV serves as an indicator of stress,since it is regulated predominantly by the autonomic nervous system (ANS),which regulates bodily functions without volition or consciousness. The ANSis affected by sympathetic and parasympathetic nervous systems: sympatheticnerves excite the heart, increasing heart rate, whereas parasympathetic nervesreduce it. By accurately measuring HRV, detected variations can be used todetermine psychological and physiological stress on the body.

Time domain analysis allows the extraction of fairly unstable signals, whichare indicative of how the body is always ready to respond external stimuli.Time domain analysis of HRV signals enables us to detect stress, fatigue anddrowsiness, based on conclusions drawn from the heart rate (HR) distributiongraph, the standard deviation of the NN interval (SDNN), the root mean squareof successive differences (RMSSD), the proportion derived by dividing NN50by the total number of NN intervals (pNN50) and the stress index (SI). Inexperiments conducted during this research, HR distribution displayed a rangeof reactions between normal state and stress state, as seen in Fig. 10. Normally,HR distribution tends to be spread widely and evenly, a telling sign that thesympathetic and parasympathetic nervous system of ANS operate accurately.On the other hand, when a person experiences stress, HR distribution tends tospread narrowly and centrally. This indicates that the parasympathetic nervoussystem is working harder than the sympathetic nervous system. A correlationbetween SDNN and RMSSD data serves to verify the different pattern for aperson in the normal state and in stress, as shown in Fig. 11. Both the SDNNand RMSSD factor is found from the mean value for SDNN (45.7 ms) andRMSSD (39.8 ms) in the normal state under resting condition. On the otherhand, in a state of stress, the SDNN and RMSSD factors exhibit clearly lowermean values (34.1 ms and 24.4 ms, respectively) than in the normal state. Theseresults, extracted from time domain analysis, are very interesting as they offera way of interpreting a persons health state. As a parameter of the mainmethod used in the histogram analysis, SI was employed to evaluate the stateof autonomic regulatory mechanisms .

SI = AMO / 2XMOThe mode (MO) represents the most frequent length of RR intervals and its

amplitude (AMO) corresponds to the maximum density of distribution at themaximum MO. In essence, AMO is the percentage of RR intervals that equalsthe MO. The variation range (AX = RRmax RRmin) can be calculated usingthe distribution density function, and RRmax and RRmin can be found at alevel of 2 percent of the AMO. SI characterizes the activity of the ANS andthe extent of centralization in cardiac rhythm regulation. This parameter issensitive to the strengthening of the tone of the sympathetic nervous system.The SI histogram serves as a reflection of the normal state of health of the bodyand provides some useful clues for medical diagnosis. Thus, the value of SIincreased from 87.2 to 109.6 under stress, and there was a significant differenceboth graphically and statistically. Time domain analysis extracted fairly uni-

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form and stable RR intervals in the stressed state reflecting activation of theparasympathetic nervous system. On the other hand, unstable RR intervalsin the normal state tended to indicate that the body was always ready to re-spond to stimuli from the external world. In frequency domain analysis focusingon negative emotions, such as stress, fatigue and drowsiness, power spectrumdensity (PSD) analysis provided basic information of power distribution as afunction of frequency. Three main spectral components are required for spec-trum power indicator analysis: very low frequency (VLF), low frequency (LF)and high frequency (HF) components. Measurement results obtained from PSDanalysis for VLF, LF and HF power components are usually given in absolutevalues. Presence of LF and HF power components emphasizes the controlledand balanced behaviour of the two branches of the ANS. Absolute values of LFand HF power components describe the total power distribution of spectral com-ponents. It turned out that the mean values of HF and LF power componentsdiffered somewhat in the normal state and in stress, as shown in Fig. 12. Basedon these results, the ratio of LF/HF was used to provide a health state assess-ment in both normal state (R = 2.49) and in stress (R = 3.63). summarizes theresults of a comparison of the mean values of HRV characteristics in time andfrequency domains The evaluation of health condition was mainly conducted byprocessing the HRV analysis, which is a simple yet powerful analysis methodin the time and frequency domain. The analysis results from the HRV analysiswere used as an indicator of ANS activities to reveal negative emotions suchas fatigue, stress, drowsiness, and so on. In addition, physiological processeswere realized with the autonomic balance diagram methodologies for high reli-ability of the biomedical signal processing. Most important health conditionssuch as cardio-related diseases and physiological states can be evaluated by theproposed methodologies using the results of health parameters. Thus, healthfeatures were used to evaluate health condition by comparing overall health pa-rameters in the normal and stressed states. Both states were indicated withsignificant difference to the values of health parameters. Detailed analysis ofhealth conditions will be studied for the more accurate and precise healthcareservices in the future.

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9 Mobile healthcare servige

Functionality of the mobile monitoring service was corroborated by a set of mon-itoring tests. These were carried out by running an Android emulator on theserver, before installing it on the cellphone. The Android SDK provides emula-tors that can mimic a variety of Android versions, screen dimensions and phonebehaviours. It runs a bit slower than a real phone, but provides efficient supportfor all features and lifecycle events, including persisting state, writing local files,etc. Android emulator tests were conducted on the monitoring application usingthe measured PPG signals, As shown in Fig. 13. After testing the monitoringapplication by the Android emulator, the monitoring application was installedon the Android mobile via a direct cable from a havebeeninstalledwirelessly fromthe Android Market store). For practical tests, a Samsung Galaxy S cellphone,based on the Android OS, was used to monitor biomedical signals, heart rateand blood oxygen saturation as well as the IPv6 address of the transmittingM2M node. Fig. 14 shows the designed monitoring application running on aSamsung Galaxy S mobile phone. This system samples PPG signals at the rateof 75 Hz, meaning that 15 data packets are sent within 1 second. As each datapacket comprises 5 PPG data points, a total of 75 PPG data points are sent ina second. Thanks to the large screen, the waveform display on this monitoringapplication provides a good graphic visualization forthe user wherever internetis properly supported

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10 Conclusion

This dissertation successfully implemented a personal M2M healthcare solu-tion using Android mobile devices in global networks with the help of the IPv6technique. Initially, motivation for the proposed system was discussed, by adescription of design challenges. A mobile healthcare sensing method was hy-pothesized, including wearable health sensors for chronic or recovering patientsto record changes in biomedical signals in an IP-enabled WSN. In addition,communication architecture for a personal M2M healthcare system relying on6LoWPAN and mobile techniques was introduced as a way of increasing thecoverage of the service. The proposed system presented key ideas for estab-lishing a 6LoWPAN network that provides efficient support for IPv6 over theIEEE 802.15.4 protocol for healthcare applications. Generally, biomedical sig-nals are ideal for predicting and monitoring the health condition of a patient.In the proposed system, two biomedical signals, ECG and PPG, were dedicatedto carrying out analysis methodologies. Briefly, the ECG signal produces arecord of the electrical activity of the heart over time, while the PPG signal isthe measurement of light absorption to monitor blood volume within periph-eral skin tissue. Wearable health sensors and M2M devices were designed forthe measurement of ECG and PPG signals and for the wireless transmission ofmeasurement data to a server PC through IP-enabled internet. Thus, the pro-posed M2M healthcare system can be globally applied to improve accessibilityvia flexible networks and scalability by the use of multiple M2M gateways con-nected to comfortable wearable sensors. Several methodologies were brought tobear on the analysis of biomedical signals to enable assessment of stress. Amongthe studied parameters were HR, HRV, SDNN, RMSSD, pNN50, and SI (timedomain) as well as VLF, LF, HF, ratio of LF/HF, ln LF and ln HF (frequencydomain). By monitoring the activation of ANS under different conditions, it waspossible to identify when a person was in a state of stress, which was verifiedby a comparison with normal state. Results from HRV analysis were used asan indicator of ANS activity to reveal negative emotions, such as fatigue, stressand drowsiness. However, in this dissertation, identification of health conditionswas limited to stress, as normal relaxed state provides a good basis for com-parison. Thus, the assessment of health condition relied on comparing overallhealth parameters in normal state and stress state. Based on these results, bothstates showed significant differences in recorded parameter values.

The Android mobile healthcare application is easy to port to other mobiledevices, such as smartphones, tablet PCs and laptops and is capable of real-time monitoring of biomedical signals. To access mobile healthcare servicesbased on the Android mobile healthcare application within internet coverage,an Android mobile phone was selected. Thus, a Samsung Galaxy S model smart-phone was used to monitor the 100 Hz ECG signal and 75 Hz PPG signal withthe analyzed health features. The mobile healthcare system provided usefulinformation on signal display, sensor type, data bytes, IPv6 address and healthfeatures. A state of stress produces a higher ratio of LF/HF in the ECG signaland a higher stress index in the PPG signal. Combining 6LoWPAN and mobilecommunication techniques allowed an extreme network connectivity extensionoffering a higher accessibility of M2M devices for seeking better achievementsof user-oriented healthcare services with maximum mobility and flexibility tothe patients who are in urgent need of such services. Harnessing a range of

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technologies makes it possible and feasible to create a more advanced mobilehealthcare system through the use of existing infrastructures to assure the ar-ranged research questions.

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