Mobile Health

Managing wandering behavior of people with dementia (PWD) has become increasingly imperative for these reasons: its high prevalence (60%) among PWD, its negative outcomes such as falls or elopement, and its burden on caregivers. In this chapter, we discuss the emergence of sensors, techniques, and applications for managing wandering behavior of PWD. First, we briefly present the 5Ws1H (WHO, WHAT, WHERE, WHEN, WHY, HOW) conceptual map of wandering science including stakeholders (WHO), measurements of wandering (WHAT), environments in which wandering takes place (WHERE), detection of wandering (WHEN), causes of wandering (WHY), interventions of wandering (HOW). Second, we introduce a framework that identifies specific groups of mHealth and eHealth assistive technologies for managing dementia-related wandering. Third, we review existing technological works that address these 4 domains in the 5Ws1H conceptual map: WHAT-WHERE-WHY-HOW. In particular, we explore mHealth sensors to geo-fence and prevent elopement, mHealth devices to track and locate PWD who wander, information services to assist caregivers, eHealth tools to measure dimensions of dementia-related wandering, and mHealth tools that analyze proximal factors as well as study background factors. Based on this review, we further discuss research and design issues, human factors, ethics, security and privacy that need to be considered when implementing mHealth applications for wandering management. We conclude the chapter by highlighting the future research work in this area.

A mobile health application solution with biofeedback based on body sensors is very useful to perform a data collection for diagnosis in patients whose clinical conditions are not favorable. This system allows comfort, mobility, and efficiency in all the process of data collection providing more confidence and operability. Falls occurrence in senior people represents a high risk for their health and life. Those falls can cause fractures or injuries causing great dependence and debilitation to the elderly and even death in extreme cases. Falls can be detected by the accelerometer included in most of the available mobile devices. To reverse this tendency, it can be obtained more accurate data for patients monitoring from body sensors attached to the human body (such as, electrocardiogram, electromyography, blood volume pressure, electro dermal activity, or galvanic skin response). Then, this chapter reviews the related literature about this topic and introduces a mobile solution for falls prevention and detection, and biofeedback monitoring. The proposed system collects sensed data that is sent to a smartphone or tablet through Bluetooth. Mobile devices are used to process and display information graphically to users. The falls prevention system uses collected data from sensors in order to control and advice the patient (user) or even to give instructions to treat an abnormal condition to reduce the falls risk. In cases of symptoms even detect a possible disease. The signal processing algorithms plays a key role in the fall prevention system. In real time, these algorithms process the collected biofeedback data in order to extract relevant information from the signals and thereby warn the patient. Monitoring and processing data from sensors is realized by a smartphone that will send warnings to the user. All the process is performed in real time. The proposed system is evaluated, demonstrated, and validated through a prototype and it is ready for use.

Why investigate depression and schizophrenia during sleep? Because studying spontaneous neural activity during sleep minimises factors related to waking such as attention, cognitive behaviour and any presence of active symptoms. Sleep architecture also activates particular neural networks and interacts with cardiovascular activity, which can be compared between depressed and schizophrenic patients, healthy and sleep disorders.

Sleep disorders, such as insomnia can seriously impair a patient’s quality of life. Existing studies have shown that insomniacs have a risk of hypertension 350 percent higher than normal sleepers. Insomnia is also a risk factor for diabetes, as well as anxiety and depression. Sleep measurements based on polysomnographic (PSG) signals and questionnaires are necessary for an accurate evaluation of insomnia; however PSG systems are uncomfortable and inconvenient as they require patients to stay overnight at sleep centers. There is an increasing interest in portable devices, which provide the opportunity for the assessment of insomnia in a native environment (e.g. patients’ homes). Due to recent advances in technology, it is now possible to continuously monitor a patient’s sleep at home and send their sleep data to a remote clinical back-end system for analysis and reporting. This chapter provides a systematic analysis on the sleep monitoring technologies that can be used for insomnia assessment and treatment. This study highlights the key technical challenges of sleep monitoring, describes different types of technologies and discusses their applications in insomnia assessment. An overview of some model-based signal processing for sleep staging and insomnia detection is presented. Lastly, this chapter ends with a discussion, which provides future directions for the deployment of effective in-home patient monitoring systems for insomnia diagnosis.

This chapter presents the current status and future directions of mHealth technologies in sleep medicine. The focus is on insomnia case as one of the most common reported sleep disorders with significant consequences on daytime functionality, long-term health and economy. Recent advancements and existing wireless technologies for sleep disorder monitoring, diagnosis and treatment from the two perspectives of smartphone applications level and wireless physiological sensor level have been investigated and described. Key challenges and limitations of the existing technologies from health and medical application perspective have been explained. Moreover, the trend and future directions of mHealth and wireless physiological sensor technologies and the need for the convergence of the two levels of smartphone and physiological sensing for effective and efficient future wireless sleep medicine technology is presented.

The synergy of communication, computation and sensing capabilities in mobile systems-on-chip artifacts such as smartphones has made possible the development of wearable smart sensor systems for user activity monitoring and recognition. Assessing physical activity is useful to enhance people health as experts have evidenced a clear correlation between physical activity and overweight, obesity and metabolism-related syndromes. Due to user acceptability and the convenient nonintrusive manner for measuring data, smartphones have the advantage of taking proprioceptive motion measurements outside of a controlled environment for rather long periods of time using embedded sensors such as the accelerometer, however, activity recognition poses several challenges. Particularly, though work has been reported for accelerometer-based activity recognition using smartphones, the portability of the device to a single fixed tight position has been a major constraint to easy the interpretation of the collected data on resource-limited devices. In this chapter, a human activity hierarchical recognition system based on neural networks without the need of the smartphone to be constrained to a single fixed position is presented. Yet it is used as a representative example to show the challenges, the role and some of the main potential impacts that smartphones have and will have in mHealth. Experimental results on Android-capable smartphones on four on-body locations show that the recognition system achieves high classification rates, above 92%, for five activities including static, walking, running, and up-down stairs walking, which outperforms other proposals. The system is fully implemented in a smartphone running continuously in near real-time with reduced power consumption in a proof-of-concept client-server application for mHealth.

The implementation of mHealth monitoring systems is attracting increasing attention in academia and industry due to rising healthcare costs and the aging of the world population. However, the problem of failure detection and management in mHealth monitoring systems is becoming more and more critical and the use of wireless technologies and the adoption of commodity hardware/software platforms pose new challenges in terms of their correct functioning.Wireless channels can be affected by packet loss and cheap and wireless-enabled medical devices can exhibit wrong readings and temporary disconnections inducing medical staff to take wrong decisions.

In this chapter, we present an approach, relying on an event-based formalism, for the dependability assessment of mHealth monitoring systems. In particular, after a detailed failure modes and effects analysis, we identify some main critical events that could undermine the dependability of a Body Area Network (BAN) which is the most critical component of a mHealth monitoring system. Finally an application scenario concludes the chapter.

According to the World Health Organization, around the world there are more than 785 million people with a disability. This phenomenon is produced to a great extent by the aging of the population and the increase in chronic diseases. These patients may be affected physically and/or cognitively, requiring nursing care and/or family assistance to perform daily activities. Bringing new tools to families and professional caregivers to improve the care of these patients is essential to increase the quality of life for both the disabled people and the caregivers. Recent advances in sensors, wireless communication systems and information technologies make possible the development of portable and wearable systems to monitor mobility impaired patients continuously during daily activities. Collecting vital signs, patient activity and ambient conditions allow the patient’s health status to be assessed, providing an extra level of safety in cases of emergency. Also this information is useful for clinicians to manage treatment and rehabilitation therapies. However, the main challenge is to acquire this information unobtrusively, with a minimal impact on patients’ daily life. To this end, new ways of collecting physiological information are needed.

This chapter presents analysis of emerging mHealth applications as well as the exploration of novel trends supporting healthcare intelligent environments assisted by mobile devices. The case of study is mHealth and remote vital sign monitoring. Particularly, we present a methodology for recollecting, processing and real-time monitoring heart activity with the main purpose to interpret electrocardiogram ECG signals, detect and manage situations of risk and provide the interaction between medical practitioner and patient into smart healthcare environment. The proposed architecture and approach provide continuous detection and interpretation of the patient’s QRS complex. The challenge is to adapt some approaches for data gathering, processing, compression, storage, analysis, and visualization to capabilities of mobile devices. The designed system for monitoring vital signals has been tested using standard MIT-BIH Arrhythmia Database achieving satisfactory ECG interpretation accuracy with relative error in range from 4 % to 10 % for signal sampling frequency of 360 and 128 samples per second respectively. It is important to note that the proposed prototype does not substitute diagnosis by physician. Our intention is to propose methodologies that serve as guide for development of complex health assistance tool expanding coverage of medical services.

One of the main concerns from Government Bodies, Health Authorities and the General Public is to assure the sustainability of the Health Care Systems [1]. In this context, Ambient Assisted Living is becoming an alternative to traditional methods of assistance that requires the presence of specialized medical staff. In this context, wireless technologies are one of the resources which enable these services [2-3]. The high mobility, ergonomics, moderate cost and interconnectivity are the main factors in the successful use of wireless systems in Electronic Health (eHealth) and Mobile Health (mHealth) implementations [4].

The evolution of medical equipment and health care involves the miniaturization and autonomy of devices that are responsible for medical monitoring, screening and even therapeutic actions.

The latest generation of smartphones is increasingly being considered as handheld computers rather than as cell phones, due to their powerful on-board computing capability, capacious memories, large screens and open operating systems that encourage new application development. Recent medical applications for smartphones, such as the ones based on Android, Apple iOS, BlackBerry OS, Symbian, and Windows Phone, can be highlighted.

In this chapter, a review on this thematic is presented, and some of the most relevant projects in the monitoring and training lifestyles framework, such as a physical activity application, a platform for motivating behavior change, and an application to detect moments of stress using georeferencing.

The development of smartphone platforms and applications allows the creation of portable solutions for personal aid that are accessible anywhere and, most important, easily accepted by the user.

The introduction of smart phones has redefined the usage of mobile phones in the communications world. Smart phones are now equipped with various sophisticated features such as Wi-Fi, Global Positioning System (GPS) navigation capability, high-resolution camera, touch screen, and broadband access which helps the mobile phone users to stay connected and updated. Many of these features are primarily integrated with the mobile operating system and are out of reach of the public; that is the users can not manipulate these features. However, this situation has changed with the advent of an innovative, user friendly and versatile operating system known as “Android”. It was developed by the Open Handset Alliance, an open source initiative piloted by Google. The open system architecture with customizable third party development and debugging environment features help the users to manipulate the features and create their own customizable applications.

In this chapter, we propose a location-aware remote system using Google’s Andriod mobile platform for safety, emergency and health scenarios and develop an Andriod application for this purpose. Emergency is divided into three categories; heart abnormalities, security threats, and road accidents. The first scenario we investigated is targeted towards heart abnormalities monitoring. The heart rate monitoring device is integrated with the proposed application to measure the heart rate of a user driving a vehicle, and if abnormalities are detected the application performs a dual role. The application uses GPS to track the location information of the user and sends this information via SMS, email and posts it on the user’s Facebook wall. Simultaneously, an emergency signal is sent to an Arduino microcontroller which will in turn trigger an alarm in the vehicle to notify the emergency situation. The second scenario is targeted towards road accidents. The proposed application is designed to detect that an accident has occurred using the sensors in the Andriod mobile phone and, consequently, an action is taken by the application to notify the emergency services that an accident has occurred and its location. Finally, the third scenario we investigated is security threats which can occur anywhere and the proposed application can also offer assistance in personal safety situations. In this case, the user interacts with the application by pressing a designated button and the application will automatically calculate the geographical information and send the location information via SMS and email to a pre-stored emergency contact, and also post the information on the user’s Facebook wall.

The mobile healthcare (mHealth) applications are becoming increasingly important in monitoring and delivery of healthcare interventions. They are often considered as pocket computers, due to their advanced computing features and diverse capabilities. Their sophisticated sensors and advanced software applications make mHealth based applications more feasible and innovative. Advanced engineering, communication and information technologies combined with medical and clinical knowledge enable the possibility of remote, wireless, continuous monitoring of physiological parameters. These technologies facilitate the implementation of mHealth based patient monitoring and diagnostic systems virtually anywhere: home, hospital and outdoors (on the move). The proposed mHealth vital sign monitoring system in this chapter is aimed to help clinicians by illustrating the trace of critical physiological parameters, generating early warning/alerts and indicating any significant changes to the data. The system was validated with different set of collected data from 20 hospitalised older adults and achieved an accuracy of 95.83%, sensitivity of 100%, specificity of 93.15%, and predictability of 90.38% in compare with a clinician assessment for tachycardia, hypertension, hypotension, hypoxemia and hypothermia. Another important aspect of this chapter is to investigate challenges and critical issues related to the use of such applications in healthcare including reliability, efficiency, mobile phone platform variability, cost effectiveness, energy usage, user interface, quality of medical data, and security and privacy.

Emergence of the Internet and widespread use of mobile computing have brought traditional eHealth beyond the boundary of the clinical setting, evolving to mHealth which is patient-centered and ubiquitous. Faced with the world’s rapidly ageing population and its burden on the healthcare system, one of the intense areas of development in mHealth is continuous patient monitoring. It requires careful integration of wearable sensors and wireless body sensor networks. Unlike traditional ambulatory monitors, sensors for mHealth may appear in various forms, such as watches, jewelry, eyewear, and even smart garments. Recent work have focused on design for wearability, alternative sensing techniques, and mHealth-specific network topologies.

Background

: Lately, the world’s biggest Professional Services company has recognized that mHealth is enabling and accelerating three major global trends in healthcare:

regulatory reform driven by demographic changes

,

industrialization of the healthcare sector

and

personalized medicine

. They have identified interoperability as a key enabler of scalable mHealth. Continua Health Alliance is a non-profit organization that globally certifies mobile health solutions. They publish interoperability guidelines for connecting health related devices. Unfortunately, the adoption of these guidelines has not been consistent.

Problem

: We want to define a platform and a framework to support the development of different e&m-health interventions for various domains. The solution has to support sharing of information between interventions within the system and also with external systems in a standardized way.

Methodology

: In the chapter we describe an approach towards interoperability that was achieved in several different aspects. First, the enterprise interoperability is supported by using IHE profiles.. Second, organizational interoperability is supported by use of BPMN2; and third, the semantic interoperability is supported by using OpenEHR, MLHIM and standard vocabularies.

Results

: We built the platform that permits iterative incremental development process of new interventions. The developed interventions are fully interoperable within e&m-health environments. On the platform we deployed five different interventions that were also clinically tested. In this chapter we describe eDiabetes intervention in greater detail. Besides, we give guidelines on how to develop and deploy a new intervention.

In recent years important advances have been made in the development of health care systems in specific areas owing to different uses and applications. One of the foremost advancements is the so-called mobile Health (mHealth), due to its potential social and economic impact. Within this scope, several technologies – such as smart phones and ultra-slim tablet-PCs, as well as low-voltage and low-power wireless sensor networks – provide telemonitoring services at home and ubiquitous support when they are strongly integrated. Therefore, the integration of international interoperability standards is, nevertheless, required for a regulated transmission of health information in mHealth scenarios. However, such implementation tends to increase the processing load and battery size in order to ensure its autonomy. To tackle this problem, different strategies are being tested in this set of mobile technologies which are aimed at providing a solution to such critical aspects. Another important aspect that should be addressed is the management of communication between different parts that constitutes the system. The new communication capabilities of smart phones enable the implementation of new techniques that improve data transmission from the mobile system to the telemonitoring center, facilitating the integration of Personal Health Devices (PHDs) with telemonitoring systems. This makes the system more accessible to the user.

This work highlights some new features of X73PHD and the benefits of using them. In addition, current needs to enhance the operational capacity of the different specializations of X73PHD are also shown, which include the definition of new remote command and control functionalities. These new management functions are being developed in order to allow any X73PHD-compliant manager to modify the operation of any X73PHD-compliant agent. This functionality could expand the technological boundaries of interoperable mHealth to healthcare services and improve Quality of Service (QoS) by providing remote configuration of medical devices to health professionals. The definition of this a new package extension for X73PHD involves the analysis of previous studies and related use cases, work which is currently being carried out within the PHD Working Group (PHD-WG), in which the authors are actively collaborating. Future lines include the proposal and adoption of the new extension remote command and control package, along with its application to other specializations.

Mobile patient monitoring has been widely recognized as a viable solution to improve the quality of patient care and decrease health care costs. Depending on the mobile patient monitoring scenario and the patient pathology, it may be necessary to monitor different bio-physiological variables by means of specialized sensors. The information collected by these sensors is sent to the healthcare provider for its analysis, processing, and storage. Thus, the collected data can be used for remote diagnosis, ambulatory patient monitoring and to trigger emergency services. In mobile patient monitoring systems it is common to use wireless network technologies to transmit the data generated by the sensors, thus defining a wireless sensor network (WSN). The implementation of the WSN is not a trivial task, as different application scenarios have different requirements in terms of the number of variables to monitor, the number of supported users, mobility, data loss tolerance, power consumption, and coverage range, among others. All these requirements must be considered when designing the WSN architecture to be used in a particular mobile patient monitoring system.

There are several network architectures and wireless technologies that can be used for the implementation of WSNs in mobile patient monitoring scenarios. Thus, the main wireless technologies that can be used for this purpose are reviewed within this chapter. Then, the advantages and disadvantages offered by homogeneous and heterogeneous WSN architectures are discussed and analyzed by comparing performance metrics for a particular application scenario. Lastly, several guidelines for the proper design of WSN architectures in mHealth mobile patient monitoring scenarios are provided.

The rapid development and use of information and communication technologies in the last two decades has influenced a dramatic transformation of public health and health care, changing the roles of the health care support systems and services. Recent trends in health care support systems are focused on developing patient-centric pervasive environments and the use of mobile devices and technologies in medical monitoring and health care systems [1].

Vehicle accidents resulting in trauma occupy the fourth place as a cause of death, in middle and low-income countries and the tenth cause of death worldwide. Paramedics, physicians, and emergency room staff coincide that in order to decrease the possibility of a patient suffering an irreversible damage, qualified medical attention must be given within the first hour of the accident (the so called golden hour). However, first-response emergency workers in low and middle income countries are mostly volunteers with minimal medical training; additionally, there is a shortage of these first aid responders worldwide. In this chapter we show that, by using pervasive and ubiquitous information technology currently available, it is possible to alleviate part of the problems stated above. In particular we include the use of mobile devices, wireless communication and remote body health sensors to build a system that allows the delivery of professional medical assistance in emergency situations. In the chapter we present and discuss the

software architecture

for assisting paramedics while attending victims of traffic accidents. Our discussion is based on a project that we have developed called ERPHA (Emergency Remote Pre-Hospital Assistance).

In this chapter, we introduce the state-of-the-art of medical oriented portable devices and smartphones that are capable of communicating with sensors and healthcare providers effectively. In this chapter, we propose a typical model for the infrastructure of smartphone-based mHealth service. This infrastructure includes hardware, software, wireless protocols, and collaborative applications. The current smartphone operating systems included in this chapter are: Android (Google), iOS (Apple), Symbian (Nokia), Windows Mobile (Microsoft), and QNX (BlackBerry). A dedicated section is provided on the amalgamation of wireless equipment with smartphone to provide specific mHealth service. Examples on such devices are the Glucophone which is an integrated cell phone-glucometer system and the Cellscope which takes a standard cell phone and transforms it into a compact, high-resolution, handheld microscope. The proposed infrastructure is efficient and cost effective.

The term mobile health (mHealth) means to adopt Information and Communication Technology (ICT) especially mobile hand held devices to provide health-care and delivery services[1]. The ICT is mainly adopted with an intention to provide cost-effective, efficient and easily accessible health care services. Patient monitoring is one of the health care activities that can effectively adopt mobile devices and allied techniques for chronic disease management and continuous patient monitoring. The Mobile Patient Monitoring (MPM) systems have promise and potential to provide as an effective alternative for traditional patient monitoring techniques.

Portable, wearable and networked medical devices for the diagnosis, monitoring, and treatment of diseases are vital and desired for future mHealth applications. For the pursuit of convenience, low power and low cost, the system of mHealth instrumentations should be developed as small as possible and the power consumption should be the lower the better, therefore; integrated circuits (ICs) designed for biomedical fields are necessary in these mHealth applications.

The key challenge in designing biomedical ICs is the pursuit of low frequency, low noise, and low power, and thus, there is a tradeoff between noise and power, and tradeoff between area, cost and bandwidth, because: 1) biomedical signals are usually low frequency, week and noisy, and 2) the relevant biomedical devices should be wearable and portable in the practical applications.

In this chapter, the recent low power, low noise and low frequency IC design technologies which have been used in multi-parameters, human-body signal monitoring and processing are introduced. These technologies are suitable for biomedical and mHealth applications including the design of analog circuits such as amplifiers and filters, digital circuits, and radio-frequency (RF) circuits which are widely used in physiological signal acquisition, signal processing and in the signal transmission respectively. In the last, some synthetic systems are introduced.

This chapter presents a secure mHealth application which is based on hybrid cloud architecture combined with cryptographic techniques to protect data privacy, integrity and security of patients and health care givers and with role based access control to authenticate and authorize cloud users. Hybrid cloud platform combines the advantages of both the private cloud which guarantees privacy and safety of data and the public cloud which provides a platform for reduced services costs. Integrating cryptography and role based access control with hybrid cloud computing ensures the safety of patients’ medical records and enables user authentication and authorization for access control. This integrated technology can provide mHealth care the required safety and privacy to flourish.

Owing to the latest technologies in wireless communication and the development of mobile devices, related issues in mobile computing are becoming more and more concerned [1-2]. However, it is challenging to run very complex applications on the mobile devices because of the strict constraints on their resources such as memory capacity, network bandwidth, CPU speed and battery power [3].

Significant technological advances over the past decade have led mHealth systems and services to a remarkable growth. It is anticipated that such systems and services will soon be established in standard clinical practice. MHealth medical video communication systems progression has been primarily driven by associated advances in video coding and wireless networks technologies. Responsive, reliable, and of high-diagnostic quality systems are now feasible. Such systems build on compression ratios and error resilience tools found in current state-of-the-art video coding standards, linked with low-delay and high-bandwidth communications facilitated by new wireless systems. To achieve this however, these systems need to be diagnostically driven. In other words, both encoding and transmission need to adapt to the underlying medical video modality’s properties, for maximizing the communicated video’s clinical capacity. Moreover, the proper mechanisms should be developed that will guarantee the quality of the transmitted clinical content. Current video quality assessment (VQA) algorithms are unsuccessful to replicating clinical evaluation performed by the relevant medical experts. Clearly, there is a demand for new clinical VQA (c-VQA) metrics.

This chapter reviews medical video communication systems. It highlights past approaches and focuses on current design trends and future challenges. It provides an insight to the most prevailing diagnostically driven concepts and the challenges associated with each system component, including pre-processing, encoding, wireless transmission, and quality assessment. It discusses how exploiting high efficiency video coding (HEVC) standard, together with the emergence of 4G and beyond wireless networks, is expected to deliver the mHealth medical video communication systems that will rival in-hospital examinations. The latter, linked with c-VQA that will correlate with clinical ratings is expected to aid the adoption of such systems and services in daily clinical practice.

The healthcare industry suffers from a high degree of fragmentation which results in inefficiencies. It is widely recognized that the use of Information and Communication Technology (ICT) systems in health care has the potential to improve healthcare systems’ efficiencies. The use of ICT in health care is referred as electronic health (eHealth). Four key steps are deemed essential for eHealth to improve healthcare delivery.

First, the healthcare industry should adopt ICT systems that can allow effective health information management. Health ICT systems, such as Electronic Health Record (EHR), Electronic Medical Record (EMR), and Personal Health Record (PHR) can considerably improve the delivery of medical services.

Second, with today’s increase of health consumerism, the integration of PHR and EHR systems is needed to facilitate physician-patient partnership and support patients’ self-management.

Third, for ICT systems to improve healthcare delivery, they should provide healthcare providers and patients with uninterrupted services and timely access to patients’ records. Distance-irrelevant and mobile connectivity systems are required to make relevant health information readily available to healthcare stakeholders irrespective of geography and time.

Fourth, to further improve healthcare delivery, eHealth should be supported by mobile technologies. This practice is referred to as mobile health.

Today, nearly six billion individuals across the world use mobile devices. The commercial wireless signals coverage is increasing. Thus, mobile health can significantly improve healthcare delivery - in terms of health costs containment and patients’ access to care. For this to happen, several obstacles to the mobile adoption must be overcome.

Terahertz refers to the electromagnetic waves with the frequency range between millimeter-wave and infrared, approximately from 300 GHz up to 10 THz. The THz spectrum, also known as the “Terahertz gap” is the last portion of the electromagnetic spectrum which has not been fully explored and exploited. Terahertz technology [1,2] is a fast-growing field with applications in biology and Medicine [3], medical imaging [4], material spectroscopy and sensing [5], security, monitoring and spectroscopy in pharmaceutical industry, and high-data-rate communications.

Recent research has explored how mobile networks are revolutionizing multiple aspects of healthcare in both developing and developed countries. As an area of innovation with the potential to make a huge difference, mHealth involves the utilization of mobile communication technologies to deliver healthcare services, such as SMS alerts that remind patients to take their prescription drugs at the appropriate time, remote diagnosis and even treatment for patients who do not have easy access to physicians, remote health monitoring devices that track and report patients’ conditions, and scheduling drug delivery. Such services could have a major impact on healthcare outcomes and costs. Decision making in mHealth drug delivery systems is complex. Decisions involve satisficing multiple goals regarding customer service quality, service cost, and healthcare worker satisfaction. With the increasing world-wide need for effective home healthcare, the increasing elderly population, and the increasing pressure from governments and other stakeholders, developing effective approaches for mHealth drug delivery decisions is imperative. In this paper, we present a multi-agent architecture that facilitates decision making in mHealth drug delivery system. The approach integrates the capabilities of a multi-agent system and Web services so as to facilitate effective decisions for home healthcare services. The aim is to provide a multi-agent system, where decisions are based on intelligent agents that provide quick and intelligent alternative decisions in a dynamic environment.

With the advancement of telemedicine systems, health information can now be transferred as interactive video over wireless networks. The key features of a mobile wireless network based system are portability, long battery life, ease of use, full duplex support, and optional encrypted communication (Pattichis, 2002). Rapid advances in wireless and mobile communication technologies have led to new categories of services to support healthcare. Telemedicine can be used to deliver healthcare and medical information remotely using wired or wireless telecommunication technologies.

Link adaptation plays a unique role to handle uncertainties and variations while delivering emerging mHealth services in mobile networks. In terms of typical link adaptation strategies, AMC (Adaptive Modulation and Coding) is designed to maximize spectrum efficiency in the physical layer, and HARQ (Hybrid Automatic Repeat reQuest) is conceived for enhancing transmission reliability in the combination of physical and upper layers. In reality, the fundamental goal of a link adaptation technique should utilize just-enough radio resources to deliver QoS (Quality of Service)-benchmarked services, since the radio resources are always limited in any mobile networks, and QoS requirements are the baseline of any kinds of mobile services. Motivated by this fundamental goal, we propose a novel cross-layer link adaptation to integrate AMC and HARQ mechanisms, in which they are sharing link feedback information by a CQI-Judgement support mechanism. To be specific, this proposal is a cross-layer combination of transmission reliability inspection and transmission pattern recognition, in which AMC is a coarse self-adaptive mechanism and HARQ is applied for the fine self-adaptation. To facilitate this proposal, we conceive an implementation scheme to realize the CQI-Judgement support mechanism, namely, BLER-oriented WMS (Weighted Mean Scheme) judgement, and the advanced version by using NLMS (Normalized Least Mean Square) model with occasional channel measurement. System-level simulation experiments demonstrate that the proposal can finely tailor just-enough radio resources to accommodate QoS-benchmarked mHealth services in LTE-Advanced mobile networks. Results show that the proposal can obtain gains 8.4% in terms of system throughput. Obviously, this proposal is open to integrate any potential advances in the future, for example, ARQ in the RLC (Radio Link Control) layer, power control in the physical layer, etc.

Telemedicine services represent restrictive applications mostly related to patient’s consultation, diagnosis and monitoring through their biomedical signals by health specialists. Since wireless telemedicine services depend on technological infrastructure, rural area coverage has been considered an important challenge, and the Institute of Electrical and Electronics Engineers (IEEE) 802.16/WiMAX standard has been used to provide broadband wireless access (BWA) because of its MAC and PHY characteristics. However, the switch-off of the analog terrestrial network presents the opportunity of delivering high data rates over large coverage areas by means of TV white spaces (TVWS) technology using cognitive radio capabilities. Hence, at the end of 2011 the IEEE Working Group for Wireless Regional Area Networks (WRAN) released the first TVWS standard named 802.22. Within this standard bandwidth availability depends on the geographical location of the base station (BS) and the customer premise equipment (CPE).Therefore, a model to evaluate the suitability of IEEE 802.22 and WiMAX for the deployment of rural telemedicine networks is proposed. The model considers specific traffic profiles based on the telemedicine services that will be offered over the rural wireless telemedicine network. The evaluation presented in this chapter is performed by calculating the number of telemedicine services that IEEE 802.22/WRAN and IEEE 802.16/WiMAX networks can support considering the available bandwidth and the telemedicine traffic profiles requirements. Frame preambles and a MAC/PHY overhead factor per active connection are considered within the analysis. A case study from the State of Chiapas, Mexico, is presented for the deployment of wireless rural telemedicine networks based on the IEEE 802.16/WiMAX and IEEE 802.22/WRAN standards.

Recent advances in wireless networked systems, intelligent low-power sensors and medical sensors, have led to the development and emergence of new embedded networks in the last years known as Wireless Body Area Networks (WBANs). These WBANs carry the promise of expanding the quality of life and care across a large variety of healthcare applications. In this chapter, we will review two fundamental mechanisms of WBANs including data dissemination and sensor deployment. A bi-objective nonlinear non-convex model based on a Min-Max formulation is proposed for deployment issue. On the other hand, a trade-off between energy consumption and the number of hops in the network was proposed for the purpose of data dissemination. The common objective of these two main proposals is saving energy and hence increasing network lifetime.

The study of the motion of individuals allows to gather relevant information on a person status, to be used in several fields (e.g., medical, sport, and entertainment). Over the past decade, the research activity in motion capture has benefited from the progress of portable and mobile sensors, paving the way toward the use of motion capture techniques in mHealth applications (e.g., remote monitoring of patients, and telerehabilitation). Indeed, even if the optical motion capture, which typically relies on a set of fixed cameras and body-worn reflecting markers, is generally perceived as the standard reference approach, other motion capture techniques, such as radio and inertial, are attracting an increasing attention because of their suitability in remote mHealth applications.

Moreover, several hybrid approaches have been studied and proposed in order to overcome the limitations of component technologies considered independently. In this chapter, we present an overview of possible integration strategies between radio and inertial motion capture techniques. We start by investigating a radio-based approach, based on the fingerprinting radio localization technique. Then, the previous approach is improved by integrating inertial measurements: namely, accelerometers are used to provide an estimate of the nodes’ pitches. Finally, the radio signals are abandoned in favor of only inertialmeasurements (obtained through accelerometers, gyroscopes, and magnetometers). The advantages and limitations of all approaches are discussed in a comparative way, characterizing the similarities and differences between the various approaches.

Today’s healthcare systems are facing major challenges generated by the increasing prevalence of chronic diseases. Telehealth services appear as promising approaches to attenuate those challenges by changing the way of delivering healthcare services. Telehealth is based on the inclusion of the last unutilized resource that has to be brought into the process of healthcare, the patient him/herself [1].

Radio Frequency Identification (RFID) enables automatic identification of objects using radio waves. The identified objects can be in and out of the line of sight and there is no need for physical contact with them. RFID technology is deployed in a wide range of industries such as supply chain management, inventory control, farming (to track animals), e-Passports, the tracking of humans (in prisons and hospitals) and in healthcare [1]. The three key elements of an RFID system are the tags, readers and the backend server. Tags are devices physically attached to objects and readers (wired or mobile) recognize the presence of objects in its range.

Wireless body area networks (WBANs) are emerging as important networks, applicable in various fields. In this chapter a comprehensive survey of WBANs that are designed for applications in healthcare is presented. The survey consists of stand-alone sections focusing on important aspects of WBANs. Topics covered are: monitoring and sensing, power efficient protocols, physical layer, MAC protocols, routing, system architectures and security. The chapter concludes with a discussion of open research issues, their potential solutions and future trends.

In recent years, in addition to fast development and maturity of information and communication technology, personal smart mobile devices (e.g., smartphones, phablets, tablets) have been becoming more and more popular. According to statistics in 2013, more than 50% of mobile consumers in United States and Canada have smartphones [1]. This number keeps increasing due to the fact that smartphones have been equipped with more powerful microprocessors that are capable of supporting more advanced operating systems and applications, larger and higher-resolution touch displays, higher capacity batteries, etc., yet at more affordable prices. Therefore, these devices are expected to have a wider adoption in electronic healthcare (eHealth). They can be integrated into patient interactions, electronic health record (EHR), and many other aspects of medicine. In other words, mobile-health (mHealth) has emerged as an important segment of eHealth. By definition, it is the delivery of healthcare services via mobile communication devices.

With recent advances in wireless networking, easy-of-use sensors, low-power microelectronics, and embedded computing technologies, wireless sensor networks (WSNs) have many applications in mHealth, such as wireless body area networks (WBANs), and wireless body sensor networks (WBSNs), which are viewed as a vital sign perception and transmission unit for the health internet of things. However, as for the battery-powered wearable or implantable sensor nodes in the WBAN, energy consumption has been the biggest bottleneck in the long-term work. Because of the medical business diversity, service differentiation, and environmental dynamics, it would make the system energy consumption increase if the traditional fixed communication mode is still applied to WSNs. In this chapter, a system level energy consumption model is constructed and analyzed for WSNs.

On the other hand, Quality of Experience (QoE) is proposed to evaluate user’s overall satisfaction with the network system and services. For mHealth systems, improving QoE means starting from user’s view to make patients and doctors (the main users of mHealth system who are especially captious to services related to health) to achieve best satisfaction. In this chapter, we also proposed an improved model for QoE of mHealth systems based on energy consumption and information integrity received by the users, and then explore on how the users’ satisfaction with mHealth systems’ energy consumption and received information integrity by exponential formula, which is influenced by the quantity of the transmitted information. Finally, from the results of simulation, we concluded that the appropriate compression of information quantity is the best way to improve QoE performance of the mHealth system.

Wireless communication technologies are an essential part of the broadband infrastructure of countries. The advantages of the installation of such networks made them very attractive to deploy broadband services in regions where wired technologies do not provide coverage. These technologies have been very successful alternative mainly in developing countries and particularly to support remote health services. Additionally, the unceasing development of mobile applications, most of them using smartphones, is changing the way medical care services are provided. The use of wireless technologies is improving the operating way of health care services and the conditions for the users to receive them. Ability of wireless technologies to transmit differentiated services is essential for the deployment of networks for mHealth applications. The efficient operation of such networks requires identifying requirements of real-time applications such as biomedical signal transmission, and requirements of

store and forward

applications such as access to EMR (Electronic Medical Record). In the first case it is necessary to assign a high priority to the transmission, but also to meet strict parameters of Quality of Service (QoS) such as delay, delay variation and packet loss rate. In the second case the QoS requirements for transmission are less strict. This chapter presents an overview of the QoS mechanisms offered by current and future wireless mobile and wired IP technologies. The defined technical requirements of quality of service for the main mHealth applications are also presented. Finally a review of the capabilities of 4G technologies to support future mHealth applications is presented.

This chapter is focused on discussions of a number of issues and challenges associated with the underlying infrastructure of mHealth, including: regulation of medical devices/smartphones, network security and privacy, interoperability, scalability, ubiquity, data management and ownership, remote monitoring, and the importance of end-to-end Quality of Service (QoS) for full reliance on mHealth technology in medicine.

Remote mobile data collection systems (MDCSs) are a combination of a client application running on mobile devices, wireless infrastructures and remotely accessible server databases. Most of the existing systems share common principles and guidelines to collect data remotely. As reported also in [3], MDCSs have been mostly aimed at projects with tight budgets, deployed in developing countries with sparsely populated areas, where low data rate and intermittent connectivity exist.

Mobile technologies confer mobility and autonomy on patients with the advantage of access to home care and health care services on demand. However, these benefits impose challenges to the future health care services. For instance, computation capacity of a conventional smartphone provides applications and services with sufficient power of calculation and automation to assist in daily life activities and medical purposes. Combined with a user-friendly interface, mobile technologies can be an easy and efficient manner to help people who are in a condition of cognitive deterioration or have a chronic disease which demands a close connection to near family members and/or to health care services.

Security and privacy have been well established as major considerations in health informatics generally (Rindfleisch 1997). A challenge for healthcare innovation is to embrace the potential of mobile health creatively within the healthcare system and not to merely replicate current technologies into a parallel wireless environment. In addressing this challenge, the complexities of securing health information along a composite clinical information pathway and in each situation of use must be defined.

Health can provide efficient and convenient personalized healthcare services to a variety of patients with diverse medical needs. Existing security vulnerabilities, such as identity theft, loss or theft of mHealth devices and health information raise grave concerns in preserving privacy as well as in promoting user acceptance. With the advent of location dependent personalized mHealth services, access control coupled with cryptographic techniques are seen as pragmatic solutions in preserving privacy of mHealth data in addition to the formal regulatory requirements. In this view, we discuss the impact of privacy threats on user acceptance of personalized mHealth services. As well as the implications of enforcing privacy preserving enforcements at mHealth device level, network level and regulatory measures for sustainable and wide-spread use of personalized mHealth services in future.