Teletmedicine

Using wireless technologies in healthcare Abstract: With an increasingly mobile society and the worldwide deployment of mobile and wireless networks, wireless infrastructure can support many current and emerging healthcare applications. However, before wireless infrastructure can be used in a wide scale, there are several challenges that must be overcome. These include how to best utilise the capabilities of diverse wireless technologies and how to effectively manage the complexity of wireless and mobile networks in healthcare applications. In this paper, we discuss how wireless technologies can be applied in the healthcare environment. Additionally, some open issues and challenges are also discussed. Keywords: mobile and wireless networks; location management; intelligent emergency system; patient monitoring; telemedicine; healthcare applications; ad hoc wireless networks. 1 Introduction The introduction of telecommunications technologies in the healthcare environment has led to an increased accessibility to healthcare providers, more efficient tasks and processes and higher quality of healthcare services (Kern and Jaron, 2003; Wells, 2003; Lin, 1999; Zhang et al., 2000; Lee et al., 2000; Holle and Zahlmann, 1999). However, many challenges, including a significant number of medical errors (To err is human: building a safer health system, 2000; Hayward and Hofer, 2001) considerable stress on healthcare providers and partial coverage of healthcare services in rural and underserved areas, still exist worldwide (Singh, 2002; Parsloe, 2003). These challenges combined with an increasing cost of healthcare services, such as the cost of healthcare services reaching to 15% of the US Gross National Product (Kern and Jaron, 2003) and the exponential increase in the number of seniors and retirees in developed countries (Forum, 2005) have created several major challenges for policy makers, healthcare providers, hospitals, insurance companies and patients. One such challenge is how to provide better healthcare services to an increasing number of people using limited financial and human resources. The current and emerging wireless technologies (Varshney and Vetter, 2000; Boric-Lubecke and Lubecke, 2002) could improve the overall quality of service for patients in both cities and rural areas. Also, these technologies can reduce the stress and strain on healthcare providers while enhancing people’s productivity, retention and quality of life, and also reduce the overall cost of healthcare services in the long-term. It is well known that many medical errors occur due to a lack of correct and complete information at the location and time it is needed, resulting in wrong diagnosis and drug interaction problems (To err is human: building a safer health system, 2000; Hayward and Hofer, 2001). The required medical information can be made available at any place and at any time using sophisticated devices and widely deployed wireless and mobile networks. Although, wireless technologies cannot eliminate all medical errors, some of the informational-errors can certainly be avoided by the anytime-anywhere access to medical information. Mobile and wireless technologies can be effectively utilised by matching infrastructure capabilities to healthcare needs. These include the following: • The use of location tracking, intelligent devices, user interfaces, body sensors and short-range wireless communications for patient monitoring. • The use of instant, flexible and universal wireless access to increase the accessibility of healthcare providers. • The use of reliable communication among medical devices, patients, healthcare providers and vehicles for effective emergency management. In the long term, affordability, portability, and reusability of wireless technologies for patient monitoring and preventive care will also reduce the overall cost of healthcare services (Varshney and Vetter, 2000; Boric-Lubecke and Lubecke, 2002; Pattichis et al., 2002; Schepps and Rosen, 2002). Before discussing our work, we briefly summarise the work done by others in applying mobile and wireless technologies to the healthcare sector. It has been noted that telemedicine is used in less than 10% of all healthcare services (Lin, 1999). Since about 50% of world population lives in rural areas in developing countries, this should be considered a partial success. As the deployment and usability of wireless infrastructure in rural areas increase, both real-time and store-and-forward type telemedicine applications could be utilised on a larger scale. For timely management of problem cases, telemedicine is used for real-time consultation between referring physicians and experts (Zhang et al., 2000). A healthcare monitoring system and field trial for chronically ill patients using cable-TV infrastructure are described in Lee et al. (2000). The system supports ECG and BP information with video and audio communications among patients and healthcare providers. To measure the effectiveness of telemedical services, a four-phase evaluation framework based on technical performance to medical outcome in therapeutic effectiveness and diagnostic accuracy is proposed in Holle and Zahlmann (1999). The evaluation also includes economical aspects for telemedical systems. The use of wireless sensors in minimally invasive continuous health-monitoring systems is discussed in Boric-Lubecke and Lubecke (2002). A maritime telemedicine system with multilingual capabilities is described in Anogianakis et al. (1998). The system uses satellite and ground-based networks for supporting audio and video-conferencing, multimedia communications and flat-file and image transfer. Several applications of wireless telemedicine systems, including telecardiology, teleradiology, and telepsychology, are presented in Pattichis et al. (2002). An implementation of pervasive computing technologies in an assisted care facility1 can be found in Stanford (2002). Using network sensors and databases, facility staff members are alerted when residents need immediate care. Using combined infrared and radio-based locator badges, which also act as keys, the system alerts the staff if a resident wanders out of a certain area (Stanford, 2002). The middleware can play an important role in healthcare as it can hide the lower level networking details from the upper-layer applications. The middleware requirements of pervasive healthcare have been identified as authorisation and authentication, adaptability, availability and modifiability (Raatikainen et al., 2002). We believe that the requirements could also include scalability, implementability and usability. For long-term health monitoring and easy retrieval of information, a wearable healthcare assistant can be used (Suzuki and Doi, 2001). This notebook computer-based system records physiological and contextual information. The assistant can sense pulse waves, user’s actions and postures. It can also capture contextual photos and continuous voice. A high-pressure (stressful) state is detected from the high pulse-rate by using the context information. The information is stored and retrieved on a website and requires modification for fitting on smaller hand-held devices. As the patient information must be protected due to legal, ethical and privacy concerns, some work has been done in selective information sharing, where a set of rules are specified for restricting access to patient healthcare information (Zhang et al., 2002). Using existing security protocols, such as IPsec, a protection system for remote patients is discussed in Kara (2001). To adapt mobile devices and services for elderly, some preliminary work has been done (Mikkonen et al., 2002). The authors found that the elderly are ready to begin using wireless and mobile technologies as long as these truly facilitate independent living. The location issues are addressed in Varshney (2003a; 2003c). Some works on delivering patient data across an enterprise are presented in Lin and Vassar (2004). The trust issues are discussed in Wickramasinghe and Mishra (2004). The use of internet GIS technology for public healthcare is suggested in Ptochos et al. (2004). The level of utilisation of mobile devices in a nursing home is examined in Chau and Turner (2004). Some development issues for wireless systems for healthcare are presented in Gururajan and Vuori (2003). The security impact of WAP on e-health is presented in Tan et al. (2003). The design and implementation of a wireless prescription system is included in Zeadally and Pan (2004). The use of mobile internet-based solution for problem drinkers is presented in Cheng and Arthur (2002). Mobile data healthcare systems are presented in Sakarya (2002). The use of a survey to derive requirements and challenges in mobilising medical information and knowledge is suggested in Han et al. (2004). The design and implementation of mobile virtual communities is described in Leimeister et al. (2002). Several general issues in PDAs, hand-held devices and wireless healthcare can be found in Fontelo and Chismas (2005). The use of a certain hand-held device in clinical emergency environment is presented in Michalowski et al. (2004). The cost and acceptance issues in mobile healthcare are evaluated in Wu et al. (2005). The experiences in using mobile information systems by physicians are included in Harkke (2005). A list of critical factors in acceptance of mobile nursing technologies is identified in Li et al. (2005). A discussion of development and evaluation of mobile decision support system can be found in San Pedro et al. (2005). The transcoding of biomedical information resources for mobile devices is described in Parmanto et al. (2005). The access to medical literature using handheld devices is described in Fontelo et al. (2005). The above review of wireless in healthcare illustrates several issues. These are the following: • The introduction of wireless technologies is very preliminary as even healthcare requirements and challenges have not been identified. • The unique capabilities of wireless and mobile infrastructure have not been utilised. • The applications and solutions are limited to using a single type of wireless network, thus restricting the access and coverage. • The introduction of wireless and mobile technologies is very fragmented and limited to a few simple cases. In this paper, we discuss and illustrate how mobile and wireless technologies can be applied for location management, intelligent emergency system, patient monitoring and mobile telemedicine applications. The contributions of this paper include: • identifying healthcare applications with the most potential for wireless technologies • deriving requirements and networking infrastructure for several healthcare applications. the paper is organised as follows. An intelligent emergency management system is presented in Section 2 and a deployment of wireless in patient care and monitoring is shown in Section 3. The use of mobile and wireless technologies in telemedicine is discussed in Section 4. The use of wireless technologies in the future healthcare environment is presented and discussed in Section 5. Finally, concluding remarks and open issues are presented in Section 6. 2 Intelligent emergency response and management system The proposed architecture supports an intelligent emergency response and management system using the information from mobile and wireless networks. The information include the locations of emergencies derived from location tracking of enhanced 911 calls. Such information can be used to filter emergency calls by matching time, location and description of events as patterns (Figure 1). This could reduce the overload on emergency call systems (such as the emergency 911 service in USA), where for some systems it is routine to receive hundreds of cellphone calls for the same incident. This is wasteful because multiple ambulances may be dispatched to handle the same emergency and thus delay similar service if another incident takes place. The information from wireless networks can also be used to find the best routes by using real-time traffic information and allowing intervehicular communication to update the traffic routing information. This could be combined with finding the closest hospital(s) with the needed care and also for checking the availability of hospital space. If the information derived from the sensors on the bodies of people involved in emergencies can be processed, it may be possible to implement a prioritised healthcare delivery mechanism in the routing of emergency vehicles. These intelligent additions would improve the overall efficiency of the emergency management system, allowing it to maximise the number of emergency cases it can handle with a limited budget and people. The proposed changes can also result in saving many more lives while keeping the quality of service for others at a high level. The requirements of intelligent mobile emergency response system include supporting intervehicle communications for incident management and finding best-current-route for vehicles. These can be supported by creating ad hoc wireless networks among emergency vehicles. However, issues on how to maintain communications among vehicles moving at high speeds and short contact time for communications must be addressed. The quality of communications channel at high speed and the amount of spectrum necessary for intervehicle communications must also be considered. It is also possible that communications among the emergency vehicles can be facilitated by emergency call systems. However, the network coverage and processing requirements could affect the scalability of such mediated communications. Other requirements include location tracking of incidents and automatic filtering of same incident-call by matching location, time and description of incidents. In terms of traffic, the sessions are likely to be short. Therefore, unicast communication for callers and multicast for emergency vehicles must be supported. The specific networking requirements include location management for calling parties, incidents and emergency vehicles. Also, wireless infrastructure must be dependable and support real-time communications among vehicles. The additional requirements are significant levels of intelligence in the emergency system and wireless networks and scalability of the system as the number of users, incidents, calls and vehicles are increased. 3 Wireless patient monitoring and requirements Many healthcare applications require reliable monitoring of patients such as those in a hospital or nursing home. Although it is fairly simple to monitor patients using one of several wireless LANs (Local Area Networks) in and out of a facility (Figure 2), the coverage of wireless networks is not comprehensive on every square metre of a facility. This could result in time and location-dependent dead-spots with unpredictable wireless coverage. Currently in a typical nursing home in USA, a patient is observed by a nurse or staff one to few times an hour. However, if a patient is having a heart attack while being in the bathroom alone, the required help may not come in time. We propose a new architecture for patient monitoring where patients are equipped with small devices (such as a watch). These devices could normally be in range of an infrastructure-based network wireless LANs. However, when not covered by a wireless network due to coverage, battery power or obstructions, several of these devices could form an ad hoc wireless network. The information on vital signs of a patient can be transmitted from his/her device to another nearby patient and so on (Figure 3). This would increase the chance that such vital signs are picked up by one or more wireless networks or healthcare provider directly to his/her device. This creation of ad hoc wireless networks among patients and devices to allow for movement of sensor information would require wireless routing and multicasting to allow for information to reach a healthcare provider. Unlike general-purpose wireless networks, the efficiency of networking operations is not the major criterion. The reliability, speed, and correctness of critical information must be supported. The requirements of mobile patient monitoring includes the continuous monitoring for some patients and event-driven monitoring for others, frequency of monitoring, number and types of vital signs that must be monitored and transmitted, and size and frequency of messages to be transmitted. The specific networking requirements include universal access to wireless networks, location management, high levels of wireless network reliability, network scalability with an increased number of users and frequency of monitoring and support for prioritised transmission of vital signs of certain patients. The quality of service requirements are low delay and high probability of message delivery. The additional requirements include the security and privacy and the ways to support the usage cost of mobile patient monitoring systems. It is not clear how insurance companies would pay for patient monitoring services. Wireless and mobile telemedicine Although in a limited scale, telemedicine, or the use of telecommunications technologies for medical diagnostics, treatment and patient care, has been in operation for several years (Varshney and Vetter, 2000). Currently, mobile devices such as PDAs with 802.11 wireless LAN access are being used to upload and download schedules for patients and doctors. In few places, doctors and nurses can access patient information on PDAs or hand-held devices and can also enter new information. People can be reminded of their appointments in their PDAs by displaying short text messages. It is likely that mobile telemedicine services would be offered and used significantly. The reasons behind this optimism are the following: • increasingly mobile savvy society with more than one billion hand-held devices worldwide • deployment of wireless-based solutions in developing countries where ‘wire-line’ infrastructure is minimal or impractical • portability and usability of mobile hand-held devices combined with an increasingly sophisticated workforce. A possible scenario to extend telemedical services to a larger population is as follows. A geriatric psychiatrist who spends most of his/her time in an out-patient clinic prefers to go to a far away nursing home only if there are several patients for comprehensive evaluation. However, preliminary consultations with nursing home staff, patients and other doctors could be done by using hand-held devices and access to multiple wireless networks, allowing him/her to be in a car, office, airport, traffic jam or other places (Figure 4). Such a system will also enable an information-aware physician to download the detailed current patient information before arriving at a nursing home for seeing multiple patients. This would also allow him/her to either see more number of patients in his/her limited time. Thus, the physician increases his/her productivity and he/she is able to finish nursing home jobs quicker. The characteristics and requirements of mobile telemedicine include long sessions for consultation, multilocation coordination, pervasive and ubiquitous access to patient data and information and ability to transmit significant data due to images, video and medical information. The specific networking requirements include dependable and reliable network architectures, universal access to wireless networks, real-time support for information upload, download and discussions, support for significant quality of service and continued access for long sessions. The additional requirements include security and privacy, mobile devices that can work with minimal input requirements and voice activation. It also includes and ways to support the installation and usage cost of mobile telemedicine systems. There are also issues of insurance payments for mobile telemedicine services rendered to patients, for potential mistakes, errors and liabilities. 5 Wireless technologies for healthcare Many of the healthcare applications would benefit from the location tracking of patients and healthcare providers, devices and supplies. Location tracking can also be very helpful for finding people with matching blood groups, locating organ donors, providing post-op care for people and helping old and mentally challenged people in hospitals and nursing homes. An integrated wireless architecture for location management could include GPS, cellular networks, wireless LANs and RFID. Each one of these networks and wireless technologies supports location tracking of people, devices and services in diverse locations accurately. Cellular networks can offer higher accuracy as Enhanced 911 (E911) infrastructure becomes increasingly available. It will allow network-based tracking with 100-metre precision and handset-based tracking with 50-metre precision for mobile users.2 The accuracy achieved for portable and fixed entities is even higher. Major E911 schemes are Assisted and Differential Global Positioning Systems (A-GPS and D-GPS, respectively), Time Difference of Arrival (TDOA), Angle of Arrival (AOA) and Location Pattern Matching (LPM) (Djuknic and Richton, 2001). TDOA and AOA schemes locate a mobile device by processing the difference in signal arrival times at three or more antenna sites. This is called base station triangulation. Our architecture supports even higher location accuracy by combining small cells with base station triangulation. Some of the cellular/PCS location schemes can be used in indoor location tracking. Since many indoor applications require higher location precision, smaller Wireless Local Area Networks (WLANs) and Personal Area Networks (PANs) can be used. The base stations are kept closer. These cooperate in the location tracking of radio-enabled devices, users, products and services. The radius of a cell can be determined as the minimum of location accuracy and the coverage of base stations. Radio Frequency Identification (RFID) uses wireless links to uniquely identify objects or people using dedicated short-range communications (D’Hont, 2001). When a product or person with a tag enters the read zone of a reader, the address and data stored on the tag is read and can be sent to a server for location tracking purposes. Since RFID readers have limited coverage (5–10 feet), multiple RFID readers will be needed to cover the whole area (e.g., a warehouse). The maximum distance between two neighbouring readers can be based on the range of readers and the location accuracy required (Varshney, 2003b). Another interesting way to perform indoor location tracking is via the use of specialised location devices attached to products and people. One such example is the Locus that can be attached to clothes or portable devices (Koshima and Hoshen, 2000). Such location terminals can return signal strength data and IDs of base stations to a monitoring centre. Using this information, the user location can be computed with varying degrees of accuracy. The outdoor tracking support for healthcare applications may be provided by either a cellular/PCS system or a satellite-based system such as assisted GPS. Nearly all the schemes used in cellular and satellite-based networks perform well in an outdoor environment. Even wireless LANs and RFID-based systems can support applications requiring outdoor location management. Many of the proposed outdoor schemes will encounter performance problems in indoor environment due to triangulation difficulties caused by weaker signals and line of sight requirements of satellite-based schemes. Since many indoor tracking applications require higher location precision, smaller WLANs and PANs should be used for indoor location management. Indoor tracking for healthcare applications can be performed using specialised cells (where a base station can locate in a very small area, but where a significant number of base stations are required to cover the whole area), wireless LANs, ad hoc PANs and Radio Frequency ID (RFID). The location precision requirement can be satisfied by using one of several wireless networks, which provide different levels of location accuracy. An extensive wireless coverage is achieved by providing indoor and outdoor coverage to fixed and mobile users in local as well as in wide area environments. Since this architecture supports the roaming of a user across multiple networks, location coordination is necessary among networks. Location tracking can also be performed using a WLAN or a PAN. These networks cover smaller areas (and fewer users). Therefore, a base station or a certain device can be programmed to determine if a certain device or user is located in the networks’ coverage. This feature is applicable to both infrastructure and ad hoc versions. Location tracking involves mobile, portable or fixed entities. Mobile entities can be located within the accuracy of the location scheme of the wireless network(s) used. If mobile entities are part of an ad hoc wireless network, then a more specialised scheme (such as using GPS and a monitoring system) has to be used. Portable entities without regular wireless access can be tracked using radio frequency tags or specialised locator devices. Location information on fixed entities can be stored in a database and can be updated as necessary (Varshney, 2003b). One example of how wireless location management techniques is used in healthcare is shown in Figure 5, where medical inventories are managed using a mobile hand-held device. As the quantity of the supplies decrease below a threshold, additional inventory is ordered and tracked using wireless networks. This system can be joined with a demand projection system for ‘pro-active’ inventory management. Such an intelligent mobile inventory system is likely to reduce the cost of inventory while increasing the chances of finding a certain item when needed. To allow multiple suppliers to compete for medical supplies, mobile auction type trading can be performed. This reduces the cost of supplies even further. 6 Open issues and conclusion There are many open issues and challenges in using wireless technologies in healthcare that must be addressed. These include a lack of comprehensive coverage of wireless and mobile networks, reliability of wireless infrastructure, general limitations of hand-held devices, medical usability of sensors and mobile devices, interference with other medical devices, privacy and security, payment and many management issues in pervasive healthcare. We will attempt to group these issues and challenges under the following categories: technologies, medical and management. The technology issues related to the introduction of wireless network technologies in healthcare includes networking support such as location tracking, routing, scalable architectures, dependability and quality of access. These issues also include how to provide patient monitoring in diverse environments (indoor, outdoor, hospitals, nursing homes, assisted living), continuous vs. event-driven monitoring of patients, use of mobile devices for healthcare information storage, update and transmission, sensing of vital signs and transmission using cellular networks and wireless LANs, formation of ad hoc wireless networks for enhanced monitoring of patients, managing healthcare emergency vehicles and routing and network support for mobile telemedicine. The medical aspects are very important in realising a wide-scale deployment of wireless network technologies in healthcare. The issues of how patient care is delivered, how medical information can be represented and requirements of diverse patients must be addressed. Many important issues include the design of suitable healthcare applications, specific requirements of vital signs in healthcare environment, diversity of patients and their specific requirements, representation of medical information in pervasive healthcare environment (multimedia, resolution, processing and storage requirements), role of medical protocols, improved delivery of healthcare services and usability of wireless-based solutions in healthcare. The diversity of patients can range from uncontrollable energetic children, violent youth and midlife, depressed or frail seniors. The requirements presented by these people to wireless networks vary significantly from keeping track of the behaviour of kids to how to avoid wandering and getting lost for dementia patients. It will be a major challenge to involve people with mental illness to use wireless infrastructure due to their limited functional intelligence or their very limited memory (such as those suffering from dementia). Many of these also suffer from psychiatric disorders such as paranoia resulting in a suspicion towards wireless technologies, especially those once requiring a patient to wear a locator or other device. The management of pervasive healthcare could bring a mini-revolution in terms of how wireless network technologies in the healthcare environment is implemented, offered and managed. There are many challenging and diverse management issues that must be addressed including the security and privacy in wireless healthcare, training of healthcare professionals for pervasive healthcare, managing the integration of wireless solutions, increasing coverage of healthcare services using wireless technologies, legal and regulatory issues, insurance payments and cost aspects and potential implications of HIPAA (Health Insurance Portability and Accountability Act of 1996). The usability and integration of wireless-based solutions in healthcare is another challenge. The devices must be designed to offer intuitive interfaces that can learn with and from individuals. It has been shown that many less-technically savvy population segments are willing to learn and use mobile and wireless technologies for allowing them to live more independently. The training of healthcare professionals to effectively utilise mobile and wireless technologies would be a less complex issue as an increasing number of those are using hand-held and wireless devices. Another major issue is how to reduce the cost of delivering healthcare services to as many people by using wireless infrastructure. Other challenges in the large-scale introduction of wireless infrastructure in healthcare are legal and regulatory issues such as the issues of liability and lawsuits in the USA and possibility of insurance companies not paying or paying differently for treatment via mobile devices. Another major issue is the privacy and the possible misuse of patient medical information. In the USA, a major regulation termed HIPAA (Health Insurance Portability and Accountability Act of 1996), which have been designed to protect such information, has received some controversy and has been interpreted differently by major players, healthcare providers, insurance people and attorneys. Some work is needed in addressing privacy and related concerns over wireless and mobile networks where security is still seen as insufficient. The role of wireless infrastructure in healthcare application is expected to become more prominent with an increasingly mobile society and with the deployment of mobile and wireless networks. The work on wireless network technologies in healthcare has been in its initial stages and many requirements have not been addressed. In this paper, we discuss and illustrate how mobile and wireless technologies can be applied for location management, intelligent emergency system, patient monitoring and mobile telemedicine applications. 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The objective of this paper is to provide an overview of the current status and challenges of mobile health systems (m-health) in emergency healthcare systems and services (e- emergency). The paper covers a review of recent e-emergency systems, including the wireless technologies used, as well as the data transmitted (electronic patient record, biosignals, medical images and video, subject video, and other). Furthermore, emerging wireless video systems for reliable communications in these applications are presented. We anticipate that m-health e-emergency systems will significantly affect the delivery of healthcare; however, their exploitation in daily practice still remains to be achieved. Index Terms- Telemedicine, mobile health systems, mobile, wireless, emergency, m-health, e-emergency, wireless video. 1. Introduction m-Health can be defined as ‘emerging mobile communications and network technologies for healthcare’ [1]. This concept represents the evolution of ‘traditional’ e-health systems from desktop platforms and wired connections to the use of more compact devices and wireless connections in e-health systems. The emerging development of m-health systems in the last decade was made possible due to the recent advances in wireless and network technologies, linked with recent advances in nano-technologies, compact biosensors, wearable devices and clothing, pervasive and ubiquitous computing systems. These advances will have a powerful impact on some of the existing healthcare services and will reshape the workflow and practices in the delivery of these services [1]. A brief review of the spectrum of m-health systems and applications and the potential benefits of these efforts was presented in a recent paper by our group [2]. Moreover, an edited volume was published [1], covering a number of areas in mobile m-health systems. The objective of this paper is to provide an overview of the status and challenges of m-health in emergency healthcare systems and services (e- emergency). The paper reviews recent e-emergency systems, including the wireless technologies used, as well as the data transmitted (electronic patient record, biosignals, medical images and video, and other). Wireless telemedicine systems and services are expected to enhance traditional emergency care provision not only within the Emergency Department but also in a variety of pre-hospital emergency care situations where geographically remote consultation and monitoring can be implemented [3], [4]. A timely and effective way of handling emergency cases can prove essential for patient’s recovery or even for patient’s survival. Especially in cases of serious injuries of the head, the spinal cord and internal organs, the way of transporting and generally the way of providing care are crucial for the future of the patient. Furthermore during cardiac disease cases, much can be done today to stop a heart attack or resuscitate a victim of Sudden Cardiac Death (SCD). Time is the enemy in the acute treatment of a heart attack or SCD. The first 60 minutes (the golden hour) are the most critical regarding the long-term patient outcome. Therefore, the ability to remotely monitor the patient and guide the paramedical staff in their management of the patient can be crucial. M- emergency becomes important in facilitating access to effective and specialist directed care. Some benefits of prehospital transmitted ECG for example, as documented by Giovas et al. [5], are the following: reduction of hospital delays, better triage, continuous monitoring, ECG data accessible for comparison, computer aided analysis and decision making, and prehospital therapy in eligible subjects with acute myocardial infarction (AMI). This paper provides an overview of the main technological components of m-health e-emergency systems. The structure of the paper is as follows. Section 2 covers an introduction to wireless transmission technologies, followed by section 3 which covers the presentation of emerging wireless video systems for reliable communications. In section 4, an overview of m-health e-emergency systems is documented based on published journal, conference papers, and book chapters. Section 5 addresses the future challenges, and section 6 the concluding remarks. 2. Wireless Transmission Technologies In this section we briefly describe the main wireless technologies that are used in wireless telemedicine systems, namely GSM, 3G (W-CDMA, CDMA2000, TD-CDMA), satellite, and wireless LAN (WLAN). Emerging wireless technologies such as Wi-Max, Home/Personal/Body Area Networks, ad-hoc and sensor networks are also described. These systems are summarized in Tables 1a & 1b. GSM is a cellular system currently in use, and is the second generation (2G) of the mobile communication networks. It had been designed for voice communication (circuit switched), but can also carry data. In the standard mode of operation, it provides data transfer speeds of up to 9.6 kbps, whereas the enhanced technique High Speed Circuit Switched Data (HSCSD) makes possible data transmissions of up to a maximum of 115 kbps [6]. The evolution of mobile telecommunication systems from 2G to 2.5G (iDEN 64 kbps, GPRS 171 kbps, EDGE 384 kbps) and subsequently to 3G (W-CDMA, CDMA2000, TD-CDMA) systems facilitates both an always-on model (as compared with the circuit-switched mode of GSM), as well as the provision of faster data transfer rates, thus enabling the development of more responsive telemedicine systems. High Speed Downlink Packet Access (HSDPA) [7] is the latest system enhancement of W-CDMA networks, resulting in higher data transfer speeds, improved spectral efficiency and greater system capacity. With a theoretical peak of 14.4 Mbps (typically around 1 Mbps), telemedicine systems can benefit from data transfer speeds currently only feasible on wired communication networks [6], [8]. Satellite systems are able to provide a variety of data transfer rates starting from 2.4 kbps and moving to high-speed data rates of up to 2x64 kbps and beyond. Satellite links also have the advantage of coverage all over the world [9], but require line of sight and comparably higher power for similar bit rates. WLAN is a flexible data communications system implemented as an extension to or as an alternative for a wired LAN. Using radio frequency (RF) technology, WLANs transmit and receive data over the air, minimizing the need for wired connections. Thus, WLANs combine data connectivity at tens of Mbps with limited user mobility, becoming very popular in a number of vertical markets, including the healthcare, retail, manufacturing, warehousing, and academia. These industries have profited from the productivity gains of using hand-held terminals and notebook computers to transmit real-time information to centralized hosts for processing. However, WLAN coverage is limited in distance to an area covering about 100 meters per cell (access point), or the coverage area of a ‘private’ entity, as for example the hospital premises, with the use of multiple access points. To extend coverage over large distances, wireless mesh networks are also being considered. These networks are peer-to-peer multi-hop wireless networks, in which stationary nodes take on the routing functionality thus forming the network’s backbone. Basically, these act as a gateway to high-speed wired networks for mobile nodes (clients) which communicate in a peer manner. WiMax is a wireless digital communications system defined by the IEEE 802.16 standard. Its advantage over WLANs lies in the fact that WiMax can provide broadband wireless access up to 50 km for fixed stations and 5 km-15 km for mobile stations, thus intended for wireless “metropolitan area networks” (WMANs) [10]. It is anticipated that utilization of this attractive feature will lead in a vast deployment of WiMax systems, however the adoption of WiMax at this point in time is still at an early phase. Today wireless LANs and MANs are becoming more widely recognized as a general-purpose connectivity alternative for a broad range of applications. These technologies have slowly started penetrating the health sector. Home/Personal/Body Area Networks allow connectivity of devices in the vicinity of tens of metres. Bluetooth or RF technologies may be incorporated to set such networks up. In disaster control cases, Bluetooth connectivity may be utilized to link ad-hoc networks to existing cellular networks. [Table 1a and Table 1b go here] While the aforementioned wireless systems are based on infrastructure and base stations connected to a wired backbone network, ad-hoc and sensor networks do not require any wired infrastructure. Mobile ad-hoc networks or MANETs are a collection of geographically distributed mobile nodes that interact ‘on the move’ with one another over a wireless medium instead of communicating wirelessly to a base station [11]. These kinds of networks are particularly useful in the absence of a wired infrastructure or under strict time constraints when no time is available to set a network up. This characteristic may prove particularly useful for emergency systems. Wireless sensor networks WSNs differentiate from MANETs which are more human oriented and instead are focused on interaction with the environment. They incorporate sensors and actuators and environment oriented as they are, they measure and can influence this environment according to the occasion, as documented by Akyildiz et al. survey [12], before the recorded information is communicated wirelessly for further processing. They are hence somewhat embedded in the environment [11]. Besides their numerous applications WSNs are also applicable in the health sector where they may be used to monitor, for example, post-surgery state and recovery or surveillance of chronically ill patients. 3. Current and Emerging Methods for Reliable Wireless Video Communications The transmission of medical video images over wireless communications channels has introduced several challenges over standard video communications methods. Clearly, for e-emergency systems, there is a strong demand for high bandwidth and a requirement for high quality and short time delays. These requirements are further complicated by frequent communications errors associated with wireless channels. In this section, our focus is on the use of error control mechanisms for maintaining acceptable video quality levels in wireless communications channels. In a typical video communications system is depicted, the original video sequence is source encoded, packetized in RTP [13] format, channel encoded, and transmitted over the packet based network. The reverse procedure is followed at the decoder’s side. An important new aspect of the incorporated scheme relies in the presence of a back channel providing a low-bandwidth communications link from the decoder back to the encoder. Video communication errors can be classified as randomly distributed single-bit errors, packet losses, burst errors, and packet delay variations. To compensate for delay variations, a buffer is maintained at the decoder, which positions the RTP packets according to their sequence number into their initial order. However, packets with significant delays are dropped, and re-transmission is often unattainable due to significant time delays. Since video compression standards rely on predictive coding methods (motion estimation and compensation), packet loss may be propagated to different parts of the video. Error control mechanisms are generally classified in terms of encoder error resilient methods, decoder error concealment, and joint encoder-decoder error control. We next provide an overview of basic, encoder error resilient methods. More advanced and recent error resilient methods are described in Section 3.1. We begin this discussion with basic definitions, to help understand the characteristics of error-resilient control mechanisms. For each concept, we briefly provide its relation to error control. A digital video can be thought of as a sequence of video frames. The first frame is intra-code, also termed an I-frame. I-frames are encoded utilizing only information of the current frame, thus requiring more bits to encode. Subsequent frames maybe inter-coded, referencing previously decoded frames (P-frames) or both previous and subsequent frames (B-frames for Bidirectional). To limit error propagation between frames, a video compression algorithm may introduce periodic or random I-frames. A frame can be divided into independently transmitted and decoded slices. This approach enables better compression and in wireless environments, where out of sequence packet delivery is possible, reduces decoding delay. A slice can be broken down into MacroBlocks (MBs) and MBs into blocks. An MB is a collection of spatially adjacent pixels, usually forming a rectangular area, that can be independently processed. At the frame level, error propagation maybe limited to the extend of a slice. Video bitstream information is encoded using variable length coding (VLC). In VLC, scalars or vectors are encoded according to their frequencies of occurrence. Frequent scalars or vectors are assigned short codes while infrequent ones are assigned longer codes. This results in significant compression of the bitstream. Unfortunately, a single bit error also makes a standard VLC bitstream undecodable. The problem is a direct consequence of the use of variable-length codes. In standard VLC, following a single bit error, there is no way to determine when the current (decoded) code ends and the new one begins. To limit the extend of these errors, we employ re-synchronization markers and reverse variable length bit coding (RVLC). Using re- synchronization markers,[14] following an error, the decoder simply jumps to the next marker and resumes decoding onwards. In RVLC,[15] the decoder jumps to the next marker and starts decoding backwards thus utilizing uncorrupted bits. Following our brief introduction to basic error-resilient methods, we provide a brief overview of decoder- based error-concealment. In error-concealment, the decoder uses spatial or temporal interpolation methods to reconstruct missing data. The basic methods can be classified as ([14], [16] – [25], and references therein): • Spatial interpolation from surrounding pixels of the same frame (MPEG-1/H.261), • Temporal interpolation from neighbouring video frames (w/out motion estimation in MPEG- 1/H.261), and • Motion compensated temporal interpolation from neighbouring video frames (MPEG-4/H.263). For medical video applications, we may need to limit interpolation to avoid any adverse effects on the diagnosis. In joint encoder-decoder error control, the encoder and the decoder work together to minimize the error. These methods were introduced in MPEG-4 and H.263. In this case, we require a back channel from the decoder to the encoder. The back channel can be used to adapt encoding to available bandwidth, and most importantly, informing the encoder of lost packets during the transmission. Once the encoder becomes aware of which packets were lost, subsequent encoding can be adapted to avoid any reference to them, in effect synchronizing the encoding process with real-time decoding. A comprehensive review of feedback based error control can be found in [26]. The basic methods involve the use of feedback information for error- tracking and also to guide the selection of a reference frame [14], [26]. The use of joint encoder-decoder error control holds great promise in emergency applications. Here, we note that the back-channel also facilitates the development of collaborative environments, where a medical expert at the decoder side can help guide the medical exam. 3.1 Error Resilient Methods in Video Compression Standards We briefly discuss the use of error resilient methods in a variety of video compression standards (see Table 2). In describing error-resilient methods, we follow a hierarchical approach, presenting general bitstream methods first, followed by frame-based methods, slice methods, and macroblock-based methods. To accommodate the wide variety bandwidths, the new compression standards rely on scalable coding methods. In scalable coding, a video is usually encoded into a base layer and many enhancement layers. The base layer provides satisfactory quality video that must be decoded by all communications channels, regardless of how little bandwidth is available. Then, according to bandwidth availability, a number of enhancement layers are added to the base layer to produce higher quality video. Here, the base layer is protected more strongly from the enhancement layers. To this end, enhanced forward error correction (FEC) [27] and automatic repeat request (ARQ) schemes are employed [14], [16], [17]. The term unequal error protection with layered coding (UEP & LC) is used to describe this scheme (see Table 2). Alternatively or additionally, in multiple description coding (MDC), a data source is encoded into a number of descriptions that are correlated and of roughly equal importance. Here, the source sequence is coded into multiple bitstreams with minor differences, which are in turn transmitted independently. Then, the decoding of a single bitstream provides adequate quality video, while the decoding of multiple robust streams provides for higher quality [14], [28]. In yet another method that uses multiple bitstreams, in H.264/AVC the decoder may be allowed to switch between two or more pre-encoded bitstreams (of different bandwidth and quality) from the same source. In H.264/AVC, this is facilitated through the use of synchronization/switching pictures SP/SI [29], [30]. The decoder triggers a bitstream switch through a back-channel regaining synchronization. In intra-updating, we limit prediction within the current frame, without any reference to previous frames. At the highest-level, we use I-frames to re-initialize the prediction. Similarly, we can use intra-coded macroblocks (MBs) with periodic intra-coding of all MBs, preemptive intra-coding based on previous knowledge of the channel loss model and random placement [14], [16], [17]. In intelligent intra-block refreshing by Rate Distortion (RD), we select a block coding scheme which minimizes a certain cost function (adopted by the H.264/AVC [31], [32]). In contrast, in multiple (two or more) reference picture motion compensation, we can extend motion compensation to more than two references. The use of multiple reference frames provides the decoder with a larger selection of reference frames for use in error concealment via temporal interpolation ([18] – [22]). In arbitrary slice ordering (ASO) ([19], [20], [22]), slices can be transmitted independently of their order within a picture. As a result, they can be also decoded out of sequence, reducing the decoding delay at the decoder. ASO is particularly effective in environments where out-of-order delivery of a packet is possible such as the internet or wireless networks, packet based networks in general. To recover from the loss of an entire slice, H.264/AVC allows the transmission of redundant slices (RS). Redundant slices may be coded different than the primary slices.In data partitioning, a primary slice can be divided in up to three parts, and then transmitted with unequal error protection (UEP). This approach allows us to use higher error protection in critical parts of the video. In flexible macroblock ordering (FMO), macroblocks within a slice are transmitted in different orderings [33]-[34]. The use of different transmission orderings provides for better error recovery. The approach improves over raster-scan ordering, which faces large problems with burst errors, and also allows region of interest (ROI) coding and recovery. Furthermore, arbitrary spatial placement of blocks provides for better error-concealment via spatial interpolation. [Table 2 goes here] 4. m-Health e-Emergency Systems In his seminal paper “Le telecardiogramme”, 100 years ago, Einthoven demonstrated the successful transmission of about one hundred ECGs through a distance of 1.5 Km, connecting his lab with the University Hospital in Holland [35]. Furthermore, according to Giovas et al. [5], 60 years later, in 1966, in Belfast, prehospital cardiac care was “moved” from the coronary care into the community by treating the early complications of AMI. In the following year, prehospital one-lead telemetry was presented in Miami [5], whereas in 1970, Uhley [36] published his experience on one-lead wireless telemetry ECG. The wireless transmission of 12-lead ECG over a cellular network was demonstrated in 1987 by Grim et al. [37]. In the following years, numerous ECG monitoring systems were developed that were also transmitting additional vital biosignals and in some cases medical images and videos. The most recent of these systems are summarized in the following section. 4.1. An Overview The MEDLINE and IEEE Explore databases were searched with the following keywords: wireless telemedicine emergency, wireless telemedicine ambulance, wireless telemedicine disaster, wireless ambulance, wireless disaster, and wireless emergency. The number of journal papers found to be published under these categories is around 180. Out of these a total of 33 applications were selected and are briefly summarized in Table 3. These systems cover the whole spectrum of wireless emergency telemedicine applications presented during the recent years. The papers are grouped using the wireless technologies types which are: GSM/GPRS, 3G, satellite and wireless LAN. The data transmitted are coded under the columns: “ECG and other biosignals”, “IMG” for medical images or patients images, “EPR/Data” for Electronic Patient Records or just DATA, “Video” for video conferencing or medical video transmission. The column “Web” identifies which of the applications were developed supporting web technologies. The majority of the applications (21) used the GSM/GPRS network while a lot of applications use Wireless LAN (11) in order to transmit data. The applications presented in the other two categories 3G and Satellite are rather limited. In the first group of applications which use the Mobile Telephony networks GSM/GPRS, we have the highest number of applications. These applications could be divided into two main categories, those transmitting biosignals such as ECG, Oxygen Saturation, Blood pressure etc., and those transmitting medical images or just pictures of a patient. Some of the presented applications are a combination of both categories. Most of the applications concern the transmission of biosignals and images in order to support prehospital treatment such as [42], [44], or the transmission of biosignals in order to monitor patients with chronic heart diseases [50]. Some of the applications concern the transmission of images only [54]-[57]. Imaging modalities are rapidly changing, thus affecting the medical procedures and the need for new telemedicine applications in order to support these procedures. Finally, one application is used for the access of electronic patient records [59]. [Table 3a and Table 3b go here] The second group covers those applications that use 3G mobile networks. The first one [60] concerns the transmission of biosignals and images of the patient, something that has been extensively presented by many applications in earlier stages. The second one [61] investigates the transmission of real time ultrasound video captured via a remotely controllable robotic arm. This application was developed by P. Vieyres and coworkers [63], and initially exploited using satellite links (see Section 4.2 for more details). Moving to the next category of communication links, the satellite links; we have only found four new significant studies. We do note however that a significant number of studies using satellite links were published prior to these studies (not reported here). The applications found here, mostly concern the transmission of ultrasound video [62]-[64]. Two of the papers [63], [64] also include the use of a robotic mechanism in order to remotely control the ultrasound acquisition as described above, in the section of 3G networks, while the other two papers [47], [65] concern the transmission of biosignals and images for emergency cases. The Virgin Atlantic press release [65] announces the first wireless telemedicine system that will be adopted by a major airline carrier that will be available in all its flights. The last category of applications, covers the use of Wireless LANs. Basically these applications are for disaster control cases where a lot of injured people might be concentrated in a small area and a Wireless LAN is used in order to monitor the condition of these people. Most of the applications presented here concerns the transmission of biosignals, and the use of sensor networks [67]-[70]. Three of the applications are transmitting images [71]-[73], with CT images transmitted in [72] and CT and MRI images in [73]. Also, (update to 72 & 73) one application is used for the access of electronic patient records [58]. 4.2 Case Study 1: Mobile Emergency Health Services in HYGEIAnet [45], [46] The HYGEIAnet represents the effort of more than 15 years of work of the e-health laboratory at the Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), led by the late Prof. Stelios Orphanoudakis. The HYGEIAnet covers the design, development and deployment of advanced e-health and m-health services at various levels of the healthcare system, including primary care, pre-hospital health emergency management, and hospital care in the island of Crete, Greece as illustrated in Fig. 1 [45], [46], [73], [74]. The HYGEIAnet covers the island of Crete. Crete has a local population of approximately 600,000, 7 hospitals, 16 primary health care centres, and a large number of isolated communities in remote locations. The population more than doubles during the summer period, and accidents more than triple during the summer period, with more than 42% of accidents involving tourists. The ambulatory service in Crete is supported with the IASO information system covering: triage protocols, coordination, and management of resources [46] covering the island as illustrated in Fig. 1. The IASO system has been in daily operation since 1997. It consists of the following four modules [46], [74]: i. Operator/Dispatcher Module: It allows creating, completing and printing the electronic “incident card”. Based on specific algorithms (online triage protocols), it offers help with regard to incident severity estimation and the selection of the most appropriate resources (e.g., ambulance car or mobile unit). ii. Doctor Application / Telematics Module: Mobile units are supporting the transmission of vital signs and ECGs [45] directly to the Doctor’s application at the dispatching centre allowing for remote monitoring and management of the patient within the ambulance. iii. Administrative Module: It supports the analysis of the contents of the emergency incident archive, enabling the efficient and effective administrative decision making. iv. GPS/GIS Module: The dispatching centre supports a GPS satellite GIS system that depicts the exact position of the ambulance. [Figure 1 goes here] v. Triage Protocols Module: This module targets in the differentiation of urgent from non-urgent calls, to enable the implementation of a triage mechanism to effectively manage ambulatory resources. Protocol categories cover allergies, heart and respiratory failure, stroke, multi-trauma, abdominal and thoracic pain, labour, haemorrhage handling and others. Patient triage is a dynamic process involving repeated reassessment of the patient until the patient has received definitive treatment [46]. The deployment and use of HYGEIAnet services has demonstrated significant economic, clinical and access to care benefits. Furthermore, 65% of pre-hospital health emergency episodes have been managed by paramedics. With regard to e-health services in Cardiology, the evaluation study also revealed significant benefits. Detailed preliminary evaluation results have been reported in [74]. The service had a rather strong diagnostic impact: only in 9 out of 21 cases the patient was immediately transferred to the hospital. Since the health care centers are in remote locations and all the patients seen would normally have been referred and transported to the Regional hospital for evaluation, this represents a clear saving of time, cost and resources. The HYGEIAnet was a finalist in the 2005 eEurope awards. 4.3 Case Study 2: A Tele-Operated Robotic System for Mobile Tele-Echography: The OTELO Project [63] The objective of the European OTELO project was the development of an advanced tele-echography robotic system to enable a medical ultrasound expert, located at an expert site (e.g. the closest university hospital), to perform an echography on a distant patient located at an isolated site and to make a reliable echographic diagnosis. The OTELO system consists of the following three subsystems [63] as illustrated in Fig. 2: i. The expert station where the ultrasound expert receives, on a control monitor and in almost real time, the ultrasound images of the patient’s organ. Based on these images, he can modify the orientation of the remote real ultrasound probe by changing the position of the fictive probe. The changes can be a slight rotation along the fictive probe axis, an inclination from the vertical axis, a rotation around the vertical axis or an x-y displacement within the chosen workspace. A graphical user interface [64], allows the specialist to record, zoom and freeze the received ultrasound image to make a first diagnosis. The expert can communicate with the remote patient via videoconferencing. ii. The patient station is equipped with the robotic probe holder system, an ultrasound device and a videoconference link. The 6 degree-of-freedom mechanical structure can hold several types of currently manufactured ultrasound probes to be found in various secondary hospitals or dispensaries. It receives the positioning data from the expert fictive probe localisation sensor, and sends back to the expert station the force exerted by the real probe on the patient’s skin. During the tele-operated diagnosis, the robot is maintained over the patient by a paramedic. [Figure 2 goes here] iii. The communication link between the two stations can be either terrestrial via ISDN lines or via satellite, or more recently via 3G wireless communications. Echographic examinations were performed successfully using ISDN [76], satellite and 3G communication links [61]. Moreover the system was exploited, using satellite and 3 ISDN connections, between the Nicosia General Hospital, and the Kyperounta Medical Centre in Cyprus, with the University of Tours, and the Barcelona Hospital Clinic (see http://www.bourges.univ-orleans.fr/otelo/home.htm). These first results show the feasibility of that device and the possibility to obtain good views from the remote site. It allows the expert to make a comparable diagnosis than the one made with a standard echography system. Future work will exploit the use of the OTELO system in emergency cases, including the ambulance vehicle via 3G connectivity. 5. Future Challenges In this section, future challenges in the following areas are given: communication for wireless e-Emergency systems, computer technology, biosignals, emerging technologies on the transmission of wireless digital images and video, and other significant issues. 5.1 Communication for wireless e-Emergency systems Communication for wireless e-emergency healthcare systems, until today, was performed mainly using 2nd Generation Mobile Telecommunication Systems, such as the GSM which is a standard used almost everywhere in the world. During the last years, the introduction of new mobile telecommunication systems (2.5 Generation), like the GPRS system which provides much higher bandwidth (theoretically up to 171.2 kbps, typically about 35 kbps [2] enables the transmission of much more information which can prove useful for the healthcare providers and crucial for patient’s treatment. Recently, in many countries 3G mobile networks like the UMTS ([8] UMTS forum) are currently installed and operating, which provide bandwidth up to 2Mbps (maximum, typically hundreds of kbps) something that will enable the transmission of more information like continuous 12 leads of ECG when monitoring cardiac patients from a moving ambulance vehicle. Furthermore the current introduction of new services like video telephony through wireless networks will be an addition that can help with communications between a health care provider (nurse, paramedics) and an expert doctor. Another important factor is the installation of wireless networks in cities (e.g. WiMAX with tens of Mbps) something that will be able to significantly improve communication in wireless health care systems operating within city boundaries. WiMAX is currently being standardized, with some commercial applications installed already. The use of such networks will be very important because health care providers will have immediate and high speed telemedicine access from anywhere in the area of a city. Using sensor networks data gathering and computation can be deeply embedded in the physical environment. This has the potential to impact provision of e-ambulatory care, e.g. resuscitative care, see Lorinz et al. [77], by allowing vital signs to be automatically collected and fully integrated into the patient care record and used for real-time triage, correlation with hospital records, and long-term observation. Beyond these networks, the current activities in what is termed as the 4G (4th Generation) mobile networks, promise ubiquitous access to differing radio network technologies, thus offering, beyond extended coverage, also the most effective connection mode at the point of contact, even using simultaneously more than one wireless access technologies and seamlessly moving between them. The use of locating systems such as the GPS (Global Positioning System), the GIS (Geographical Information Systems), and intelligent traffic control systems also have potential to improve health care services. For example when a moving ambulance vehicle is trying to reach a patient using the fastest route, or when an Ambulance vehicle carrying a patient is trying to get to the base hospital. 5.2 Mobile Computing Technology Changes in commercial computer systems are rapid and continuous. New systems are presented every day. Modern portable computer systems have smaller size and weight but provide almost the same computational capabilities as non portable computer systems. The use of these devices in wireless telemedicine application is something that was presented some years ago but capabilities were limited due to the size or the computational capabilities of the systems. Nowadays the introduction of portable devices like PDAs, Smart Phones, Small Size laptops, Pen-Tablet PC’s is something that enables wireless telemedicine systems designers to create faster, better and smaller systems. Such efforts have already appeared and will continue to appear during the next years. In a recent study, it was shown that approximately 25% or more of the physicians use PDAs mainly however for personal information management and static medical applications, without exploiting the features of wireless internet connectivity [78]. 5.3 Biosignals Biosignals acquisition is another technological field which affects wireless telemedicine systems. The collection of biosignals [79] -[82], such as ECG, until now was performed using expensive devices which could only be handled and supported by medical personnel. Nowadays the collection of biosignals, such as ECG, can be performed by very small devices. These are not always devices on their own but they might connect to a PC in order to display the signals or to a mobile phone in order to send the signals, or even have Bluetooth or GPRS connectivity to wirelessly transfer the signals. They might be wearable, have the shape and weight of a necklace etc. These devices will enable the use of wireless telemedicine systems almost anywhere and at less cost. Such devices can be used for home care purposes much easier than the standard medical devices. 5.4 Emerging Technologies on the Transmission of Wireless Digital Images and Video The future needs of signal and image processing applications in e-health will involve a multitude of different signals, ranging from one-dimensional signals such as the ECG to real-time color video signals. There will also continue to be strong demand to move more and more services to smaller, low-power, compact computing devices. The challenge to e-health signal and image processing systems is to deliver the highest possible quality while minimizing the computational power and bandwidth requirements. We discuss future challenges from the perspective of the development of real-time, collaborative systems. There has been substantial progress made in the processing and analysis of one-dimensional biomedical signals (see [83]). Their bandwidth requirements can usually be met, and their strong diagnostic value makes them an essential part of most future collaborative systems. They are essential for continuous, real-time monitoring. Clearly, if a medical condition can be detected using a one-dimensional signal, such as the ECG, then we should avoid using images and/or video to accomplish the same task. In joint-processing, it is important to consider the use of one-dimensional signals to reduce bandwidth and computational requirements of higher-dimensional signals. Furthermore, we should consider scalable coding systems, where one-dimensional biomedical signals belong to the base-layer with strong protection from transmission error. Due to the high-bandwidth requirements, image and video compression methods will continue to play an important role in future, real-time collaborative environments (see [49], [63]). At the most basic level, medical image quality assessment is an important area of future research (see http://live.ece.utexas.edu/research/Quality/index.htm). Traditional mean-square error measurements do not necessarily correspond to perceptual quality, and may correspond even less to diagnostic quality. As an example, image quality assessment over the near-regions of ultrasound video is not useful, while in general, the users expect the highest quality in regions that are in-focus of the ultrasound beam. In addition, there will continue to be strong interest in region of interest (ROI) and object-based coding methods. The challenges associated with applying these methods require the development of effective segmentation methods. Scalable image and video coding will see continual development [84]. For medical imaging applications, there are many challenges in defining diagnostically relevant scalable methods. There are obvious applications in object-based scalability, not only where the object is of diagnostic interest, but also in defining the base layer and enhancement layers so that the base layer is of diagnostic significance. In transmitting video images of the patient, on-going and future research on facial image coding will be important in determining the patient's feelings and reactions during medical exams. There is also a need to consider new image compression models that correspond more closely with the structured texture and image acquisition characteristics of medical video. From the wireless communications perspective, video image compression research will continue in areas such as error-resilience and error-concealment [85], [86]. Especially for error-resilience methods, there will be strong interest in new encoding schemes that allow for robust decoding. For medical imaging applications, a small percentage of errors could be tolerated and their effects minimized through error- concealment. Future research in error-concealment methods should take into account the complex nature of biomedical images. Over a single video, different interpolation schemes should be employed for text objects, background, and texture objects. We should also consider the development of new interpolation methods that are a function of well-established imaging parameters such as the use of different interpolation methods for near-field and in-focus regions in ultrasound video. For in-focus regions, we can consider accurate, yet computationally expensive methods. On the other hand, we can use fast and somewhat less accurate methods for near-field regions. Many signal and image processing challenges lie in the joint processing and transmission of biomedical signals, images (see [49]). We list challenges in three areas: (i) multi-modality signal synchronization, (ii) joint signal, image and video compression, and (iii) interactive collaborative environments. The basic application is the development of high-quality collaborative environments, where a variety of biomedical signal and images are exchanged. For joint decoding, there are significant synchronization issues. Clearly, the one-dimensional biomedical signals should correspond to the video images. As an example, we note the synchronization of the ECG, respiratory, 2D, and Doppler signals in ultrasound systems. In addition, we note that two-way voice communications must be synchronized to all clinical signals, as well as to real-time video images of the patient and the doctor. Re-synchronization in the presence of wireless communication errors will require innovative error-resilience methods. For well-synchronized signals and images, we can develop methods for joint signal, image and video compression. We offer an example in video image compression. In cardiac imaging using ultrasound, the ECG signal can be used to predict changes in the video imaging signal. Thus, we can appropriately adapt the rate requirements, so as to allocate more rate to capture significant motion, while allocating less rate for anticipated less motion. Scalable coding for jointly coded signals, images, and video also presents significant challenges. It is important to decide how to jointly break the one- dimensional biomedical signals with the voice, images and video to form independently encoded blocks. Then, the base and enhancement layers in such layers should be decided based on diagnostic measures, as well as with regards to available bandwidth. Clearly, we will require spatiotemporal scalability, as well as object-based (or region-based) quality scalability. In addition, in the future, we want to consider content- based access for the jointly encoded signals. There are many challenges associated with the use of interactive, collaborative environments. As an example, all the MPEG-2/MPEG-4 functionalities (see [87], [88]) need to be re-thought of, in the context of synchronized, jointly-compressed signals. The users may be reviewing a particular signal, asking to see the corresponding signals (images or video) from other modalities. Such an interactive preview capability requires the development of fast joint-decoding methods. For real-time collaborative work, the heterogeneity of the networks, computing systems and image displays, will be best served by innovative, scalable, network-aware systems. In conclusion, we note that the high quality, robust, requirements of e-health systems will only be met by addressing particular clinical needs. 5.5 Other Significant Issues Legal, liability, ethical issues as well as the workflow of m-health services [89] will have to change to enable the effective and efficient use of these systems. Starting from legal issues the introduction of new services will have to be covered by several laws National, EU laws in the case of European countries or Federal laws for the United States. These laws will have to cover all issues, including the responsibilities during an emergency or home care incident. Furthermore, the liability of systems will have to be covered by standards which will describe everything that a system should follow. Even though there has been significant effort in creating a standard for collecting and exchanging biosignals [90], [91] (like DICOM for images) no standard is widely used by manufacturers of commercial biosignal monitors. Such issues will need to be resolved in the near future in order to cover liability and interoperability of medical devices. On the other hand several ethical issues will also have to be covered when using these systems; such issues concern the exchange of medical information through public networks thus having potential problems with security. This is currently being addressed with effective security systems available, however the tradeoff between a ‘heavy’ security system (thus impacting on system load and friendliness) versus a lean implementation with just adequate security is still a matter of intense research [92]. 6. Concluding Remarks This paper reviews wireless technologies and emerging wireless video systems for reliable communications. It also provides an overview of recently published wireless emergency healthcare systems, in which some of the reviewed technology is presented. These systems clearly demonstrate the benefits and the need for their wider deployment. Even though, Einthoven in 1906 demonstrated the successful transmission of ECG [35], and Grim in 1987 transmitted error-free 12-lead ECG over a wireless network [37], the wide use of e-emergency systems including the monitoring of ECG for prehospital care is still lacking. Similarly, the transmission of echography video in teleradiology for various organs using satellite connections has been proven feasible and successful in numerous cases [59], [63]. Early security concerns are currently been addressed and successful secure e-health applications are rapidly becoming commercialized, with many well known health and IT vendors appearing in the marketplace. However, in a recent study carried out by the World Health Organization (WHO) on e-health tools and services including m-health, it was concluded that countries need: support in the adoption of policy and strategy for the development for e-health; advice on needs assessment and evaluation of eHealth services; information on best practice and trends; and advice on e-health norms and standards. That is countries require consultancy services to assist in all aspects of e-health, and a need for education and training in this area [93]. The know-how and technology developed lately in disaster management is leading to the development of new approaches to emergency evaluation, triage, and treatment in prehospital and hospital care and services [94]. The ability to provide timely “hands-on” expertise to the trauma patient, irrespective of the specialist’s location, facilitates the potential for real advancement in the field. However, m-health e-emergency is still largely undeveloped. The success, experience, and benefits of clinical services in emergency telemedicine have only recently been published on a large scale of emergency cases by the telemedicine program at the State University of New York at Buffalo, School of Medicine, and the Erie County Medical Center (UB/ECMC) [95]. In addition, it was shown that the use of emergency telemedicine services could result in an approximately 15% decrease in ambulance transports when it is added to the prehospital care provider's services, with emphasis given on younger subjects [96]. More convincing studies similar to these ones are encouraged in order to help in the wider deployment of e- Emergency systems. In particular the transmission of echography video in the monitoring of pre-hospital subjects in cardiac emergencies (as for example using wireless LAN connections [66] and in trauma cases using satellite connections [62]) has been demonstrated in only a very limited number of cases. It is expected that the recent wide availability of portable ultrasound systems, the wide availability of 3G systems and beyond, and the further development of video systems will facilitate the spread of video systems both for the transmission of ultrasound exams, as well as for the transmission of subject video and teleconferencing applications. Concluding, it is expected that m-health e-emergency systems will significantly affect the delivery of healthcare; however, their exploitation in daily practice as well as the monitoring and evaluation of these systems still remains a novel goal, yet to be achieved. Acknowledgements This study was partly supported through the EU European Regional Development Fund, INTERREG III B Archimed Program, projects: i. A Mediterranean Research and Higher Education Intranet in Medical and Biological Sciences, and ii. 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