Abstract. Current wireless technologies, such as wireless body area networks (WBANs) and wireless personal area networks (WPANs), provide promising applications in medical monitoring systems to measure specified physiological data and also provide location-based information, if required. With the increasing sophistication of wearable and implantable medical devices and their integration with wireless sensors, an ever-expanding range of therapeutic and diagnostic applications is being pursued by research and commercial organisations. This paper aims to provide a comprehensive review of recent developments in wireless sensor technology for monitoring behaviour related to human physiological responses. It presents background information on the use of wireless technology and sensors to develop a wireless physiological measurement system. A generic miniature platform and other available technologies for wireless sensors have been studied in terms of hardware and software structural requirements for a low cost, low power, non-invasive and unobtrusive system. Keywords. Wireless sensor networks It is recognized that the growing global population is also an aging one. This trend, coupled with the daily developments in new diagnostic techniques and medication discoveries, will place increasing demand on medical and health-care resources. There are currently more than 650 million people over the age of 65, a number that will double over the next 10 years. In the US, about 20% of the population will be over 65 by 2030, compared to 12% today. Similarly, about 20% of the UK population will be over 65 by 2025 [1], as shown in Figure 1 . The rapidly growing aging population has resulted in an increase of chronic age-related diseases, such as Type 2 diabetes, cancer, congestive heart failure, chronic obstructive pulmonary disease (COPD), arthritis, osteoporosis, dementia and sleep apnea [2] . In addition, there are more than 1 billion adults worldwide today that are overweight; evidence from several studies indicates that obesity and weight gain are associated with an increased risk of diabetes. Nearly 17.5 million people (29% of the total population in the UK) now live with a long-term medical condition or disease, whilst as much as 80% of healthcare budgets is being spent on the management of chronic diseases [3] . In contrast, the world has just 200,000 hospitals with 18 million beds. There is an ever-increasing shortage of doctors and nurses and skilled ancillary personnel, and an increasing demand by citizens for healthcare services [4] . Meanwhile, information and communication technology (ICT) has become a central part of our daily life. Many people, including clinical practitioners, are equipped with mobile devices of some kind: pagers; cell phones; personal digital assistants (PDAs); and various mobile computing devices [4] . Today, over 50% of hospitals in the US have wireless local area networks (WLANs) and widely-accessible Wi-Fi and WiMax devices, enabling practitioners to access information, both at the point of care and anywhere else it is needed. Similarly, tablet PCs, PDAs and laptops connected to the WLAN allow clinicians to immediately record medical information in electronic format, as well as order tests and prescribe medication at the patient's bedside, all from their chosen device. Consumers can benefit from advanced home monitoring systems, tele-consultations, personalized care and individualized treatments [4] . The ever-increasing interest in wireless communications has resulted in the development of new technologies and applications for the personal use of radio frequencies. Technological advancement in integrated circuits (ICs), coupled with that of wireless technology and physiological sensors, opens up opportunities for developing small, low power, lightweight and intelligent physiological monitoring devices. These devices can form a Wireless Body Sensor Network (WBSN), launching a new era of using technology to unobtrusively obtain physiological measurements for improved well-being monitoring. This paper aims to give a comprehensive review on the use of wireless sensor technology for monitoring behaviour related to human physiological responses. Section 2 gives a brief introduction to wireless sensor networks and on-going applications for health-care scenarios. Section 3 presents a design approach and related wireless technologies for body-sensor networks (BSN). Current international standards related to wireless sensor technologies are reviewed; in addition, a recent proposal from GE Healthcare (GEHC) to the Federal Communications Commission (FCC) for the allocation of new radio spectrum for healthcare applications is discussed. Section 4 draws some conclusions. Medical telemetry systems were originally fixed installations connected to the patient with wires. These installations benefited from the advances in electronics (e.g. reductions in size of the equipment have occurred, allowing in-home use in some cases). However, the main limitations of such wired systems are that the patient is effectively tethered to one location and that the relative costs, including both the equipment and the need for trained staff, are such that patients are only observed for relatively short periods, usually under abnormal circumstances. Wireless systems have been investigated to overcome these issues. In the past, the use of wireless connectivity in health-care was mostly limited to ambulatory telemetry in hospitals. In the US, the FCC dedicated a part of the spectrum licensed for Wireless Medical Telemetry Service (WMTS) applications. Commercial products include ApexPro™ from GE Healthcare, a leading source of wireless medical telemetry systems. Since the frequency band is specified for low data rate and medical applications, there are hardly any issues of unavailability or interference, or other co-existence issues. Each node (one per patient) communicates with a network controller in a star-type architecture. Recently, the emergence of 802.11b WLAN technology, operating in the unlicensed 2.4 GHz band, has enabled the movement of patient monitors (e.g. the DASH® 4000 Pro, GEHC) around a hospital or health-care facility. It also allows patient data, such as demographics, medication history and examination results available from portable and bedside monitors, to be transmitted wirelessly to health-care professionals. The data rate to support this service is likely to be less than 1 Mbps. There are other applications that require higher data rates (up to 1 Gbps), including the transfer of X-ray and MRI images and interconnection of medical imaging devices. The WLAN network architecture will normally be star clusters around wireless access points. A wireless physiological measurement system (WPMS) is the use of a wireless medium to carry real-time physiological measurement data from wearable/implantable medical sensors to a central processing unit. The primary purpose of the WPMS is to improve the quality and efficiency of health-care [16] . Wireless physiological measurements hold a number of advantages over wired alternatives, including [17]: • ease of use; • reduced risk of infection; • reduced risk of failure; • reduced user discomfort; • enhanced mobility; • lower cost of care delivery. Wireless physiological measurements have not only found applications in health-care, but have also been applied in the areas of the military, security, sport and fitness monitoring. In the improvement of health-care delivery services, the first function of the WPMS is to alert its wearer of the approach or development of a potential medical emergency, so that precautionary action can be taken. Moreover, the use of wireless physiological measurement systems outside the hospital could also The third function of a wireless physiological measurement system is to measure a real-time bio-signal for local processing. A good example is an Automatic Internal Cardiac Defibrillator (AICD, also known as an Implantable Cardioverter Defibrillator, or ICD), which acts to restore the regular heart rhythm by delivering an electrical shock if abnormal behaviour is detected, potentially averting sudden cardiac death. Another example is an implantable drug delivery system: these deliver medication more efficiently for such applications as chemotherapy, pain management, diabetic insulin delivery and AIDS therapy, by locally processing wireless physiological measurements [18] . The wireless physiological measurement system can also provide real bio-signal information for post-processing. In the military, a WPMS implementation can facilitate remote non-invasive monitoring of vital signs of soldiers during training exercises and combat. For example, it can be used to remotely determine a casualty's condition by medics in a combat situation, without exposing first-responders to increased risks; or to quickly identify the severity of injuries and continuously track the injured condition until they arrive safely at a medical care facility. In safety and emergency, wireless physiological measurement information can also be useful for fire fighters, hazardous material workers, mountain climbers, or emergency firstresponders operating in harsh and hazardous environments. It can keep track of an injured person's vital signs, allowing rapid distribution of the information to medical providers and assisting emergency responders in making critical, and often life-saving, decisions in order to expedite rescue operations [19] . Wireless Physiological Measurement Systems can be classified into critical and non-critical applications, as shown in Table 1 below.  Monitoring chronically ill patients with heart disease, diabetes, epileptic (those that need alarms) Monitoring of physical conditions and efficiency of sport athlete during exercises. Monitoring at home and nursing home for  The FCC has recently indicated that "the health care industry has reached the beginning of a wave of breakthroughs in providing care and rehabilitation that will use radio communication technologies in a variety of ways" [16] . With recent advancements in wireless technology, it is possible to use wireless as never before in the delivery of health-care services. One of the most promising concepts on the horizon is that of the wireless body sensor network (WBSN). WBSNs typically consist of multiple sensor nodes worn on the body, each capable of sampling, processing and communicating wirelessly one or more physiological measurements or environmental parameters [20] . These physiological parameters are typically blood glucose and oxygen levels, pulse rate, blood pressure, circadian rhythm and wake-sleep patterns through actigraphy. Antenna components embedded in the sensor nodes make it possible for the data generated to be transmitted wirelessly to a body-worn or closely located hub device, eliminating the need for cables. The hub device, in turn, receives the data generated from the various sensor nodes on the body and may process the data locally, transmit it wirelessly via an appropriate radio link for centralized processing, display and storage, or both [16] . Another benefit of WBSNs is the greater spectrum efficiency that can be achieved. Traditional telemetry systems generally create a separate RF link to the remote monitoring station for each patient sensor. WBSNs utilize network bandwidth more efficiently, by creating a very short-range point-to-point wireless network, thereby enabling greater spectrum reuse. The most common approach to WBSNs is to use a mesh topology. This means that there are typically many links between nodes and each node will choose the best path through the network to the destination. Data transfer thus occurs in multiple "hops" between nodes. Mesh networks are generally self-forming: each node builds up a routing table of the network. This may, however, be calculated by a network controller node in some cases. One advantage of self-forming is that limited configuration is required, a particular need in environmental sensing over relatively wide geographic areas (e.g. in military applications), or if the nodes are mobile, making the network topology dynamic. Mesh networks are also relatively fault-tolerant, as the availability of multiple paths enables the network to handle individual nodes failures gracefully. However, some networks will have a fixed architecture and so may use a star or tree topology. Currently, there is no specific standard for WBSNs operating among other available wireless consumer technologies. The IEEE 802.15 group has recently initiated a special interest group concentrating on BANs for medical devices and additional specific uses; however, the focus is not broad enough for ultimate deployment. The group's purpose is to tackle the main challenges and constraints, such as power consumption, discovery and quality of service (QoS), in BANs. The main requirements of the initial study are support for very low power devices and sensors (with a target of less than 10% power consumption for communications compared to the total device) and to have a single standard with a broad range of supported data rates -scalability. The IEEE 802.15 Special Interest Group's first report concluded that (for low data rate applications) WBSNs should operate on, inside or in the vicinity of the body with limited range (<0.01 to 2 metres). The channel model should cover human body effects, including absorption and health effects. Extremely low consumption power (0.1-1mW) is required for each device, which may require them to be capable of energy scavenging/battery-less operation. The WBSN should support a scalable data rate (0.01-1000 kbps, optional 10 Mbps). Deployed devices would provide support for different classes of QoS for high reliability, asymmetric traffic and power-constrained scenarios. Operation of high number of simultaneous piconets (with a maximum of 100 devices per network) using application specific security/privacy is required. Wireless sensor nodes, also known as motes, are tiny microcomputers capable of doing small amounts of processing, sensing and using wireless media to communicate with other wireless sensor nodes in the sensor network. The motes combine processing, sensing and wireless networking into a single tiny package. The main components of a wireless sensor node are: a radio transceiver with antenna for wireless communication, microprocessor, memory, analogue or biochemical sensors (e.g. ECG, temperature, glucose) and a battery for electrical power. These motes typically use an operating system with a compact footprint, in terms of memory requirements and system overhead; the most common example is the opensource TinyOS software, coupled with the nesC programming language. Each wireless sensor node is strategically placed on the human body as a tiny patch or implant, or hidden in users' cloths, allowing ubiquitous physiological measurements in the natural environment over an extended period of time. The following are several kinds of physiological wireless sensor nodes: 1. Swallowed pills (containing a wireless transceiver and sensors that can detect enzymes, nucleic acids, intestinal acidity, pressure, contractions of intestinal muscle and other parameters) allow the WBSN to be involved in gastrointestinal disease monitoring in a non-invasive manner [21]; 2. Wired electrode sensors plus a local wireless device, such as a wireless ECG, with several wired electrodes put on the chest to measure the heart parameters; 3. Patch/portable sensors with a wireless transceiver mounted on the surface of human body (e.g. a ring-shaped sensor worn on the finger to monitor heart rate and blood pressure); 4. Implantable physiological sensors, such as an embedded glucose level monitor with an insulin injection system that could be implanted in the patient once to operate within the human body. The blood glucose monitor automatically sends blood sugar readings to a subcutaneous continuous insulin pump, which takes the glucose readings and other information entered by the user (such as target blood glucose, insulin sensitivity and insulin-to-carbohydrate ratios) and calculates the amount of insulin bolus needed to keep the blood glucose reading in a normal range; 5. Nano-physiological sensors with wireless communication are futuristic and exciting concepts. They use biodegradable nanomachines able to run through the bloodstream, taking physiological measurements and relaying the data wirelessly [21] . To permit prolonged ubiquitous monitoring and seamless integration into a WBSN, wireless sensor nodes must meet requirements for being low profile, lightweight and low cost and having low power  Wireless Physiological Measurement Systems, like other innovations, seek to reduce risk. However, any innovation introduces new risk. The major requirement and challenges for a wireless physiological measurement system are: • Reliability -the main challenge is to make sure that information reliably gets to its destination. The reliability of a wireless physiological measurement system relies on many aspects, such as reliable wireless communication between nodes, efficient computation in each sensor node and stable software programming [23]; • Biocompatibility -the shape, size and materials are restricted for sensors that directly act on the human body. One of the solutions is to package the sensor nodes in biocompatible materials [23] . • Portability -the size of the sensors used in wireless physiological measurement systems needs to be small and lightweight, whether they are swallowed or worn; • Privacy and security -there are big security issues to be considered, such as eavesdropping, identity spoofing (i.e. the assumption of a trusted user's security credentials during a communications session) and redirection of private data to unauthorized persons. Security can be improved using data encryption. It is necessary to protected private data from improper access and • Lightweight protocols for wireless communication -must support self-organising networks (including security aspects) and able to perform data collection and routing; • Energy-aware communication -it is desirable for nodes to transmit at low power. An energyaware protocol is necessary to allow nodes to negotiate their transmission power to a minimum; • RF radiation safety -the electromagnetic radiation must be within recommended SAR limits. In the United States, the FCC has set the safe exposure limit to a SAR level at or below 1.6 W/kg in 1 g of tissue. In Europe, the European Union Council has adopted the SAR limit of 2 W/kg in 10 g of tissue. The next task is to design a wireless physiological measurement system with low power consumption (to enable long term monitoring), no unwanted interference with other wireless systems and an efficient protocol due to limited computation capabilities. This system must be both extremely reliable and extremely secure, as it relates to the health and life of the user. Another consequence of this is that it must be robust to changes in the network topology (e.g. if one sensor node fails). With the increasing sophistication of wearable and implantable medical devices, and their integration with wireless sensors, ever expanding ranges of therapeutic and diagnostic applications are being pursued by research and commercial organisations. Internationally, much of the early work in WBSNs originated at Imperial College, London (ICL); the BSN Workshop series (www.bsn-web.org) initiated by ICL is now regarded as the major international forum for BSN research, attracting top research institutions and industrial organisations each year. The main difficulty is that, for wearable wireless communication devices to be practical and affordable, the antenna must be optimized. One of the most significant developments was arguably the establishment of TinyOS. This open source operating system for small wireless embedded devices was developed by Jason Hill as part of his PhD [26] and is now used in a significant proportion of academic research in this field and even as the basis for some commercially-produced operating systems (e.g. Crossbow's MoteWorks In the former category, there is significant effort invested in higher network functions, including selfconfigurability, security-related issues and quality of service (QoS). A policy-based approach was undertaken by Zhu et al (2008) , where a light-weight policy system called Finger was implemented in TinyOS and its performance evaluated, with benefits in the adaptability and security of sensor nodes [36] . Roman et al (2008) recently discussed the requirement for sensor nodes to be "situation-aware", together with mechanisms for achieving such behaviour [37] . This ability has benefits in self-configuration and security areas. Xiao (2008) has discussed the need for "accountability" in wireless networks, in order to properly determine the source of errors or malicious activity and also act as a mechanism to restore trust Table 2 . Table 3 summarizes some examples of on-going academic research in relation to the development of wireless physiological measurement systems.  Method and Design Approach for Wireless Sensors A number of parameters need to be considered when designing a miniature wireless sensor device. The application sets constraints and requirements for the device. The volume, shape and weight are important factors, particularly for wearable sensors. For a given application, aimed at wireless physiological measurements, operational lifetime, duty cycle, accuracy and stability of sensor output need to be considered. The most challenging scenario is 24-hour, round-the-clock monitoring, where even the incorporation of sensors into clothing may fall short of the goal, making implanted or skin-mounted devices necessary. Implantable devices have a level of invasiveness exceeding current mainstream acceptance. However, the necessity of skin-worn devices or skin-contacting devices is unavoidable in some cases, such as galvanic skin response. Devices embedded into clothing are certainly champions of unobtrusiveness, as long as their weight, shape and volume stay within limits [48] . To meet the design requirements, an investigation into various applicable wireless technologies must be performed. Communication in networks is a complex task. To simplify the problem, it is broken into a set of layers. Each layer provides a function or service to the layer above; communication is enabled through the building up of the layers. independence from how the data is represented) to the application (APP) layer, which is intended to provide commonly-required communications services to applications. In practice, most network protocol "stacks" relate only loosely to the OSI reference model. Figure 2 . The OSI reference model for data transfer across a network, based on an example given in [49] . The dashed arrows indicate the logical data transfer path; the solid arrows indicate the physical transfer path. Each layer adds additional bits to the "packet", as headers or footers to the data. These headers contain information in a fixed format, enabling the receiving node to recover the data from the received bits. ZigBee is an open, global standard providing a low data rate, low power consumption and low cost wireless technology. It is built on top of the IEEE 802.15.4 standard, defined for the PHY and Media Access (MAC) layers. The MAC layer loosely corresponds to the DAT layer in the OSI model. ZigBee specifies network, security, and application layers (see its protocol stack in Figure 3) . The MAC layer controls access to the radio channels using the CSMA-CA (Carrier Sense Multiple Access-Collision Avoidance) mechanism, together with an optional time-slot structure and security functionality. The MAC layer supports several network architectures, including the star, tree cluster and mesh topologies, allowing ZigBee to create a scalable, reliable and self-healing network. There are three categories of ZigBee devices, as shown in Figure 4 [51]: • ZigBee Coordinator (MAC Network Coordinator) -this is a smart node that automatically initiates the formation of the network and maintains overall network knowledge. It is the most sophisticated of the three devices, requiring the most memory and processing power; • ZigBee Router (MAC Full Function Device) -this is also a smart node that links groups together and provides multi-hoping for messages. It associates with other routers and end-devices; • ZigBee End Devices (MAC Reduced Function Device) -this node can only communicate with a full function device. It carries limited functionality to control cost and complexity. ZigBee provide authentication, encryption and integrity services for wireless systems through the security layer, with 128-bit AES encryption and authentication. The transmission range is from 10 to 75 metres, depending on the power output and environmental characteristics. ZigBee devices are expected to have a battery life ranging from months to years. Bluetooth, also known as IEEE 802.15.1, is a low cost, low-power wireless radio frequency standard for short distances, aimed at cable replacement between lightweight electronic devices and also building adhoc networks. The Bluetooth protocol stack, shown in Figure 5 , is somewhat unusual compared to other IEEE networking stacks. The Bluetooth stack defines many components above the PHY and MAC layers, some of which are optional, making it complex [50]. Bluetooth devices are divided into one of three classes, which specify the antenna's output power: • Class 1 devices broadcast using 1-100 mW of power; • Class 2 devices broadcast using 0.25-2.5 mW of power; and • Class 3 devices broadcast using up to 1 mW of power. The maximum data rate is 723 kbps at a maximum signal range of 100 m. Bluetooth devices form into piconets and, potentially, scatternets (i.e. groups of inter-connected piconets). Piconets consist of one master device that communicates directly with up to 7 active slave devices. Bluetooth defines three power-saving modes. In hold mode, devices only handle slots reserved for synchronous links, and sleep the rest of the time. In sniff mode, the device stays asleep most of the time, waking up periodically (from every 1.25 ms to every 40.9 s) to communicate. Finally, in parked mode, the device shuts down its links to the master device, excluding the PSB (Parked Slave Broadcast) link. The master device can wake up parked devices by beaconing them over the PSB link [50] . ZigBee looks rather similar to Bluetooth but it is much simpler. ZigBee offers a lower data rate and the nodes spend most of the time snoozing [52] . This characteristic means that ZigBee node can run at low power for a period ranging from six months to two years, on just two AA batteries. A Bluetooth node running on same batteries would only last between 1 and 7 days. Bluetooth supports a higher data rate of 750 kbps compared to the low data rates of ZigBee, the maximum being 250 kbps at 2.4 GHz. The simple architecture of ZigBee allows configuration of static and dynamic star networks, or peer-to-peer network that can support up to 65000 nodes in a network. In contrast, the complex architecture of Bluetooth only allows 8 nodes in a basic master-slave piconet configuration. ZigBee uses DSSS, which allows nodes to sleep without close synchronisation. When a ZigBee node goes into sleep mode, it can wake up and get data in around 15 ms. Bluetooth, on the other hand, uses FHSS, which does require close synchronisation; therefore, a Bluetooth node that goes into sleep mode would take around 3 s to wake up and respond. ZigBee is an emerging wireless standard for low data rate, very low-power applications. It has a maximum data rate of 250 kbps, which is still sufficient for intelligent sensors. ZigBee has a simple protocol stack and uses AES 128-bit authentication and security. ZigBee is scalable; it can support a large number of nodes and can respond quickly to topology changes. It is not prone to interference by other devices operating in the same frequency range. It is thus a very good candidate for internal communication by individual sensors in the WBSN. Bluetooth is a mature technology, already integrated in many cell phones and Personal Digital Assistant (PDA) devices. It allows communication bandwidth of up to 720 kbps, which is more than sufficient for most intelligent sensors. However, Bluetooth is too complex, power demanding and prone to interference by other devices operating in the same frequency range. It is better suited as an external communication technology to connect the WBSN to other networks or devices, such as a PDA or cell phone. A comparison of various short-range wireless technologies is shown below in Table 4 .  Apart from the aforementioned wireless technologies, there are other wireless communications protocols that may be considered for use in WBSNs. The first, and perhaps most obvious, set of protocols to consider are those based on the IEEE 802.11 family of standards, for use in WLANs. These generally use the 2.4 GHz band, although there is a second band at 5.8 GHz used in 802.11a-based networks. The main motivation for considering the Wi-Fi/WLAN approach is commercial: the rapid growth in mobile
Similarly, Hao et al. REF provided a comprehensive review of development in wireless sensor networks for human physiological monitoring.
Wireless body sensor networks for health-monitoring applications
Physiological measurement
Physiological measurement
Engineering Medicine