Читать книгу Body Sensor Networking, Design and Algorithms - Saeid Sanei - Страница 12

Wearable technology including sensors, sensor networks, and the associated devices has opened its space in a variety of applications. Long-term, noninvasive, and nonintrusive monitoring of the human body through collecting as much biometric data and state indicators as possible is the major goal of healthcare wearable technology developers. Patients suffering from diabetes need a simple noninvasive tool to monitor their blood sugar on an hourly basis. Those suffering from seizure require the necessary instrumentation to alarm them before any seizure onset to prevent them from fall injury. The stroke patients need their heart rate recorded constantly. These are only a small number of examples which show how crucial and necessary wearable healthcare systems can be.

At the Wearable Technology Conference in 2018, the winners of seven wearable device producers were introduced. These winners include the best ones in Lifestyle with the objective of ‘play stress away’; Sports and Fitness for making a football performance device, healthcare for developing a smart eyewear with assistive artificial intelligence capabilities for the blind and visually impaired; Industrial for designing a unique smart and connected industry 4.0 safety shoe; Smart Clothing Challenge for the nonintrusive acquisition of heart signals that will enable pervasive health monitoring, emotional state assessment, drowsiness detection, and identity recognition; Smart Lamp, which allows you to move the light in any direction without moving the lamp; and Connected Living Challenge, for creating accessories linking braintech with fashion design. Headpieces and earrings use electroencephalography (EEG) technology, capturing and providing users with brain data, allowing them to be conscious of their mental state in real time, for example for reducing anxiety and depression or increasing focus or relaxation of the user [1]. This simple example together with the above examples clearly show the diversity in applications of wearable technology. The aim of this book is therefore to familiarise readers with sensors, connections, signal processing tools and algorithms, electronics, communication systems, and networking protocols as well as many applications of wearable devices for the monitoring of mental, metabolic, physical, and physiological states of the human body.

Disease prevention, patient monitoring, and disable and elderly homecare have become the major objectives for investment in social health and public wellbeing. According to the World Health Organization (WHO), an ageing population is becoming a significant problem and degenerative brain diseases, such as dementia and depression, are increasingly seen in people while a bad lifestyle is causing millions of people to suffer from obesity or chronic diseases. It is thus reasonable to expect that this circumstance will only contribute to an ongoing decline in the quality of services (QoSs) provided by an already overloaded healthcare system [2]. A remote low-cost monitoring strategy, therefore, would significantly promote social and clinical wellbeing. This can only be achieved if sufficient reliably recorded information from the human body is available. Such information may be metabolic, biological, physiological, behavioural, psychological, functional, or motion-related.

On the other hand, the development of mobile telephone systems since the early 1990s and its improvement till now together with the availability of large size archiving and wideband communication channels significantly increase the chance of achieving the above objectives without hospitalising the caretakers in hospitals and care units for a long time. This may be considered a revolution in human welfare. More effective and efficient data collection from the human body has therefore a tremendous impact and influence on healthcare and the technology involved. The state of a patient during rest, walking, working, and sleeping can be well recognised if all the biomarkers of the physiological, biological, and behavioural changes of human body can be measured and processed. This requirement sparks the need for deployment of a multisensor and multimodal data collection system on the body. A body sensor network (BSN) therefore is central to a complete solution for patient monitoring and healthcare. Several key applications benefit from the advanced integration of BSNs, often called body area networks (BANs), with the new mobile communication technology [3, 4].

The main applications of BSNs are expected to appear in the healthcare domain, especially for the continuous monitoring and logging of vital parameters of elderly people or patients suffering from degenerative diseases such as dementia or chronic diseases such as diabetes, asthma, and heart attacks. As an example, a BAN network on a patient can alert the hospital, even before they have a heart attack, through measuring changes in their vital signs, or placing it on a diabetic patient could auto-inject insulin through a pump as soon as their insulin level declines.

The IEEE 802.15 Task Group 6 (BAN) is developing a communication standard optimised for reliable low-power devices and operation on, in, or around the human body (but not limited to humans) to serve a variety of applications including medical, consumer electronics/personal entertainment, and security [5]. This was approved on 22 July 2011 and the first meeting of IEEE 802.15 wireless personal area network (WPAN) was held on 3 March 2017.

The BSN technology benefits from developments in various areas of sensors, automation, communications, and more closely the vast advances in wired and wireless sensor networks (WSNs) for short- and long-range communications and industrial control. For interconnecting multiple appliances, for example, some developed their own personal area network (PAN). One was by Massachusetts Institute of Technology (MIT) which was later expanded by Thomas G. Zimmerman to interconnect different body sensors and actuators to locate the human through the measures performed by electric field sensors. He introduced the PAN technology by exploiting the body as a conductor. Neil Gershenfeld, a physician at MIT, did the major work on near-field coupling of the field and human body tissue for localisation [6]. By fixing pairs of antennas on the body, for example around the elbow and hand, and applying an electric current through them, they showed that the system is capable of tracking the person. They learnt that as one moves a capacitance in their circuit is charged. So, they can locate the antennas in places where there is maximum change in the movement between them.

BSNs have their root within WSNs. Like many advanced technologies, the origin of WSNs can be seen in military and heavy industrial applications. The first wireless network which had some similarity with a modern WSN is the sound surveillance system (SOSUS), developed by the United States military in the 1950s to detect and track Soviet submarines. This network used submerged acoustic sensors – hydrophones – distributed in the Atlantic and Pacific oceans. This sensing technology is still in service, though for many different objectives, from monitoring undersea wildlife to volcanic activity [7]. Echoing the investments made in the 1960s and 1970s to develop the hardware for today's Internet, the United States Defense Advanced Research Projects Agency (DARPA) started the Distributed Sensor Network (DSN) programme in 1980 to formally explore the challenges in implementing distributed WSNs. With the birth of DSN and its progression into academia through partnering universities such as Carnegie Mellon University and the MIT Lincoln Laboratory, WSN technology soon found its place in academia and civilian scientific research.

Governments and universities eventually began using WSNs in applications such as air quality monitoring, forest fire detection, natural disaster prevention, weather stations, and structural monitoring. Then as engineering students made their way into the corporate world of the technology giants of the day, such as IBM and Bell Labs, they began promoting the use of WSNs in heavy industrial applications such as power distribution, wastewater treatment, and specialised factory automation.

Although BSNs' objective and technology have their own requirements, they owe their birth and early development, particularly with regards to data communication, to the WSN technologies, which enable fruitful use of permitted wireless communication features and frequency range.

BSNs are also called wireless body area networks (WBANs) as often the transmission is through wireless systems. In their current form, BSNs are wireless networks of wearable devices with recording and some processing capabilities [4, 7–9]. Such devices may be embedded inside the body, implants, surface-mounted on the body in fixed positions, or carried in one way or another [10]. From its start of development, there have been tremendous attempts in reducing the size and cost, and increasing the flexibility, of such devices–particularly those with direct contact with the human body [11, 12]. The development of BSN technology started in 1995 around the idea of using WPAN technologies to implement communications on, near, and around the human body. Later in early 2000, the term ‘BAN’ came to refer to the systems where communication is entirely within, on, and in the immediate proximity of a human body [13, 14]. A WBAN further expands WPAN wireless technologies as gateways to reach longer ranges. Through gateway devices, it is possible to connect wearable devices on the human body to the Internet. This allows medical professionals to access patient data online using the Internet independent of patient location [15].

BSNs have opened two important fronts in research and technology: one as a measuring tool in health and the other as an integral part of the public network. Such networks have tremendous applications in healthcare [16–18], sports, entertainment [19–21], industry, the military, and surveillance [22], assistive technology [23], and interactive and collaborative computer games [24] and other social public fields [25–27]. In parallel with introducing and supplying new sensors, embedding electronic circuits as well as mobile applications and gadgets (Google glass, wristband, armband, headband, watch, and mobile with more biological data recording capabilities), which can be conveniently mounted on human body, the research and development in BSN technology continue apace. The key BSN applications, stated above, benefit from the advanced integration of BANs and emerging wireless technologies. For example, in remote health/fitness monitoring, health and motion information are monitored in real-time and delivered to nearby diagnostic or storage devices, through which the data can be forwarded to off-site clinical unites for further inspection. In military and sports training the motion sensors can be worn on both hands and elbows for tracking the movement and accurate feature extraction of sports players' movements. In interactive gaming, body sensors enable players to simulate and perform actual body movements, such as boxing and shooting, that can be fed back to the gaming console, thereby enhancing their entertainment experiences. Or for personal information sharing any private or business information can be stored in body sensors for many daily life applications such as shopping, activity monitoring, and information exchange. Finally, in secure authentication both physiological and behavioural biometrics – such as facial patterns, fingerprints, and iris recognition – can be restored and shared with authorities all over the world. In such cases, potential problems, such as forgery and duplicability, have motivated investigations into more and new physical/behavioural characteristics of the human body, by means of other measurements, such as EEG, gait information, and multimodal biometric systems.

BSNs may also be considered a subset of WSN often used in various industrial applications to monitor a large connected system. In many cases, however, each group of sensors, such as those for an EEG, can be wired up to a central recording system, such as the EEG machine, which can then be processed together. For BSNs the sensors often sample the physiological and metabolic variables from human body. Using BSNs for health monitoring, the necessary warning or alarming states for risk prevention can be generated and the diagnostic data for long-term inspection by clinicians can be recorded and archived.

The main components of the BSN technology are sensors, data processing, data fusion, machine learning, and low- and long-rage communication systems. Groups of researchers in sensor design, microelectronics, integrated circuit fabrication, data processing, machine learning, short- and long- range communications, security, data science, and computer networking, as well as clinicians, have to work together to design an efficient and usable BSN.

The advances in sensor technology, data analytics for large datasets, distributed systems, new generation of communication systems, mobile technology, and cooperative networks have opened a vast research platform in BSN as an emerging technology and an essential tool for the future development of ubiquitous healthcare monitoring systems [28]. Researchers should (i) enable seamless data transfer through standards such as Bluetooth, ZigBee, or ultrawideband (UWB) Wi-Fi to promote information exchange and the efficiency of migration across networks and uninterrupted connectivity, (ii) the sensors used in an BSN should be of low complexity, small size lightweight, easy to use, reconfigurable, and compatible with the existing tools and software, (iii) the transmission should be secure and reliable, and (iv) the sensors should be convenient to use and ethically approved.

On the other hand, agile solutions for clinical problems require access to multimodal physiological, biological, and metabolic data as well as those related to body motion, behaviour, mode, etc., which may be captured by cameras. The fusion of multimodal information is itself a fascinating area of research within both computer science and engineering communities.

Looking at the BSN with respect to WSN, WSNs have more general applications. For example, they can be deployed to inaccessible environments, such as forests, sea vessels, swamps, or mountains. In such cases, many redundant or spare nodes may be placed in the environment, making more dense distribution of the sensors to avoid any negative impact of node failures. In BSNs, however, the nodes are located in clinically more informative zones around or even inside the human body. This makes the total number of nodes limited, and generally rarely more than a few dozen. Each node is mounted properly to ensure more robust and accurate results [29]. However, there are cases where the sensors are movable and deployed for short duration recordings. An example of such sensors is endoscopic capsules, also called esophagogastroduodenoscopy (EGD), for monitoring human intestine and internal abdomen tissues.

Also, in terms of functionality attributes, the nodes in WSNs often record data of the same modality (although, in recent applications, different modalities such as sound and video have been taken into account by WSNs), whereas, in BSNs, various sensors collect different physiological and biological data.

Some limitations in sensor design – such as their geometrical dimensions, weight, shape, appearance, and size – may be less important for the WSN nodes than those of BSNs. Different sensor types are used in a BSN for recording various data types from the human body [8]. For a WSN there may be large-size sensors which are very resistive to a rough and hostile environment. In BSNs the nodes are supported by more robust electronic circuits which are less sensitive to noise, such as well-tuned differential amplifiers, to enable the recording of very low amplitude signals such as scalp EEG or surface electromyography (EMG). The sensors are often small and delicate enough to be wearable, less intrusive, easily deployable within the human body, and in many cases biocompatible [30].

There are other considerations and limitations for BSNs, for example in many applications the human body is in motion and the BSN nodes move accordingly. Also, unlike for WSNs, where the nodes are powered by many sources such as the national grid, wind turbine, and solar cells, for conventional BSNs, the consumable energy should be optimised and batteries with limited power (though rechargeable) used [31, 32]. On the other hand, with regards to data transmission, the nodes in a WSN often transfer the data with similar rates as long as the data modality is the same. This is, however, not the case for a BSN, as various sensors sample and transfer the data at rates appropriate to the underlying physiological variables under examination.

Another concern about the data type in BSNs is that the human body is nonhomogeneous and each part is modelled as an entirely nonlinear system. Also, the physiological signals are inherently highly nonstationary, i.e. their statistical properties vary over time. Therefore, accurate analysis of such data is significantly more challenging than for other types of data, and many linear signal processing methods, therefore, are likely to fail to capture and analyse the true features of the data.

Additionally, BSNs are generally meant for monitoring human physiological, biological, and motion data, which are related to user's personal safety and privacy as well as other ethical issues. Therefore, some means of QoS, privacy protection, integrity, prosperity, and security in archiving and real-time data transmission must be considered [33, 34].

In terms of data communication through conventional wireless systems, WBANs support a variety of real-time health monitoring and consumer electronics applications. The latest standardization of WBANs is the IEEE 802.15.6 standard [35] which aims to provide an international standard for low-power, short-range, and extremely reliable wireless communication within the surrounding area of the human body, supporting a vast range of data rates for different applications. The security association in this standard includes four elliptic curve-based key agreement protocols that are used for generating a master key.

The Federal Communications Commission (FCC) has approved the allocation of 40 MHz of spectrum bandwidth for medical BAN low-power, wide-area radio links at the 2360–2400 MHz band. This allows off-loading WBAN communication from the already saturated standard Wi-Fi spectrum to a standard band [36].

Apart from 2390–2400 MHz band which is not subject to registration or coordination and may be used in all areas including residential, the 2360–2390 MHz frequency range is available on a secondary basis. The FCC will expand the existing Medical Device Radiocommunication (MedRadio) Service in Part 95 of its rules. WBAN devices using this band can operate on a ‘licence-by-rule’ basis, which eliminates the need to apply for individual transmitter licences. Usage of the 2360–2390 MHz frequencies is restricted to indoor operation at healthcare facilities and subject to registration and site approval by coordinators to protect aeronautical telemetry primary usage [37].