Carmen C. Y. Poon

A novel biometrics method to secure wireless body area sensor networks for telemedicine and m-health

The development of the wireless body area sensor network (BASN) is imperative for modern telemedicine and m-health, but security remains a formidable challenge yet to be resolved. As nodes of BASN are expected to be interconnected on or in the human body, the body itself can form an inherently secure communication pathway that is unavailable to all other kinds of wireless networks. This article explores the use of this conduit in the security mechanism of BASN; that is, by a biometrics approach that uses an intrinsic characteristic of the human body as the authentication identity or the means of securing the distribution of a cipher key to secure inter-BASN communications. The method was tested on 99 subjects with 838 segments of simultaneous recordings of electrocardiogram and photoplethysmogram. By using the interpulse interval (IPI) as the biometric trait, the system achieved a minimum half total error rate of 2.58 percent when the IPIs measured from signals, which were sampled at 1000 Hz, were coded into 128-bit binary sequences. The study opens up a few key issues for future investigation, including compensation schemes for the asynchrony of different channels, coding schemes, and other suitable biometric traits.

Unobtrusive Sensing and Wearable Devices for Health Informatics

The aging population, prevalence of chronic diseases, and outbreaks of infectious diseases are some of the major challenges of our present-day society. To address these unmet healthcare needs, especially for the early prediction and treatment of major diseases, health informatics, which deals with the acquisition, transmission, processing, storage, retrieval, and use of health information, has emerged as an active area of interdisciplinary research. In particular, acquisition of health-related information by unobtrusive sensing and wearable technologies is considered as a cornerstone in health informatics. Sensors can be weaved or integrated into clothing, accessories, and the living environment, such that health information can be acquired seamlessly and pervasively in daily living. Sensors can even be designed as stick-on electronic tattoos or directly printed onto human skin to enable long-term health monitoring. This paper aims to provide an overview of four emerging unobtrusive and wearable technologies, which are essential to the realization of pervasive health information acquisition, including: 1) unobtrusive sensing methods, 2) smart textile technology, 3) flexible-stretchable-printable electronics, and 4) sensor fusion, and then to identify some future directions of research.

Cuff-less and Noninvasive Measurements of Arterial Blood Pressure by Pulse Transit Time

The current blood pressure (BP) measurement devices are mostly built on the principle of auscultation, oscillometry or tonometry, all of which use an inflatable cuff to occlude or unload the artery. The need of a cuff in these devices limits the further reduction in size and power consumption, and restricts the frequency and ease of their usage. Therefore, this study aims to develop a cuff-less and noninvasive technique for measuring BP by pulse transit time. The technique was evaluated on 85 subjects, aged 57plusmn29 yrs., including 36 males and 39 hypertensives, over an average period of 6.4 wks. A total of 999 pairs of systolic BP (SBP) and diastolic BP (DBP) estimations were made. The average of BP readings reported by an experienced registered nurse and those obtained from a clinically approved automatic BP meter was used as reference. It is found that the estimated SBP and DBP differed from the reference BP by 0.6plusmn9.8 mmHg and 0.9plusmn5.6 mmHg respectively. When compared to the AAMI requirement (5plusmn8 mmHg for both SBP and DBP estimation), the results suggest that the cuff-less technology has great potential to be developed into wearable devices that are useful in self BP monitoring for home healthcare and eventually in clinical diagnosis

Wearable Medical Systems for p-Health

Driven by the growing aging population, prevalence of chronic diseases, and continuously rising healthcare costs, the healthcare system is undergoing a fundamental transformation, from the conventional hospital-centered system to an individual-centered system. Current and emerging developments in wearable medical systems will have a radical impact on this paradigm shift. Advances in wearable medical systems will enable the accessibility and affordability of healthcare, so that physiological conditions can be monitored not only at sporadic snapshots but also continuously for extended periods of time, making early disease detection and timely response to health threats possible. This paper reviews recent developments in the area of wearable medical systems for p-Health. Enabling technologies for continuous and noninvasive measurements of vital signs and biochemical variables, advances in intelligent biomedical clothing and body area networks, approaches for motion artifact reduction, strategies for wearable energy harvesting, and the establishment of standard protocols for the evaluation of wearable medical devices are presented in this paper with examples of clinical applications of these technologies.

Big Data for Health

This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.

Using the Timing Information of Heartbeats as an Entity Identifier to Secure Body Sensor Network

Security of the emerging body sensor network (BSN) in telemedicine applications is a crucial problem because personal medical information must be protected against flaws and misdeeds. The solution is, however, nontrivial because lightweight mechanisms have to be deployed to meet the stringent resource constraints of these networks. It has been suggested that the inherent ability of human body to transfer information is a unique and resource-saving method to secure wireless communications within a BSN. For example, physiological characteristics can be captured by individual sensors of a BSN to generate entity identifiers (EIs) for identifying nodes and even securing keying materials, i.e., by a biometric approach. This study demonstrates the performance analysis of such a biometric trait, i.e., the interpulse intervals (IPIs) of heartbeats that were calculated from electrocardiogram and photoplethysmogram of 99 subjects. Based on the characteristics of IPIs, a lightweight generation scheme of EIs is proposed. Individual randomness and group similarity of the generated EIs are then evaluated. False acceptance rate and false rejection rate are also calculated to measure the effectiveness of the proposed identification system. The results suggest that the readily available IPI information can be a good source for generating EIs among BSN nodes.

A high-sensitivity near-infrared phototransistor based on an organic bulk heterojunction

High-gain photodetectors with near-infrared (NIR) sensitivity are critical for biomedical applications such as photoplethysmography and optical coherence tomography where detected optical signals are relatively weak. Current photodetection technologies rely on avalanche photodiodes and photomultipliers to achieve high sensitivity. These devices, however, require a high operation voltage and are not compatible with CMOS based read-out circuits (ROCs). In this work we demonstrate a solution-proceeded NIR phototransistor structure based on a bulk heterojunction (BHJ) of a narrow bandgap polymer, poly(N-alkyl diketopyrrolo-pyrrole dithienylthieno[3,2-b]thiophene) (DPP-DTT), and [6,6]-phenyl-C61-butyric acid methylester (PCBM). The device exhibits ultrahigh responsivity (∼5 × 10(5) A W(-1)) as well as wide tunability (>1 × 10(4)) of photoconductive gain. Using the current-voltage and transient photocurrent measurements we show that the high responsivity is due to the combined effects of fast transport of holes in the polymer matrix and slow detrapping of electrons from the isolated PCBM domains. The wide gain tunability and the efficient suppression of noise current are achieved through the use of the optically tunable gate terminal. We demonstrate that our phototransistor can be used as the detection unit in a photoplethysmography sensor for non-invasive, continuous finger pulse wave monitoring. The high-sensitivity of the phototransistor allows the use of a low-power light source, thus reducing the overall power consumption of the sensor. This, together with the solution processibility and the simple device configuration (which is compatible with conventional ROCs), make the phototransistor a very promising component for the next generation low-cost, mobile biomedical devices for health monitoring and remote diagnostics.

Guest Editorial Body Sensor Networks: From Theory to Emerging Applications

The use of sensor networks for healthcare, well-being, and working in extreme environments has long roots in the engineering sector in medicine and biology community. With the maturity of wireless sensor networks, body area networks (BANs), and wireless BANs (WBANs), recent efforts in promoting the concept of body sensor networks (BSNs) aim to move beyond sensor connectivity to adopt a system-level approach to address issues related to biosensor design, interfacing, and embodiment, as well as ultralow-power processing/communication, power scavenging, autonomic sensing, data mining, inferencing, and integrated wireless sensor microsystems. As a result, the system architecture based on WBAN and BSN is becoming a widely accepted method of organization for ambulatory and ubiquitous monitoring systems. This editorial paper presents a snapshot of the current research and emerging applications and addresses some of the challenges and implementation issues.

An Armband Wearable Device for Overnight and Cuff-Less Blood Pressure Measurement

24-h blood pressure (BP) has significant prognostic value for cardiovascular risk screening, but the present BP devices are mainly cuff-based, which are unsuitable for long-term BP measurement, especially during nighttime. In this paper, we developed an armband wearable pulse transit time (PTT) system for 24-h cuff-less BP measurement and evaluated it in an unattended out-of-laboratory setting. Ten healthy young subjects participated in this ambulatory study, where PTT was measured at 30-min interval by this wearable system and the reference BP was measured by a standard oscillometric ambulatory BP monitor. Due to the misalignment of BP and PTT on their recording time caused by the different measurement principles of the two BP devices, a new estimation method has been proposed: transients in PTT were removed from the raw data by defined criteria, and then evenly interpolated, low-pass filtered, and resampled to synchronize at the time when BP was recorded. The results show that with the proposed method, the correlation between PTT and systolic BP (SBP) during nighttime with dynamic range of 21.8 ± 14.2 mmHg has improved from -0.50 ± 0.24 to -0.62 ± 0.20 (p<;0.1), and the difference between the estimated and reference SBP has improved from 0.7 ± 10.7 to 2.8 ± 8.2 mmHg with root mean square error reduced from 10.7 to 8.7 mmHg. In addition, the correlation between a very low frequency component of SBP and PTT obtained from the proposed method during nighttime is -0.80 ± 0.10 and the difference is 2.4 ± 5.7 mmHg for a dynamic BP range of 13.5 ± 8.0 mmHg. It is therefore concluded from this study that the proposed wearable system has great potential to be used for overnight SBP monitoring, especially to measure the averaged SBP over a long period.

Perspectives on High Technologies for Low-Cost Healthcare

This article discusses some of the unique demographic and epidemiological changes that China faces. As China is still a developing country, most of its people cannot afford expensive healthcare solutions. Especially in the poor rural areas, healthcare service is a luxury for some people. Therefore, this article summarizes several key strategies to reduce medical expenditures at the national level and proposes to develop a new information system in the form of a personal, home, community, and hospital (PHCH) four-layered architecture. Using the management of blood pressure (BP) as an example, we have shown that innovative technologies in wearable medical devices and body area networks (BANs) can be developed to collect information for this new system to overcome geographic and financial constraints and to provide a low-cost and effective solution to manage chronic health problems.