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.

M-Health can be defined as “mobile computing, medical sensor, and communications technologies for health-care.” This emerging concept represents the evolution of e-health systems from traditional desktop “telemedicine” platforms to wireless and mobile configurations. Current and emerging developments in wireless communications integrated with developments in pervasive and wearable technologies will have a radical impact on future health-care delivery systems. This editorial paper presents a snapshot of recent developments in these areas and addresses some of the challenges and future implementation issues from the m-Health perspective. The contributions presented in this special section represent some of these recent developments and illustrate the multidisciplinary nature of this important and emerging concept.

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.

Pulse transit time (PTT) has attracted much interest for cuffless blood pressure (BP) measurement. However, its limited accuracy is one of the main problems preventing its widespread acceptance. Arterial BP oscillates mainly at high frequency (HF) because of respiratory activity, and at low frequency (LF) because of vasomotor tone. Prior studies suggested that PTT can track BP variation in HF range, but was inadequate to follow the LF variation, which is probably the main reason for its unsatisfactory accuracy. This paper presents a new indicator, the photoplethysmogram intensity ratio (PIR), which can be affected by changes in the arterial diameter, and, thus, trace the LF variation of BP. Spectral analysis of BP, PTT, PIR, and respiratory signal confirmed that PTT was related to BP in HF at the respiratory frequency, while PIR was associated with BP in LF range. We, therefore, develop a novel BP estimation algorithm by using both PTT and PIR. The proposed algorithm was validated on 27 healthy subjects with continuous Finapres BP as reference. The results showed that the mean ± standard deviation (SD) for the estimated systolic, diastolic, and mean BP with the proposed method against reference were -0.37 ± 5.21, -0.08 ± 4.06, -0.18 ± 4.13 mmHg, and mean absolute difference (MAD) were 4.09, 3.18, 3.18 mmHg, respectively. Furthermore, the proposed method outperformed the two most cited PTT algorithms for about 2 mmHg in SD and MAD. These results demonstrated that the proposed BP model using PIR and PTT can estimate continuous BP with improved accuracy.

Proposes a fuzzy approach to classify single-site electromyograph (EMG) signals for multifunctional prosthesis control. While the classification problem is the focus of this paper, the ultimate goal is to improve myoelectric system control performance, and classification is an essential step in the control. Time segmented features are fed to a fuzzy system for training and classification. In order to obtain acceptable training speed and realistic fuzzy system structure, these features are clustered without supervision using the Basic Isodata algorithm at the beginning of the training phase, and the clustering results are used in initializing the fuzzy system parameters. Afterwards, fuzzy rules in the system are trained with the back-propagation algorithm. The fuzzy approach was compared with an artificial neural network (ANN) method on four subjects, and very similar classification results were obtained. It is superior to the latter in at least three points: slightly higher recognition rate; insensitivity to overtraining; and consistent outputs demonstrating higher reliability. Some potential advantages of the fuzzy approach over the ANN approach are also discussed.