A Method of Secure Compressed Sensing for Physiological Signals in Healthcare Internet-of-Things Applications.

With the increasing demand forinformation interaction and data transmission in medical and healthcare Internet of Things applications, effective and secure transmissions of data become particularly important. To address this problem, this paper focuses on a novel method of secure compressed sensing, which can be readily applied to physiological signals and other kinds of health signals. The method is able to efficiently reduce the sampling data and at the same time secure them without an extra significant computational cost, where a key is bound to the compressed sensing process with a symmetric cryptography design.

A Method of Signal Scrambling to Secure Data Storage for Healthcare Applications.

A body sensor network that consists of wearable and/or implantable biosensors has been an important front-end for collecting personal health records. It is expected that the full integration of outside-hospital personal health information and hospital electronic health records will further promote preventative health services as well as global health. However, the integration and sharing of health information is bound to bring with it security and privacy issues.

Fusion of Inertial/Magnetic Sensor Measurements and Map Information for Pedestrian Tracking.

The wearable inertial/magnetic sensor based human motion analysis plays an important role in many biomedical applications, such as physical therapy, gait analysis and rehabilitation. One of the main challenges for the lower body bio-motion analysis is how to reliably provide position estimations of human subject during walking. In this paper, we propose a particle filter based human position estimation method using a foot-mounted inertial and magnetic sensor module, which not only uses the traditional zero velocity update (ZUPT), but also applies map information to further correct the acceleration double integration drift and thus improve estimation accuracy.

A method of ECG template extraction for biometrics applications.

ECG has attracted widespread attention as one of the most important non-invasive physiological signals in healthcare-system related biometrics for its characteristics like ease-of-monitoring, individual uniqueness as well as important clinical value. This study proposes a method of dynamic threshold setting to extract the most stable ECG waveform as the template for the consequent ECG identification process. With the proposed method, the accuracy of ECG biometrics using the dynamic time wraping for difference measures has been significantly improved.

A compensation method to improve the performance of IPI-based entity recognition system in body sensor networks.

Security of wireless body sensor networks (BSNs) with telemedicine applications remains a crucial issue. A family of novel biometrics schemes has been recently proposed for node recognition and cryptographic key distribution without any pre-deployment in BSNs, where dynamic entity identifiers (EIs) generated from physiological signals captured by individual sensor nodes are used for nodes to recognize each other. As the recognition performance of EIs determines the maximal performance that can be achieved in such biometric systems, several kinds of EI generation schemes have been proposed.

The PLR-DTW method for ECG based biometric identification.

There has been a surge of research on electrocardiogram (ECG) signal based biometric for person identification. Though most of the existing studies claimed that ECG signal is unique to an individual and can be a viable biometric, one of the main difficulties for real-world applications of ECG biometric is the accuracy performance. To address this problem, this study proposes a PLR-DTW method for ECG biometric, where the Piecewise Linear Representation (PLR) is used to keep important information of an ECG signal segment while reduce the data dimension at the same time if necessary, and the Dynamic Time Warping (DTW) is used for similarity measures between two signal segments.

Improvement of fuzzy vault scheme for securing key distribution in body sensor network.

The security of Body Sensor Network (BSN) has become a vital concern, as the massive development of BSN applications in healthcare. A family of biometrics based security methods has been proposed in the last several years, where the bio-information derived from physiological signals is used as entity identifiers (EIs) for multiple security purposes, including node recognition and keying material protection. Among them, a method named as Physiological Signal based Key Agreement (PSKA) was proposed to use frequency-domain information of physiological signals together with Fuzzy Vault scheme to secure key distribution in BSN.

An improved scheme of IPI-based entity identifier generation for securing body sensor networks.

Securing body sensor network (BSN) in an efficient manner is very important for preserving the privacy of medical data. Protecting data confidentiality, integrity and to authenticate the communicating nodes are basic requirements to secure BSN. The existing method to generate entity identifier (EI) from inter-pulse intervals (IPIs) of heartbeats has its advantages in authenticating and identifying nodes, which however was found in this study that such generated EIs are not so resistant to attacks because of potential error patterns.

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.

Physiological signal based entity authentication for body area sensor networks and mobile healthcare systems.

With the evolution of m-Health, an increasing number of biomedical sensors will be worn on or implanted in an individual in the future for the monitoring, diagnosis, and treatment of diseases. For the optimization of resources, it is therefore necessary to investigate how to interconnect these sensors in a wireless body area network, wherein security of private data transmission is always a major concern. This paper proposes a novel solution to tackle the problem of entity authentication in body area sensor network (BASN) for m-Health.