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As electronic communications become more prevalent, mobile and universal, the threats of data compromises also accordingly loom larger. In the context of a body sensor network (BSN), which permits pervasive monitoring of potentially sensitive medical data, security and privacy concerns are particularly important. It is a challenge to implement traditional security infrastructures in these types of lightweight networks since they are by design limited in both computational and communication resources. A key enabling technology for secure communications in BSN's has emerged to be biometrics. In this work, we present two complementary approaches which exploit physiological signals to address security issues: (1) a resource-efficient key management system for generating and distributing cryptographic keys to constituent sensors in a BSN; (2) a novel data scrambling method, based on interpolation and random sampling, that is envisioned as a potential alternative to conventional symmetric encryption algorithms for certain types of data. The former targets the resource constraints in BSN's, while the latter addresses the fuzzy variability of biometric signals, which has largely precluded the direct application of conventional encryption. Using electrocardiogram (ECG) signals as biometrics, the resulting computer simulations demonstrate the feasibility and efficacy of these methods for delivering secure communications in BSN's.

Wireless Capsule Endoscopy (WCE) is a technology in the field of endoscopic imaging which facilitates direct visualization of the entire small intestine. Many algorithms are being developed to automatically identify clinically important frames in WCE videos. This paper presents a supervised method for automated detection of bleeding regions present in WCE frames or images. The proposed method characterizes the image regions by using statistical features derived from the first order histogram probability of the three planes of RGB color space. Despite being inconsistent and tiresome, manual selection of regions has been a popular technique for creating training data in the studies of capsule endoscopic images. We propose a semi-automatic region-annotation algorithm for creating training data efficiently. All possible combinations of different features are exhaustively analyzed to find the optimum feature set with the best performance. During operation, regions from images are obtained by applying a segmentation method. Finally, a trained neural network recognizes the patterns of the data arising from bleeding and non-bleeding regions.

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The technological advancements in the field of remote sensing have resulted in substantial growth of the telemedicine industry. While health care practitioners may now monitor their patients' well-being from a distance and deliver their services remotely, the lack of physical presence introduces security risks, primarily with regard to the identity of the involved parties. The sensing apparatus, that a patient may employ at home, collects and transmits vital signals to medical centres which respond with treatment decisions despite the lack of solid authentication of the transmitter's identity. In essence, remote monitoring increases the risks of identity fraud in health care. This paper proposes a biometric identification solution suitable for continuous monitoring environments. The system uses the electrocardiogram (ECG) signal in order to extract unique characteristics which allow to discriminate users. In security, ECG falls under the category of medical biometrics, a relatively young but promising field of biometric security solutions. In this work, the authors investigate the idiosyncratic properties of home telemonitoring that may affect the ECG signal and compromise security. The effects of psychological changes on the ECG waveform are taken into consideration for the design of a robust biometric system that can identify users based on cardiac signals despite physical or emotional variations.

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