Feasibility Analysis on the Use of Ultrasonic Communications for Body Sensor Networks.

Ultrasonic waves have good propagation in the human body and have been widely applied in biomedical device design without any reported side effects. Therefore, ultrasonic waves can provide an alternative method as an information carrier for body sensor networks (BSNs). This paper presents a novel wireless communication method that uses ultrasonic sound waves as a medium for healthcare systems. We investigated the feasibility of our proposal by testing it in a real digital communication experimental setup. To find an acceptable modulation method, the functionality of the proposed ultrasound-based digital communication approach was tested involving three principal modulation methods: amplitude shift keying (ASK), frequency shift keying (FSK), and phase shift keying (PSK). The modulated digital signals obtained from the experiments were compared with the simulated signals. Analysis of the results shows that ultrasonic waves are feasible and can be used for digital communication.

From Pacemaker to Wearable: Techniques for ECG Detection Systems.

With the alarming rise in the deaths due to cardiovascular diseases (CVD), present medical research scenario places notable importance on techniques and methods to detect CVDs. As adduced by world health organization, technological proceeds in the field of cardiac function assessment have become the nucleus and heart of all leading research studies in CVDs in which electrocardiogram (ECG) analysis is the most functional and convenient tool used to test the range of heart-related irregularities. Most of the approaches present in the literature of ECG signal analysis consider noise removal, rhythm-based analysis, and heartbeat detection to improve the performance of a cardiac pacemaker. Advancements achieved in the field of ECG segments detection and beat classification have a limited evaluation and still require clinical approvals. In this paper, approaches on techniques to implement on-chip ECG detector for a cardiac pacemaker system are discussed. Moreover, different challenges regarding the ECG signal morphology analysis deriving from medical literature is extensively reviewed. It is found that robustness to noise, wavelet parameter choice, numerical efficiency, and detection performance are essential performance indicators required by a state-of-the-art ECG detector. Furthermore, many algorithms described in the existing literature are not verified using ECG data from the standard databases. Some ECG detection algorithms show very high detection performance with the total number of detected QRS complexes. However, the high detection performance of the algorithm is verified using only a few datasets. Finally, gaps in current advancements and testing are identified, and the primary challenge remains to be implementing bullseye test for morphology analysis evaluation.

An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks.

Body Sensor Network (BSN) is a network of several associated sensor nodes on, inside or around the human body to monitor vital signals, such as, Electroencephalogram (EEG), Photoplethysmography (PPG), Electrocardiogram (ECG), etc. Each sensor node in BSN delivers major information; therefore, it is very significant to provide data confidentiality and security. All existing approaches to secure BSN are based on complex cryptographic key generation procedures, which not only demands high resource utilization and computation time, but also consumes large amount of energy, power and memory during data transmission. However, it is indispensable to put forward energy efficient and computationally less complex authentication technique for BSN. In this paper, a novel biometric-based algorithm is proposed, which utilizes Heart Rate Variability (HRV) for simple key generation process to secure BSN. Our proposed algorithm is compared with three data authentication techniques, namely Physiological Signal based Key Agreement (PSKA), Data Encryption Standard (DES) and Rivest Shamir Adleman (RSA). Simulation is performed in Matlab and results suggest that proposed algorithm is quite efficient in terms of transmission time utilization, average remaining energy and total power consumption.

A Lightweight Encryption Scheme Combined with Trust Management for Privacy-Preserving in Body Sensor Networks.

With the pervasiveness of smart phones and the advance of wireless body sensor network (BSN), mobile Healthcare (m-Healthcare), which extends the operation of Healthcare provider into a pervasive environment for better health monitoring, has attracted considerable interest recently. However, the flourish of m-Healthcare still faces many challenges including information security and privacy preservation. In this paper, we propose a secure and privacy-preserving framework combining with multilevel trust management. In our scheme, smart phone resources including computing power and energy can be opportunistically gathered to process the computing-intensive PHI (personal health information) during m-Healthcare emergency with minimal privacy disclosure. In specific, to leverage the PHI privacy disclosure and the high reliability of PHI process and transmission in m-Healthcare emergency, we introduce an efficient lightweight encryption for those users whose trust level is low, which is based on mix cipher algorithms and pair of plain text and cipher texts, and allow a medical user to decide who can participate in the opportunistic computing to assist in processing his overwhelming PHI data. Detailed security analysis and simulations show that the proposed framework can efficiently achieve user-centric privacy protection in m-Healthcare system.