Abstract
In this paper a new wearable locomotion data capture system for gait analysis is presented. The system under development intends to help clinicians to detect and identify mobility impairments as well as to evaluate the effectiveness of surgical or rehabilitation intervention. The proposed system allows the measurement of kinematic and biomechanical parameters in a practical and comfortable weft knitted legging, in which the sensors are incorporated.

Abstract
The design, manufacture and operational characteristics (e.g., yield, performance, and reliability) of modern electronic integrated systems exhibit extreme levels of complexity that cannot be easily modelled or predicted. Different mathematical methodologies have been explored to address this issue. Monte Carlo simulation is the most widely employed and straightforward approach to evaluate the circuits' performance statistics. However, the high number of trial cases and the long simulations times required to obtain results for complex circuits with a ppm resolution, lead to very long analysis times. The present work addresses the evaluation of alternative statistical inference methodologies which allow obtaining similar results departing from a smaller dimension data set of Monte Carlo simulations from which the overall population is estimated. These methodologies include the use of Bayesian inference, Expectation-inimization, and Kolmogorov-Smirnov tests. Results are presented which show the validity of these approaches.

Abstract
Wearable systems are expected to contribute for improving traditional biopotential signals monitoring devices due to higher freedom and unobtrusiveness provided to the wearer. Textile electrodes present advantages compared with the conventional Ag/AgCl electrodes for the capturing of biopotentials, namely in terms of skin irritation due to the hydrogel and the need of a technician to place the electrodes on the correct positions. Due to the lack of hydrogel, textile electrodes present different electrical contact characteristics. The skin-electrode impedance is an important feature since it affects the captured signal quality. Although a low impedance is desired, a comfortable wearable system should not require the electrodes to be covered by the hydrogel or be moistened. A forearm sleeve provided with textile electrodes was used to study the electrode-skin impedance and the signal-to-noise ratio (SNR) of surface electromyographic (EMG) signals on a long-term use basis. The sleeve can be adjusted for different levels of tightening to control the pressure applied on the electrodes. The obtained results provide valuable information on the pressure that the textile garments of a sleeve or vest should apply on the recording electrodes, in order to assure a good electrical and mechanical contact between the electrodes and the skin and decrease the noise due to motion. It was observed that the electrode-skin impedance measurement alone is not sufficient to establish a relation with the SNR. The extraction of parameters from an electrical equivalent model of the electrode-skin interface allows to determine a relation with the model parameters and the SNR. The evaluation of these parameters during long-term monitoring will allow assessing the quality of biopotential measurements in textile electrodes. © 2014 IEEE.

Abstract
One of the currently available treatments for aortic aneurysms is endovascular aneurysm repair (EVAR). In spite of major advances in the operating techniques, complications still occur and lifelong surveillance is recommended. In order to reduce and even eliminate the commonly used surveillance imaging exams, as well as to reduce follow-up costs, new technological solutions are being pursued. In this paper, we describe the development, including design and performance characterization, of a flexible remote pressure measurement system based on inductive-coupling for post-EVAR monitoring purposes. The telemetry system architecture and operation are described and main performance characteristics discussed. The implantable sensor details are provided and its model is presented. Simulations with the reading circuit and the sensor's model were performed and compared with measurements carried out with air and a phantom as media, in order to characterize the telemetry system and validate the models. The transfer characteristic curve (pressure versus frequency) of the monitoring system was obtained with measurements performed with the sensor inside a controlled pressure vacuum chamber. Additional experimental results which proof the system functionality were obtained within a hydraulic test bench that emulates the aorta. Several innovative aspects, when compared to the state of the art, both in the sensor and in the telemetry system were achieved.

Abstract
A new methodology for fault detection on wearable medical devices is proposed. The main strategy relies on correctly classifying the captured physiological signals, in order to distinguish whether the actual cause is a wearer health abnormality or a system functional flaw. Data fusion techniques, namely fuzzy logic, are employed to process the captured data, like the electrocardiogram and blood pressure, to increase the trust levels with which diagnostics are made. Concerning the wearer condition, additional information is provided after classifying the set of signals into normal or abnormal (e.g. arrhythmia, chest angina, and stroke). As for the monitoring system, once an abnormal situation is detected in its operation or in the sensors, a set of tests is run to check if actually the wearer shows a degradation of his health condition or if the system is reporting erroneous values.

Abstract
A new methodology for fault detection on wearable medical devices is proposed. The basic strategy relies on correctly classifying the captured physiological signals, in order to identify whether the actual cause is a wearer health abnormality or a system functional flaw. Data fusion techniques, namely fuzzy logic, are employed to process the physiological signals, like the electrocardiogram (ECG) and blood pressure (BP), to increase the trust levels of the captured data after rejecting or correcting distorted vital signals from each sensor, and to provide additional information on the patient's condition by classifying the set of signals into normal or abnormal condition (e.g. arrhythmia, chest angina, and stroke). Once an abnormal situation is detected in one or several sensors the monitoring system runs a set of tests in a fast and energy efficient way to check if the wearer shows a degradation of his health condition or the system is reporting erroneous values.