In this work, we describe a complete step-by-step workflow to apply machine-learning (ML) classification for chipless radio-frequency identification (RFID) tag identification, covering: 1) the tag implementation criteria for circular ring resonator (CRR) and square ring resonator (SRR) arrays for ML interoperability; 2) the data collection procedure to get a sufficiently representative dataset of real measurements; 3) the ML techniques to visualize the data and reduce its dimensionality; 4) the evaluation of the ML classifier to ensure high-accuracy predictions on new measurements; and 5) a thresholding scheme to increase the certainty of the predictions. The differences in the tags' frequency responses are maximized by optimizing the Hamming distance between the tag identifiers (IDs) and by controlling each resonator array's radar cross section (RCS) level. We show that the proposed workflow achieves perfect accuracy for the identification of four tags at a fixed distance of 160 cm. We also evaluate the performance of the proposed workflow to identify up to 16 tags within a flexible range (up to 140 cm), showcasing the tradeoff between the number of tags that can be correctly classified based on the reading range.

A scalable system for wireless wear monitoring in a distributed harsh industrial environment, in which installation of the electronic equipment near the distributed wearing measuring locations is unfeasible, has been successfully designed, developed and deployed. The system is composed of a novel LC-type passive wireless wear sensor system, a sensor readout coil, an electronic equipment, a readout unit for a scalable multisensor system and a control and monitoring system. The capacitor structure, employed as a sensing device in the LC sensor manufactured on low cost PCB, provides a linear response to the wear level. This allowed us to define a measuring and calibration method to suppress the cable effects from each distributed readout coil to the electronic equipment readout unit. It was possible to achieve a multiple simultaneous measurements in different sensors distributed along an industrial process without significant cabling influence. Concerning the sensor data analysis, a double Holt-Winters method with trend was used to reduce noise effects and an "experience based learning" tool was implemented in order to solve any misbehavior or time lag. Laboratory measurements and on-site operation results in an article mill indicate 1 mm precision and about 0, 5 MHz/mm sensitivity of the LC-type passive wireless wear sensor system.

The objective of this research is to select a reduced group of surface electromyographic (sEMG) channels and signal-features that is able to provide an accurate classification rate in a myoelectric control system for any user. To that end, the location of 32 sEMG electrodes placed around-along the forearm and 86 signal-features are evaluated simultaneously in a static-hand gesture classification task (14 different gestures). A novel multivariate variable selection filter method named mRMR-FCO is presented as part of the selection process. This process finds the most informative and least redundant combination of sEMG channels and signal-features among all the possible ones. The performance of the selected set of channels and signal-features is evaluated with a Support Vector Machine classifier. (C) 2014 Elsevier Ltd. All rights reserved.

In this article we present the design and implementation of a mobile cardiac monitoring system oriented to patients in Phase II and III of cardiac rehabilitation. The complete monitoring system involves both hardware and software design perspectives. At the hardware level, we present a T-shirt with a 12-lead ECG system and an embedded inertial sensor for the monitoring of activity and energy expenditure. At the software level, a modular cloud platform performs data processing to detect relevant cardiac events and to provide advanced visualization capabilities. As a case study, we have implemented our system at the Cardiac Rehabilitation program at Donostia University Hospital (Spain). Finally, the validation of the 12-lead ECG recording system is also presented and discussed.

Repetitive and alternating lower limb movements are a specific component of human gait. Due to technical challenges, the neural mechanisms underlying such movements have not been previously studied with functional magnetic resonance imaging. In this study, we present a novel treadmill device employed to investigate the kinematics and the brain activation patterns involved in alternating and repetitive movements of the lower limbs. Once inside the scanner, 19 healthy subjects were guided by two visual cues and instructed to perform a motor task which involved repetitive and alternating movements of both lower limbs while selecting their individual comfortable amplitude on the treadmill. The device facilitated the performance of coordinated stepping while registering the concurrent lower-limb displacements, which allowed us to quantify some movement primary kinematic features such as amplitude and frequency. During stepping, significant blood oxygen level dependent signal increases were observed bilaterally in primary and secondary sensorimotor cortex, the supplementary motor area, premotor cortex, prefrontal cortex, superior and inferior parietal lobules, putamen and cerebellum, regions that are known to be involved in lower limb motor control. Brain activations related to individual adjustments during motor performance were identified in a right lateralized network including striatal, extrastriatal, and fronto-parietal areas.

Portable systems and global communications open a broad spectrum for new health applications. In the framework of electrophysiological applications, several challenges are faced when developing portable systems embedded in Cloud computing services. In order to facilitate new developers in this area based on our experience, five areas of interest are presented in this paper where strategies can be applied for improving the performance of portable systems: transducer and conditioning, processing, wireless communications, battery and power management. Likewise, for Cloud services, scalability, portability, privacy and security guidelines have been highlighted.

The structure and distribution of the sources underlying the generation of evoked potentials (EPs) is often very complex. In an effort to improve localization accuracy of the auditory N100 (negative response occurring around 100 ms poststimulus) component, we analyzed 13 datasets of single-trial EPs obtained from normal subjects using an iterative independent component analysis procedure which allowed us to detect a clear N100 component in each single trial and to study gross changes in component morphology across trials. We found that single-trial N100 amplitude was most often negative in polarity, as expected, but occasionally exhibited a marked reversal to become positive. The average N100, however, showed the typical negative polarity, in all subjects. Based on this observation, we separated the processed single trials in two groups of typical and aberrant responses, and from each group, we computed a partial EP that was used to localize the underlying intracranial sources. Additionally, we localized the classical ensemble average EP. Before processing, the N100 sources were identified correctly in the primary auditory cortex in only four datasets, while after processing, all 13 datasets yielded correct localizations, and the confidence volume of the sources improved by about 80%. Further analysis demonstrated that in nine datasets the improvement was mostly due to the typical responses, while the aberrant responses had an antagonistic effect.

In the quest for neurophysiological biomarkers that uniquely characterize schizophrenia subjects, auditory evoked potentials (EPs) have been extensively used during the past several decades. Typically, EPs are estimated using ensemble averaging to obtain robust components. Averaging, however, eliminates all temporal variability of the recorded signals and, therefore, hampers the study of the brain temporal dynamics underlying the generation of EP components. In this chapter, we present a methodology for analyzing EPs on a single-trial basis using an iterative independent component analysis procedure. The method is capable of identifying and measuring the amplitude, latency, and overall morphology of individual EP components in single trials and, as such, permits the study of phase characteristics among single trials while preserving known features of the average EPs. Recordings from schizophrenia patients and normal controls demonstrate that activity phase synchronization plays a crucial role in EP generation and explains the sensory gating deficits observed in schizophrenia subjects. Furthermore, the findings from this method are very robust across recordings from different labs and experimental protocols and can be used to separate schizophrenia patients from normal controls with 100 % classification accuracy.