Open Access
Multicore fiber sensors for strain measurement towards traffic monitoring
(SPIE, 2023) Sánchez González, Arturo; Pradas Martínez, Javier; Corera Orzanco, Íñigo; Bravo Acha, Mikel; Leandro González, Daniel; Dauliat, Romain; Jamier, Raphael; Roy, Philippe; Pérez Herrera, Rosa Ana; López-Amo Sáinz, Manuel; Ingeniería Eléctrica, Electrónica y de Comunicación; Institute of Smart Cities - ISC; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, PJUPNA06-2022
In this work, two new interferometric sensors based on multicore optical fibers for the measurement of strain with the ultimate goal of traffic monitoring are presented. The operating principle of each sensor relied on the monitoring of the phase shift difference accumulated between the supermodes of the structure of the multicore segment in a full round trip. The strain characterization for both sensors resulted in a linear response, with sensitivities of -4.073·10-3 rad/με and - 4.389·10-3 rad/με for the aligned and V-shaped cases respectively, and one-hour instabilities below 4.6·10-3 rad with a 95% confidence level. These results suggest its feasibility in applications requiring high sensitivities over very wide strain ranges, such as heavy-vehicle traffic monitoring. To corroborate the hypothesis, both sensors were integrated into the pavement and their response to the traffic was analyzed.
Open Access
Estimation of the motor unit structure from scanning electromyography and surface electromyography recordings
(2021) Corera Orzanco, Íñigo; Navallas Irujo, Javier; Rodríguez Falces, Javier; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
In this thesis, several algorithmic procedures to estimate the MU fiber parameters from the waveform of a scanning-EMG signal (i.e., from the recording of a MUP in multiple positions along a linear corridor) have been developed. The different procedures correspond to different modifications of the scanning-EMG technique, which differ in the number of scanning needles and recording ports used to record the signal. The three proposed variants are: the 1-port recording, based on a single scanning needle with a single recording port placed at one side of the needle (i.e., a single fiber EMG needle); the 2-port recording, based on a single scanning needle, with two recording ports, placed at opposite sides of the needle; and the 4-port recording, based on two scanning needles, each one with two recording ports. The estimation system also uses a linear array of surface-EMG recordings to obtain complementary information about the MU. In this way, the recording setup to achieve the MU parameter estimation consists on a simultaneous surface- and scanning-EMG signal recording of the MUP. The estimation system has been evaluated for the three scanning-EMG recording configurations (1-port, 2-port, and 4-port), and compared to the case in which the estimation is performed from a MUP recorded at a single position. The evaluation has been done in a simulation framework, using state of the art models of the muscle, MUs, recruitment, and needles, and developing specific models for the simultaneous surface- and scanning-EMG recording process. This provides a controlled environment in which the performance of the system can be objectively quantified and evaluated.The results evidence that MU parameters are estimated much more accurately when using the scanning-EMG technique than when using a MUP recorded at a single position, corroborating the hypothesis that the use of signals recorded at multiple positions enhances the parameter estimation. Among the three proposed recording configurations, the poorest estimation results have been obtained for the 1-port configuration which, moreover, is only capable of estimating the MU fibers at one side of the needle. The 2-port configuration gives better results, and allows to estimate the MU fibers at both sides of the needle. The 4-port configuration is the one that provides the best performance, but it has the disadvantage of being the most difficult configuration to be physically implemented. In the view of these results, a deeper evaluation of the 2-port recording configuration is done. This is because it combines a good estimation performance with a relative ease to be physically implemented. An additional effort is done to calculate several global MU parameters, such as the MU fiber density, the average potential propagation velocity of the fibers, and the width of the innervation zone, from the resulting set of estimated fibers. These global MU parameters provide relevant physiological information from a clinical point of view. Hence global parameters connect the estimation system developed in this thesis with a future application in the diagnosis and follow-up of neuromuscular pathologies.
Open Access
Procesado de señales de Scanning-EMG
(2015) Corera Orzanco, Íñigo; Navallas Irujo, Javier; Escuela Técnica Superior de Ingenieros Industriales y de Telecomunicación; Telekomunikazio eta Industria Ingeniarien Goi Mailako Eskola Teknikoa
La técnica del scanning-EMG consiste en el registro de la actividad eléctrica de la unidad motora en diferentes posiciones del músculo a lo largo de un corredor lineal utilizando un electrodo de aguja. Las señales de scanning-EMG registradas mediante esta técnica proporcionan información acerca de la estructura anatómica y fisiológica de la unidad motora bajo estudio no disponible en los registros convencionales, lo que tiene importantes implicaciones en el estudio y en el diagnóstico de enfermedades neuromusculares. Sin embargo, durante el proceso de registro, dichas señales son contaminadas con ruido e interferencias de distinta naturaleza, por lo que antes de extraer cualquier tipo de información es necesario realizar un procesado de la señal de scanning que mitigue sus efectos. Actualmente ya existe un algoritmo de procesado de señales de scanning-EMG; sin embargo, este algoritmo presenta varias limitaciones que condicionan la calidad de la señal de scanning procesada mediante esta técnica. Estas limitaciones son, en primer lugar un efecto de recorte de picos de la señal de scanning debida a la utilización en el procesado de un filtro de mediana, y en segundo lugar, la no corrección del desalineamiento presente en la señal de scanning. En este proyecto se ha implementado un nuevo algoritmo de procesado de señales de scanning-EMG con el objetivo de superar las limitaciones expuestas. El algoritmo se ha estructurado en cuatro bloques de procesado que atacan, sucesivamente, el ruido de línea de base, el desalineamiento, los artefactos y el ruido de instrumentación. Para evaluar el funcionamiento del nuevo algoritmo de procesado y comparar el rendimiento del mismo con el del algoritmo de procesado clásico, se han diseñado una serie de experimentos en los que se han procesado diferentes señales de scanning-EMG mediante los dos algoritmos. Con objeto de realizar la evaluación en un entorno controlado, las señales de scanning utilizadas en los experimentos han sido generadas a partir de un modelo de simulación de señales de scanning-EMG también desarrollado durante este proyecto. Los resultados revelan que el nuevo algoritmo de procesado reduce notablemente el desalineamiento y presenta valores de SNR superiores a los obtenidos mediante el algoritmo de procesado clásico (SNR mediana 23.69 dB frente a 19.07 dB del clásico en condiciones de contaminación estándar). Además los errores de amplitud pico-pico dados para el nuevo algoritmo de procesado son menores, lo que significa que el nuevo algoritmo recorta menos los picos de la señal de scanning que el algoritmo de procesado clásico (5 % de recorte frente al 15 % del clásico). Estos mejores resultados dados por el nuevo algoritmo de procesado han sido obtenidos para diferentes condiciones de ruido de la señal de scanning, por lo que se puede concluir que el rendimiento del nuevo algoritmo de procesado superior al rendimiento del algoritmo de procesado clásico.
Open Access
Estimación de la estructura de la unidad motora en base a registros de EMG
(2014) Corera Orzanco, Íñigo; Navallas Irujo, Javier; Escuela Técnica Superior de Ingenieros Industriales y de Telecomunicación; Telekomunikazio eta Industria Ingeniarien Goi Mailako Eskola Teknikoa; Ingeniería Eléctrica y Electrónica; Ingeniaritza Elektrikoa eta Elektronikoa
El objetivo de este proyecto fin de carrera es desarrollar y perfeccionar un algoritmo cuya función sea extraer información relevante sobre la unidad motora a partir de señales electromiográficas registradas con un electrodo de aguja concéntrica en la unidad motora bajo estudio. En concreto se desea extraer información acerca de las posiciones y velocidades de propagación de las diferentes fibras que contribuyen al potencial de unidad motora, así como del número de fibras que la constituyen.
Open Access
Long-range and high-resolution traffic monitoring based on pulse-compression DAS and advanced vehicle tracking algorithm
(Optica Publishing Group, 2022) Corera Orzanco, Íñigo; Piñeiro Ben, Enrique; Navallas Irujo, Javier; Sagüés García, Mikel; Loayssa Lara, Alayn; Ingeniería Eléctrica, Electrónica y de Comunicación; Institute of Smart Cities - ISC; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
We demonstrate traffic monitoring over tens of kilometres of road using an enhanced distributed acoustic sensing system based on optical pulse compression and a novel transformed-domain-based processing scheme with enhanced vehicle detection and tracking capabilities.
Open Access
One-year analysis of road condition using FBG arrays
(SPIE, 2023) Corera Orzanco, Íñigo; Pradas Martínez, Javier; Leandro González, Daniel; Bravo Acha, Mikel; López-Amo Sáinz, Manuel; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, PJUPNA06-2022
In this work, it is presented an analysis of FBG arrays installed in a public road. The arrays were installed in a newly paved urban road and were monitored for more than one year. The study evidences the permanent deformation of the wearing course and the degradation of the reflected spectra of the sensors.
Open Access
Masked least-squares averaging in processing of scanning-EMG recordings with multiple-discharges
(Springer, 2020) Corera Orzanco, Íñigo; Malanda Trigueros, Armando; Rodríguez Falces, Javier; Navallas Irujo, Javier; Ingeniería Eléctrica y Electrónica; Ingeniaritza Elektrikoa eta Elektronikoa
Removing artifacts from nearby motor units is one of the main objectives when processing scanning-EMG recordings. Methods such as median filtering or masked least-squares smoothing (MLSS) can be used to eliminate artifacts in recordings with just one discharge of the motor unit potential (MUP) at each location. However, more effective artifact removal can be achieved if several discharges per position are recorded. In this case, processing usually involves averaging the discharges available at each position and then applying a median filter in the spatial dimension. The main drawback of this approach is that the median filter tends to distort the signal waveform. In this paper, we present a new algorithm that operates on multiple discharges simultaneously and in the spatial dimension. We refer to this algorithm as the multi masked least-squares smoothing (MMLSS) algorithm: an extension of the MLSS algorithm for the case of multiple discharges. The algorithm is tested using simulated scanning-EMG signals in different recording conditions, i.e., at different levels of muscle contraction and for different numbers of discharges per position. Results demonstrate that the algorithm eliminates artifacts more effectively than any previously available method and does so without distorting the waveform of the signal.
Open Access
A masked least-squares smoothing procedure for artifact reduction in scanning-EMG recordings
(Springer, 2018) Corera Orzanco, Íñigo; Eciolaza Ferrando, Adrián; Rubio Zamora, Oliver; Malanda Trigueros, Armando; Rodríguez Falces, Javier; Navallas Irujo, Javier; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
Scanning-EMG is an electrophysiological technique in which the electrical activity of the motor unit is recorded at multiple points along a corridor crossing the motor unit territory. Correct analysis of the scanning-EMG signal requires prior elimination of interference from nearby motor units. Although the traditional processing based on the median filtering is effective in removing such interference, it distorts the physiological waveform of the scanning-EMG signal. In this study, we describe a new scanning-EMG signal processing algorithm that preserves the physiological signal waveform while effectively removing interference from other motor units. To obtain a cleaned-up version of the scanning signal, the masked least-squares smoothing (MLSS) algorithm recalculates and replaces each sample value of the signal using a least-squares smoothing in the spatial dimension, taking into account the information of only those samples that are not contaminated with activity of other motor units. The performance of the new algorithm with simulated scanning-EMG signals is studied and compared with the performance of the median algorithm and tested with real scanning signals. Results show that the MLSS algorithm distorts the waveform of the scanning-EMG signal much less than the median algorithm (approximately 3.5 dB gain), being at the same time very effective at removing interference components.
Open Access
Motor unit profile: a new way to describe the scanning-EMG potential
(Elsevier, 2017) Corera Orzanco, Íñigo; Malanda Trigueros, Armando; Rodríguez Falces, Javier; Porta Cuéllar, Sonia; Navallas Irujo, Javier; Ingeniería Eléctrica y Electrónica; Ingeniaritza Elektrikoa eta Elektronikoa
The motor unit profile, a representation of the trajectories of positive and negative turns of a scanning-EMG signal, is a new way to characterize the motor unit potential. Such characterization allows quantification of the scanning-EMG signal's complexity, which is closely related to the anatomy and physiology of the motor unit. To extract the motor unit profile, an algorithm that detects the turns of the scanning-EMG signal and links them using point-tracking techniques has been developed. The performance of this algorithm is sensitive to three parameters: the turn detection threshold, the maximum tracking interval threshold, and the trajectory purge threshold. Real scanning-EMG signals have been used to analyze the algorithm's behavior and the influence of the algorithm's parameters and to determine which parameter values provide the best performance.
Open Access
Long-range traffic monitoring based on pulse-compression distributed acoustic sensing and advanced vehicle tracking and classification algorithm
(MDPI, 2023) Corera Orzanco, Íñigo; Piñeiro Ben, Enrique; Navallas Irujo, Javier; Sagüés García, Mikel; Loayssa Lara, Alayn; Ingeniería Eléctrica, Electrónica y de Comunicación; Institute of Smart Cities - ISC; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
We introduce a novel long-range traffic monitoring system for vehicle detection, tracking, and classification based on fiber-optic distributed acoustic sensing (DAS). High resolution and long range are provided by the use of an optimized setup incorporating pulse compression, which, to our knowledge, is the first time that is applied to a traffic-monitoring DAS system. The raw data acquired with this sensor feeds an automatic vehicle detection and tracking algorithm based on a novel transformed domain that can be regarded as an evolution of the Hough Transform operating with non-binary valued signals. The detection of vehicles is performed by calculating the local maxima in the transformed domain for a given time-distance processing block of the detected signal. Then, an automatic tracking algorithm, which relies on a moving window paradigm, identifies the trajectory of the vehicle. Hence, the output of the tracking stage is a set of trajectories, each of which can be regarded as a vehicle passing event from which a vehicle signature can be extracted. This signature is unique for each vehicle, allowing us to implement a machine-learning algorithm for vehicle classification purposes. The system has been experimentally tested by performing measurements using dark fiber in a telecommunication fiber cable running in a buried conduit along 40 km of a road open to traffic. Excellent results were obtained, with a general classification rate of 97.7% for detecting vehicle passing events and 99.6% and 85.7% for specific car and truck passing events, respectively.