Open Access
Classification of south american camelid and goat fiber samples based on fourier transform infrared spectroscopy and machine learning
(Taylor and Francis Group, 2024) Quispe Bonilla, Max David; Trigo Vilaseca, Jesús Daniel; Serrano Arriezu, Luis Javier; Huere, Jorge; Quispe Peña, Edgar; Beruete Díaz, Miguel; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa.
Some animal fibers are considerably cheaper than others. Hence the existence of counterfeit products, which are detrimental to legitimate producers and consumers alike. Fourier-Transform Infrared (FTIR) spectroscopy can extract a characteristic waveform from fibers that can be later used for classification. However, visually inspecting such waveforms is imprecise. Previous research has complemented FTIR with other mathematical or physical methods to improve accuracy. In parallel, Artificial Intelligence (AI) is an emerging field that could be helpful in this domain. The objective of this work is therefore to develop and validate two machine learning models, namely, Deep Neural Networks (DNN) and Support Vector Machine (SVM) to classify spectra of fibers by species. The spectra are acquired using an FTIR spectrometer in Attenuated Total Reflectance (ATR) mode (FTIR-ATR). Camelid (alpaca: n = 51, llama: n = 50, vicuña: n = 50) and goat (mohair: n = 35 and cashmere: n = 20) samples were evaluated, from which 1236 FTIR-ATR spectra were obtained. Some visual differences were observed between the spectra of the different species. Accuracies up to 96.75% and 95.12% were obtained when evaluating the DNN and SVM models. Furthermore, an accuracy of 97.8% was obtained when evaluating the FTIR-ATR spectra of South American Camelids (SAC) fibers with DNN, and 97.2% when evaluating them with SVM. A 100% accuracy was obtained when evaluating the FTIR-ATR spectra of vicuña fibers with both models. No significant differences were found (p-value = 0.368) by comparing the number of hits against the total number of alpaca, llama, vicuña, mohair and cashmere fibers using DNN. As per the results, it seems that DNN is more accurate than SVM. In conclusion, FTIR-ATR spectrometry techniques combined with machine learning models are a reliable alternative for the identification of SAC and goats through the spectrum of their fibers.
Open Access
Application of artificial intelligence and digital images analysis to automatically determine the percentage of fiber medullation in alpaca fleece samples
(Elsevier Science, 2022) Quispe Bonilla, Max David; Serrano Arriezu, Luis Javier; Trigo Vilaseca, Jesús Daniel; Quispe Bonilla, Christian; Poma Gutiérrez, Adolfo; Quispe Peña, Edgar; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Institute of Smart Cities - ISC; Ingeniería Eléctrica, Electrónica y de Comunicación; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
The aim of this research is to develop and validate two computer programs based on artificial intelligence (AI) and digital image analysis (DIA) in order to determine the incidence of medullation in white alpaca fibers. Two data sets were analyzed: 76 samples of Huacaya alpaca fibers obtained from Huancavelica, Peru, and 200 samples of white alpacas of two genotypes (Huacaya, n =100; Suri, n = 100), obtained from Arequipa, Peru. The preparation of each sample followed the procedure described in IWTO-8-2011. The Pytorch framework was used to generate several training models based on the You Only Look at Once (YOLO) architecture. Circa 4000 pictures of fibers were taken and 661 of them were selected as representative. Using the LabelImg software, the fibers present in each representative picture (approximately 10 fibers/picture) were labeled as one of these two classes: either medullated or non-medullated. Subsequently, the data augmentation technique was applied to expand the data set to 3966 photographs. Thus, 90 of them were used as initial validation data, while the reaming 3876 pictures (containing a total of 23,964 labeled fibers) were used as training data. Matlab was used to develop the DIA-based software. More specifically, algorithms of pre-processing, segmentation, smoothing, skeletonization and Hough transform were implemented to detect medullated and non-medullated fibers. Correlation and linear regression analyses were used to evaluate the models. The medullation percentage results show that there is no statistically significant difference between the AI-based method and the projection microscope method (p-value = 0.668 and 0.672 for the t-student and Wilcoxon tests, respectively). Moreover, the correlation of each of the developed computer methods with the projection microscope method is very strong (r = 0.99 and 0.97). This confirms the software ability to perform the recognition of fibers with and without medullation. Similar results (p-value = 0.357) were obtained when comparing the projection microscope method and DIA-based software method. Finally, using the proposed framework, the average time required to analyze a sample was 19.44 s. As a result, this software allows the implementation of practical, precise, and efficient methodologies to determine the incidence of medullation of alpaca fibers.
Open Access
Analysis of bluetooth-based wireless sensor networks performance in hospital environments
(MDPI, 2016) López Iturri, Peio; Led Ramos, Santiago; Aguirre Gallego, Erik; Azpilicueta Fernández de las Heras, Leyre; Serrano Arriezu, Luis Javier; Falcone Lanas, Francisco; Ingeniería Eléctrica y Electrónica; Ingeniaritza Elektrikoa eta Elektronikoa
In this work, a method to analyze the performance of Bluetooth-based Wireless Sensor Networks (WSN) deployed within hospital environments is presented. Due to the complexity that this kind of scenarios exhibit in terms of radio propagation and coexistence with other wireless communication systems and other potential interference sources, the deployment of WSNs becomes a complex task which requires an in-depth radio planning analysis. For that purpose, simulation results obtained with the aid of an in-house developed 3D Ray Launching code are presented. The scenarios under analysis are located at the Hospital of Navarre Complex (HNC), in the city of Pamplona. As hospitals have a wide variety of scenarios, the analysis has been carried out in different zones such as Boxes, where different medical sensors based on Bluetooth communication protocol have been deployed. The simulation results obtained have been validated with measurements within the scenario under analysis, exhibiting Bluetooth-based WSNs performance within hospital environments in terms of coverage/capacity relations. The proposed methodology can aid in obtaining optimal network configuration and hence performance of Bluetooth-based WSNs within medical/health service provision environments.
Open Access
Analysis and description of HOLTIN service provision for AECG monitoring in complex indoor environments
(MDPI, 2013) Led Ramos, Santiago; Azpilicueta Fernández de las Heras, Leyre; Aguirre Gallego, Erik; Martínez de Espronceda Cámara, Miguel; Serrano Arriezu, Luis Javier; Falcone Lanas, Francisco; Ingeniería Eléctrica y Electrónica; Ingeniaritza Elektrikoa eta Elektronikoa
In this work, a novel ambulatory ECG monitoring device developed in-house called HOLTIN is analyzed when operating in complex indoor scenarios. The HOLTIN system is described, from the technological platform level to its functional model. In addition, by using in-house 3D ray launching simulation code, the wireless channel behavior, which enables ubiquitous operation, is performed. The effect of human body presence is taken into account by a novel simplified model embedded within the 3D Ray Launching code. Simulation as well as measurement results are presented, showing good agreement. These results may aid in the adequate deployment of this novel device to automate conventional medical processes, increasing the coverage radius and optimizing energy consumption.
Open Access
Multimodal minimally invasive wearable technology for epilepsy monitoring: a feasibility study of the periauricular area
(IEEE, 2023) Besné, Guillermo M.; López Iturri, Peio; Alegre, Manuel; Artieda, Julio; Trigo Vilaseca, Jesús Daniel; Serrano Arriezu, Luis Javier; Falcone Lanas, Francisco; Valencia Ustárroz, Miguel; Institute of Smart Cities - ISC
Ambulatory monitoring is of great interest in both clinical and domestic environments. Despite the technological advances, few monitoring solutions are suitable for medical application and diagnosis. Here, we investigate the feasibility of targeting the periauricular area (ear pavilion, ear canal, and the surrounding skin areas) to implement a multimodal system that fulfills the requirements of ergonomics and minimal obstructiveness in the context of epilepsy monitoring. Six physiological signals are selected and explored for their integration in the area of interest and a ¿proof-of-concept¿ prototype integrating the components in a single portable device targeting the selected location is implemented. Results show mixed results where some parameters are highly reliable, and others are impractical or require customized technology to provide clinically relevant information. To enable data acquisition, storage, and processing within the Internet of Medical Things paradigms, wireless body area transceiver integration is also analyzed in terms of coverage/capacity relations, showing feasibility for such device configuration.
Open Access
Patient tracking in a multi-building, tunnel-connected hospital complex
(IEEE, 2020) Trigo Vilaseca, Jesús Daniel; Klaina, Hicham; Picallo Guembe, Imanol; López Iturri, Peio; Astrain Escola, José Javier; Falcone Lanas, Francisco; Serrano Arriezu, Luis Javier; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, PJUPNA29
Patients admitted to Intensive Care Units (ICU) are transported from and to other units. Knowing their location is strategic for a sound planning of intra-hospital transports as well as resources management. This is even more crucial in big hospital complexes, comprised of several buildings often connected through tunnels. In this work, a patient tracking application in a multi-building, tunnel-connected hospital complex (the Hospital Complex of Navarre) is presented. The system leverages Internet of Medical Things (IoMT) communication technologies, such as Long Range Wide-Area Network (LoRaWAN) and Near Field Communication (NFC). The locations of the LoRaWAN nodes were selected based on several factors, including the situation of the tunnels, buildings services and medical equipment and a literature review on intra-hospital ICU patients' trips. The possible locations of the LoRaWAN gateways were selected based on 3D Ray Launching Simulations, in order to obtain accurate characterization. Once the locations were set, a LoRaWAN radio coverage studio was performed. The main conclusion drawn is that just one LoRaWAN gateway would be enough to cover all overground LoRaWAN nodes deployed. A second one would be required for underground coverage. In addition, a remote, private cloud infrastructure together with a mobile application was created to manage the information generated. On-field tests were performed to assess the technical feasibility of the system. The application provides with on-demand ICU patients' movement flow around the complex. Although designed for the ICU-admitted patients' context, the system could be easily extrapolated to other use cases.
Open Access
Implementation and operational analysis of an interactive intensive care unit within a smart health context
(MDPI, 2018) López Iturri, Peio; Aguirre Gallego, Erik; Trigo Vilaseca, Jesús Daniel; Astrain Escola, José Javier; Azpilicueta Fernández de las Heras, Leyre; Serrano Arriezu, Luis Javier; Villadangos Alonso, Jesús; Falcone Lanas, Francisco; Ingeniaritza Elektrikoa eta Elektronikoa; Matematika eta Informatika Ingeniaritza; Institute of Smart Cities - ISC; Ingeniería Eléctrica y Electrónica; Ingeniería Matemática e Informática
In the context of hospital management and operation, Intensive Care Units (ICU) are one of the most challenging in terms of time responsiveness and criticality, in which adequate resource management and signal processing play a key role in overall system performance. In this work, a context aware Intensive Care Unit is implemented and analyzed to provide scalable signal acquisition capabilities, as well as to provide tracking and access control. Wireless channel analysis is performed by means of hybrid optimized 3D Ray Launching deterministic simulation to assess potential interference impact as well as to provide required coverage/capacity thresholds for employed transceivers. Wireless system operation within the ICU scenario, considering conventional transceiver operation, is feasible in terms of quality of service for the complete scenario. Extensive measurements of overall interference levels have also been carried out, enabling subsequent adequate coverage/capacity estimations, for a set of Zigbee based nodes. Real system operation has been tested, with ad-hoc designed Zigbee wireless motes, employing lightweight communication protocols to minimize energy and bandwidth usage. An ICU information gathering application and software architecture for Visitor Access Control has been implemented, providing monitoring of the Boxes external doors and the identification of visitors via a RFID system. The results enable a solution to provide ICU access control and tracking capabilities previously not exploited, providing a step forward in the implementation of a Smart Health framework.
Open Access
Implementation of context aware e-health environments based on social sensor networks
(MDPI, 2016) Aguirre Gallego, Erik; Led Ramos, Santiago; López Iturri, Peio; Azpilicueta Fernández de las Heras, Leyre; Serrano Arriezu, Luis Javier; Falcone Lanas, Francisco; Ingeniería Eléctrica y Electrónica; Ingeniaritza Elektrikoa eta Elektronikoa; Gobierno de Navarra / Nafarroako Gobernua
In this work, context aware scenarios applied to e-Health and m-Health in the framework of typical households (urban and rural) by means of deploying Social Sensors will be described. Interaction with end-users and social/medical staff is achieved using a multi-signal input/output device, capable of sensing and transmitting environmental, biomedical or activity signals and information with the aid of a combined Bluetooth and Mobile system platform. The devices, which play the role of Social Sensors, are implemented and tested in order to guarantee adequate service levels in terms of multiple signal processing tasks as well as robustness in relation with the use wireless transceivers and channel variability. Initial tests within a Living Lab environment have been performed in order to validate overall system operation. The results obtained show good acceptance of the proposed system both by end users as well as by medical and social staff, increasing interaction, reducing overall response time and social inclusion levels, with a compact and moderate cost solution that can readily be largely deployed.
Open Access
Development and validation of a smart system for medullation and diameter assessment of alpaca, llama and mohair fibres
(Elsevier, 2023) Quispe Bonilla, Max David; Quispe Bonilla, Christian; Serrano Arriezu, Luis Javier; Trigo Vilaseca, Jesús Daniel; Bengoechea, J. J.; Quispe Peña, Edgar; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Medullated fibres, due to their higher resistance to bending and pressure, constitute a problem for the textile industry. Thus, having practical instruments to identify them is essential. Therefore, the aim of this research was to develop and validate a novel, swift, automatic system (referred to as S-Fiber Med) for medullation and diameter assessment of animal fibres based on artificial intelligence. The medullation of 88 samples of alpaca, llama and mohair fibres (41, 43 and 4, respectively) was evaluated. Additionally, 269 samples of alpacas were considered for average fibre diameter (AFD) and the results were compared with the Portable Fiber Tester (PFT) and Optical Fibre Diameter Analyser (OFDA) methods (72 and 197 samples, respectively). The preparation of each sample to be analysed followed the procedure described in IWTO-8-2011. Version 5 of “You Only Look Once” and DenseNet models were used to recognise the type of medullation and diameter of the fibres, respectively. Within each image (n = 661 for alpaca), all fibres were labelled (as Non-Medullated, Fragmented Medulla, Uncontinuous Medulla, Continuous Medulla and Strongly Medullated) using the LabelImg tool. Data augmentation technique was applied to obtain 3 966 images. Such data set was divided into 3 576 and 390 images for training and test data, respectively. For mohair samples (n = 321), a similar process was carried out. The data to train the model used to infer the diameter contained 16 446 fibres labelled with his respective AFD. A complementary hardware composed of three subsystems (mechanical, electronic, and optical) was developed for evaluation purposes. T-test, Pearson and Concordance correlation, Bland-Altman plot and linear regression analyses were used to validate and compare the S-Fiber Med with other methods. Results indicate that there was no significant difference between medullation percentage obtained with the projection microscope and the S-Fiber Med. The Pearson and Concordance correlation analysis shows a strong, high and significant relationship (P-value < 0.001). The AFDs of alpaca and llama fibre samples obtained with the two methods are very similar, because no significant difference was found at the t-test (P-value > 0.172), and they have a strong, high and significant relationship between them, given the high Pearson correlation value (r ≥ 0.96 with P-value < 0.001), high Concordance coefficient and bias correction factor. Similar results were found when PFT and OFDA100 were compared with S-Fiber Med. As a conclusion, this new system provides precise, accurate measurements of medullation and AFD in an expeditious fashion (40 seconds/sample).
Open Access
Development and validation of an automatic and intelligent system for medullation and average diameter evaluation to alpaca, llama and mohair fibers
(2021) Quispe Bonilla, Max David; Quispe Peña, Edgar; Serrano Arriezu, Luis Javier; Trigo Vilaseca, Jesús Daniel; Quispe Bonilla, Christian; Institute of Smart Cities - ISC
The aim of this work was to develop and validate an automatic and intelligent system (based in artificial intelligence) capable of quantify and identify fibers, by type of medullation in 5 categories. This work was carried out in Lima, Peru. To develop the software a trained model was generated based on You Only Look at Once for medullation and DenseNet for average fiber diameter (AFD), using Python. Language C was used to develop the graphical user interface. For the hardware; mechanical, electronic and optical subsystems was design and development. Samples of white alpaca, llama and mohair fibers (2108, 1858, 901 fibers, respectively) were evaluated for identification the fibers medullation. Additionally, AFD of 197 samples of white alpacas were measured with two methods. This system identifies 5 types of medullation and measures the diameter of the fibers. Each sample is evaluated in 40 sec, considering about 1500 fibers/sample. At two-proportion z-test of different fiber medullation types obtained with direct counting and our system no significant differences were found. At t-test of AFD obtained with OFDA device and our system no significant difference were found. The relationship between these methods was very stronger (r=0.95). The use of this system is recommended for fiber evaluation for the purpose of genetic improvement of fibers in animal production; purchase-sale, and processing of fiber to verify the quality of fibers; and research on medullation to increase knowledge about alpaca, llama and mohair fibers.