Open Access
The effect of a multicomponent intervention on steatosis is partially mediated by the reduction of intermuscular abdominal adipose tissue in children with overweight or obesity: the EFIGRO Project
(American Diabetes Association, 2022) Cadenas-Sánchez, Cristina; Idoate, Fernando; Cabeza Laguna, Rafael; Villanueva Larre, Arantxa; Rodríguez Vigil, Beatriz; Medrano Echeverría, María; Osés Recalde, Maddi; Ortega, Francisco B.; Ruiz, Jonatan R.; Labayen Goñi, Idoia; Osasun Zientziak; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Institute of Smart Cities - ISC; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Ciencias de la Salud; Ingeniería Eléctrica, Electrónica y de Comunicación; Gobierno de Navarra / Nafarroako Gobernua
OBJECTIVE: In adults, there is evidence that improvement of metabolic-associated fatty liver disease (MAFLD) depends on the reduction of myosteatosis. In children, in whom the prevalence of MAFLD is alarming, this muscle-liver crosstalk has not been tested. Therefore, we aimed to explore whether the effects of a multicomponent intervention on hepatic fat is mediated by changes in intermuscular abdominal adipose tissue (IMAAT) in children with overweight/obesity. RESEARCH DESIGN AND METHODS: A total of 116 children with overweight/obesity were allocated to a 22-week family-based lifestyle and psychoeducational intervention (control group, n = 57) or the same intervention plus supervised exercise (exercise group, n = 59). Hepatic fat percentage and IMAAT were acquired by MRI at baseline and at the end of the intervention. RESULTS: Changes in IMAAT explained 20.7% of the improvements in hepatic steatosis (P < 0.05). Only children who meaningfully reduced their IMAAT (i.e., responders) had improved hepatic steatosis at the end of the intervention (within-group analysis: responders -20% [P = 0.005] vs. nonresponders -1.5% [P = 0.803]). Between-group analysis showed greater reductions in favor of IMAAT responders compared with nonresponders (18.3% vs. 0.6%, P = 0.018), regardless of overall abdominal fat loss. CONCLUSIONS: The reduction of IMAAT plays a relevant role in the improvement of hepatic steatosis after a multicomponent intervention in children with overweight/obesity. Indeed, only children who achieved a meaningful reduction in IMAAT at the end of the intervention had a reduced percentage of hepatic fat independent of abdominal fat loss. Our findings suggest that abdominal muscle fat infiltration could be a therapeutic target for the treatment of MAFLD in childhood.
Open Access
Evaluation of accurate eye corner detection methods for gaze estimation
(Bern Open Publishing, 2014) Bengoechea Irañeta, José Javier; Cerrolaza Martínez, Juan José; Villanueva Larre, Arantxa; Cabeza Laguna, Rafael; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
Accurate detection of iris center and eye corners appears to be a promising approach for low cost gaze estimation. In this paper we propose novel eye inner corner detection methods. Appearance and feature based segmentation approaches are suggested. All these methods are exhaustively tested on a realistic dataset containing images of subjects gazing at different points on a screen. We have demonstrated that a method based on a neural network presents the best performance even in light changing scenarios. In addition to this method, algorithms based on AAM and Harris corner detector present better accuracies than recent high performance face points tracking methods such as Intraface.
Open Access
Synthetic gaze data augmentation for improved user calibration
(Springer, 2021) Garde Lecumberri, Gonzalo; Larumbe Bergera, Andoni; Porta Cuéllar, Sonia; Cabeza Laguna, Rafael; Villanueva Larre, Arantxa; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Institute of Smart Cities - ISC; Ingeniería Eléctrica, Electrónica y de Comunicación
In this paper, we focus on the calibration possibilitiesó of a deep learning based gaze estimation process applying transfer learning, comparing its performance when using a general dataset versus when using a gaze specific dataset in the pretrained model. Subject calibration has demonstrated to improve gaze accuracy in high performance eye trackers. Hence, we wonder about the potential of a deep learning gaze estimation model for subject calibration employing fine-tuning procedures. A pretrained Resnet-18 network, which has great performance in many computer vision tasks, is fine-tuned using user’s specific data in a few shot adaptive gaze estimation approach. We study the impact of pretraining a model with a synthetic dataset, U2Eyes, before addressing the gaze estimation calibration in a real dataset, I2Head. The results of the work show that the success of the individual calibration largely depends on the balance between fine-tuning and the standard supervised learning procedures and that using a gaze specific dataset to pretrain the model improves the accuracy when few images are available for calibration. This paper shows that calibration is feasible in low resolution scenarios providing outstanding accuracies below 1.5 ∘ ∘ of error.
Open Access
Differences in specific abdominal fat depots between metabolically healthy and unhealthy children with overweight/obesity: the role of cardiorespiratory fitness
(Wiley, 2023) Cadenas-Sánchez, Cristina; Medrano Echeverría, María; Villanueva Larre, Arantxa; Cabeza Laguna, Rafael; Idoate, Fernando; Osés Recalde, Maddi; Rodríguez Vigil, Beatriz; Álvarez de Eulate, Natalia; Alberdi Aldasoro, Nerea; Ortega, Francisco B.; Labayen Goñi, Idoia; Ciencias de la Salud; Ingeniería Eléctrica, Electrónica y de Comunicación; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Institute of Smart Cities - ISC; Osasun Zientziak; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
Objectives: Fat depots localization has a critical role in the metabolic health status of adults. Nevertheless, whether that is also the case in children remains under- studied. Therefore, the aims of this study were: (i) to examine the differ-ences between metabolically healthy (MHO) and unhealthy (MUO) overweight/obesity phenotypes on specific abdominal fat depots, and (ii) to further explore whether cardiorespiratory fitness plays a major role in the differences between metabolic phenotypes among children with overweight/obesity. Methods: A total of 114 children with overweight/obesity (10.6 ±1.1 years, 62 girls) were included. Children were classified as MHO (n=68) or MUO. visceral (VAT), abdominal subcutaneous (ASAT), intermuscular abdominal (IMAAT), psoas, hepatic, pancreatic, and lumbar bone marrow adipose tissues were measured by magnetic resonance imaging. Cardiorespiratory fitness was assessed using the 20 m shuttle run test. Results: MHO children had lower VAT and ASAT contents and psoas fat fraction compared to MUO children (difference =12.4%– 25.8%, all p<0.035). MUO- unfit had more VAT and ASAT content than those MUO- fit and MHO- fit (difference =34.8%– 45.3%, all p<0.044). MUO- unfit shows also greater IMAAT fat fraction than those MUO- fit and MHO- fit peers (difference =16.4%– 13.9% respectively, all p≤0.001). In addition, MHO- unfit presented higher IMAAT fat fraction than MHO- fit (difference =13.4%, p<0.001). MUO- unfit presented higher psoas fat fraction than MHO- fit (difference =29.1%, p=0.008). Conclusions: VAT together with ASAT and psoas fat fraction, were lower in MHO than in MUO children. Further, we also observed that being fit, regardless of metabolic phenotype, has a protective role over the specific abdominal fat depots among children with overweight/obesity.
Open Access
Gaze tracking system model based on physical parameters
(World Scientific Publishing, 2007) Villanueva Larre, Arantxa; Cabeza Laguna, Rafael; Porta Cuéllar, Sonia; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
In the past years, research in eye tracking development and applications has attracted much attention and the possibility of interacting with a computer employing just gaze information is becoming more and more feasible. Efforts in eye tracking cover a broad spectrum of fields, system mathematical modeling being an important aspect in this research. Expressions relating to several elements and variables of the gaze tracker would lead to establish geometric relations and to find out symmetrical behaviors of the human eye when looking at a screen. To this end a deep knowledge of projective geometry as well as eye physiology and kinematics are basic. This paper presents a model for a bright-pupil technique tracker fully based on realistic parameters describing the system elements. The system so modeled is superior to that obtained with generic expressions based on linear or quadratic expressions. Moreover, model symmetry knowledge leads to more effective and simpler calibration strategies, resulting in just two calibration points needed to fit the optical axis and only three points to adjust the visual axis. Reducing considerably the time spent by other systems employing more calibration points renders a more attractive model.
Open Access
Robust and accurate 2D-tracking-based 3D positioning method: application to head pose estimation
(Elsevier, 2019) Ariz Galilea, Mikel; Villanueva Larre, Arantxa; Cabeza Laguna, Rafael; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
Head pose estimation (HPE) is currently a growing research field, mainly because of the proliferation of human–computer interfaces (HCI) in the last decade. It offers a wide variety of applications, including human behavior analysis, driver assistance systems or gaze estimation systems. This article aims to contribute to the development of robust and accurate HPE methods based on 2D tracking of the face, enhancing performance of both 2D point tracking and 3D pose estimation. We start with a baseline method for pose estimation based on POSIT algorithm. A novel weighted variant of POSIT is then proposed, together with a methodology to estimate weights for the 2D–3D point correspondences. Further, outlier detection and correction methods are also proposed in order to enhance both point tracking and pose estimation. With the aim of achieving a wider impact, the problem is addressed using a global approach: all the methods proposed are generalizable to any kind of object for which an approximate 3D model is available. These methods have been evaluated for the specific task of HPE using two different head pose video databases; a recently published one that reflects the expected performance of the system in current technological conditions, and an older one that allows an extensive comparison with state-of-the-art HPE methods. Results show that the proposed enhancements improve the accuracy of both 2D facial point tracking and 3D HPE, with respect to the implemented baseline method, by over 15% in normal tracking conditions and over 30% in noisy tracking conditions. Moreover, the proposed HPE system outperforms the state of the art on the two databases.
Open Access
Development of a prediction protocol for the screening of metabolic associated fatty liver disease in children with overweight or obesity
(Wiley, 2022) Osés Recalde, Maddi; Cadenas-Sánchez, Cristina; Medrano Echeverría, María; Galbete Jiménez, Arkaitz; Miranda Ferrúa, Emiliano; Ruiz, Jonatan R.; Sánchez-Valverde, Félix; Ortega, Francisco B.; Cabeza Laguna, Rafael; Villanueva Larre, Arantxa; Idoate, Fernando; Labayen Goñi, Idoia; Osasun Zientziak; Institute of Smart Cities - ISC; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Ciencias de la Salud; Gobierno de Navarra / Nafarroako Gobernua
Background: the early detection and management of children with metabolic associ-ated fatty liver disease (MAFLD) is challenging. Objective: to develop a non-invasive and accurate prediction protocol for the identi-fication of MAFLD among children with overweight/obesity candidates to confirma-tory diagnosis. Methods: a total of 115 children aged 8–12 years with overweight/obesity, rec-ruited at a primary care, were enrolled in this cross-sectional study. The external vali-dation was performed using a cohort of children with overweight/obesity (N=46)aged 8.5–14.0 years. MAFLD (≥5.5% hepatic fat) was diagnosed by magnetic reso-nance imaging (MRI). Fasting blood biochemical parameters were measured, and25 candidates’ single nucleotide polymorphisms (SNPs) were determined. Variablespotentially associated with the presence of MAFLD were included in a multivariatelogistic regression. Results: children with MAFLD (36%) showed higher plasma triglycerides (TG),insulin, homeostasis model assessment ofinsulin resistance (HOMA-IR), alanineaminotransferase (ALT), aspartate transaminase (AST), glutamyl-transferase (GGT)and ferritin (p< 0.05). The distribution of the risk-alleles of PPARGrs13081389, PPARGrs1801282, HFErs1800562 and PNLPLA3rs4823173 was significantly different between children with and without MAFLD (p<0.05). Threebiochemical- and/or SNPs-based predictive models were developed, showingstrong discriminatory capacity (AUC-ROC: 0.708–0.888) but limited diagnosticperformance (sensitivity 67%–82% and specificity 63%–69%). A prediction proto-col with elevated sensitivity (72%) and specificity (84%) based on two consecutive steps was developed. The external validation showed similar results: sensitivity of 70% and specificity of 85%. Conclusions: the HEPAKID prediction protocol is an accurate, easy to implant, minimally invasive and low economic cost tool useful for the early identification and management of paediatric MAFLD in primary care.
Open Access
Beyond basic tuning: exploring discrepancies in user and setup calibration for gaze estimation
(ACM, 2024-06-04) Garde Lecumberri, Gonzalo; Armendáriz Armenteros, José María; Beruete Cerezo, Rubén; Cabeza Laguna, Rafael; Villanueva Larre, Arantxa; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza
Calibrating gaze estimation models is crucial to maximize the effectiveness of these systems, although its implementation also poses challenges related to usability. Therefore, the simplification of this process is key. In this work, we dissect the impact of calibration due to both the environment and the user in gaze estimation models that employ general-purpose devices. We aim to replicate a workflow close to the final application by starting with pre-trained models and subsequently calibrating them using different strategies, testing under various camera arrangements and user-specific variability. The results indicate differentiation between the impact due to the user and the setup, being the components due to the users a slightly more pronounced impact than those related to the setup, opening the door to understanding calibration as a composite process. In any case, the development of calibration-free remote gaze estimation solutions remains a great challenge, given the crucial role of calibration.
Open Access
Accurate pupil center detection in off-the-shelf eye tracking systems using convolutional neural networks
(MDPI, 2021) Larumbe Bergera, Andoni; Garde Lecumberri, Gonzalo; Porta Cuéllar, Sonia; Cabeza Laguna, Rafael; Villanueva Larre, Arantxa; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Remote eye tracking technology has suffered an increasing growth in recent years due to its applicability in many research areas. In this paper, a video-oculography method based on convolutional neural networks (CNNs) for pupil center detection over webcam images is proposed. As the first contribution of this work and in order to train the model, a pupil center manual labeling procedure of a facial landmark dataset has been performed. The model has been tested over both real and synthetic databases and outperforms state-of-the-art methods, achieving pupil center estimation errors below the size of a constricted pupil in more than 95% of the images, while reducing computing time by a 8 factor. Results show the importance of use high quality training data and well-known architectures to achieve an outstanding performance.
Open Access
Low cost gaze estimation: knowledge-based solutions
(IEEE, 2020) Martinikorena Aranburu, Ion; Larumbe Bergera, Andoni; Ariz Galilea, Mikel; Porta Cuéllar, Sonia; Cabeza Laguna, Rafael; Villanueva Larre, Arantxa; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
Eye tracking technology in low resolution scenarios is not a completely solved issue to date. The possibility of using eye tracking in a mobile gadget is a challenging objective that would permit to spread this technology to non-explored fields. In this paper, a knowledge based approach is presented to solve gaze estimation in low resolution settings. The understanding of the high resolution paradigm permits to propose alternative models to solve gaze estimation. In this manner, three models are presented: a geometrical model, an interpolation model and a compound model, as solutions for gaze estimation for remote low resolution systems. Since this work considers head position essential to improve gaze accuracy, a method for head pose estimation is also proposed. The methods are validated in an optimal framework, I2Head database, which combines head and gaze data. The experimental validation of the models demonstrates their sensitivity to image processing inaccuracies, critical in the case of the geometrical model. Static and extreme movement scenarios are analyzed showing the higher robustness of compound and geometrical models in the presence of user’s displacement. Accuracy values of about 3◦ have been obtained, increasing to values close to 5◦ in extreme displacement settings, results fully comparable with the state-of-the-art.