Browsing by Keyword "Computer Science Applications"
Now showing 1 - 20 of 101
Results Per Page
Sort Options
Item 3D Active Surfaces for Liver Segmentation in Multisequence MRI Images(2016-08-01) Bereciartua, Arantza; Picon, Artzai; Galdran, Adrian; Iriondo, Pedro M.; COMPUTER_VISION; Tecnalia Research & InnovationBiopsies for diagnosis can sometimes be replaced by non-invasive techniques such as CT and MRI. Surgeons require accurate and efficient methods that allow proper segmentation of the organs in order to ensure the most reliable intervention planning. Automated liver segmentation is a difficult and open problem where CT has been more widely explored than MRI. MRI liver segmentation represents a challenge due to the presence of characteristic artifacts, such as partial volumes, noise and low contrast. In this paper, we present a novel method for multichannel MRI automatic liver segmentation. The proposed method consists of the minimization of a 3D active surface by means of the dual approach to the variational formulation of the underlying problem. This active surface evolves over a probability map that is based on a new compact descriptor comprising spatial and multisequence information which is further modeled by means of a liver statistical model. This proposed 3D active surface approach naturally integrates volumetric regularization in the statistical model. The advantages of the compact visual descriptor together with the proposed approach result in a fast and accurate 3D segmentation method. The method was tested on 18 healthy liver studies and results were compared to a gold standard made by expert radiologists. Comparisons with other state-of-the-art approaches are provided by means of nine well established quality metrics. The obtained results improve these methodologies, achieving a Dice Similarity Coefficient of 98.59.Item Accessible Ubiquitous Services for Supporting Daily Activities: A Case Study with Young Adults with Intellectual Disabilities(2018-12-28) Aizpurua, Amaia; Miñón, Raúl; Gamecho, Borja; Cearreta, Idoia; Arrue, Myriam; Garay-Vitoria, Nestor; HPAUbiquitous environments have considerable potential to provide services supporting daily activities (using public transportation to and from workplace, using ATM machines, selecting and purchasing goods in ticketing or vending machines, etc.) in order to assist people with disabilities. Nevertheless, the ubiquitous service providers generally supply generic user interfaces which are not usually accessible for all potential end users. In this article, a case study to verify the adequacy of the user interfaces automatically generated by the Egoki system for two supporting ubiquitous services adapted to young adults with moderate intellectual disabilities was presented. The task completion times and the level of assistance required by participants when using the interfaces were analyzed. Participants were able to access services through a tablet and successfully complete the tasks, regardless of their level of expertise and familiarity with the service. Moreover, results indicate that their performance and confidence improved with practice, as they required fewer direct verbal and pointer cues to accomplish tasks. By applying observational methods during the experimental sessions, several potential improvements for the automated interface generation process were also detected.Item Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB(IEEE, 2017-10) Alvarez-Gila, Aitor; Van de Weijer, Joost; Garrote, Estibaliz; Tecnalia Research & Innovation; QuantumHyperspectral signal reconstruction aims at recovering the original spectral input that produced a certain trichromatic (RGB) response from a capturing device or observer. Given the heavily underconstrained, non-linear nature of the problem, traditional techniques leverage different statistical properties of the spectral signal in order to build informative priors from real world object reflectances for constructing such RGB to spectral signal mapping. However, most of them treat each sample independently, and thus do not benefit from the contextual information that the spatial dimensions can provide. We pose hyperspectral natural image reconstruction as an image to image mapping learning problem, and apply a conditional generative adversarial framework to help capture spatial semantics. This is the first time Convolutional Neural Networks -and, particularly, Generative Adversarial Networks- are used to solve this task. Quantitative evaluation shows a Root Mean Squared Error (RMSE) drop of 44.7% and a Relative RMSE drop of 47.0% on the ICVL natural hyperspectral image dataset.Item Analysis and comparative study of factors affecting quality in the hemming of 6016T4AA performed by means of electromagnetic forming and process characterization(2011-05-01) Jimbert, P.; Eguia, I.; Perez, I.; Gutierrez, M.A.; Hurtado, I.; COMPOSITE; Tecnalia Research & Innovation; PROMETALHemming is commonly one of the last operations for stamped parts. For this reason it is of critical importance on the performance and perceived quality of assembled vehicles. However, designing the hemmed unión is a complicated task and is deeply influenced by the mechanical properties of the materail of the bent part. Significant problems can arise in this operation when bending aluminum alloys, because cracks can appera due to the localized strain during hemming as a result of the low ductility of automotive aluminum alloys. This paper presents the devlopment of the lectromagnetic forming (EMF) technology for auto body-in-white parts hemming. A relatively simple experimental procedure to perform a hemming operation based on th eprinciple of EMF is presented in order to compare the variation in the quality parameters of a hemmed joint. The achieved results are compared with the corrresponding geometry hemmed utilizing the conventional process. At the same time, the study is completed with the development of a new simulation method for the EMF technology. The results obtained during this study prove the capability of the EMF to obatin quality hem unions simplifying the complicated conventional hemming operation. In this study a loose coupling EMF hemming simulation method has been developed using Maxwell 3D to solve the electromagnetic field computation and Abaqus to solve the mechanical computation. Thsi simulation method shows good agreement with the physiscal experiments. Finally, the EMF hemming process is characterized by analyzing the influence of main input parameters on the quality output parameters.Item Analysis of Field Data to Describe the Effect of Context (Acoustic and Non-Acoustic Factors) on Urban Soundscapes(2017) Herranz-Pascual, Karmele; García, Igone; Diez, Itxasne; Santander, Alvaro; Aspuru, Itziar; Tecnalia Research & Innovation; CALIDAD Y CONFORT AMBIENTAL; ADAPTACIÓN AL CAMBIO CLIMÁTICOThe need to improve acoustic environments in our cities has led to increased interest in correcting or minimising noise pollution in urban environments, something that has been associated with the resurgence of the soundscape approach. This line of research highlights the importance of context in the perception of acoustic environments. Despite this, few studies consider together a wide number of variables relating to the context, and analyse the relative importance of each. The purpose of this paper is therefore to identify the acoustic and non-acoustic characteristics of a place (context) that influence an individual’s perception of the sound environment and the relative importance of these factors in soundscape. The aim is to continue advancing in the definition of an acoustic comfort indicator for urban places. The data used here were collected in various soundscape campaigns carried out by Tecnalia in Bilbao (Spain) between 2011 and 2014. These studies involved 534 evaluations of 10 different places. The results indicate that many diverse contextual factors determine soundscape, the most important being the congruence between soundscape and landscape. The limitations of the findings and suggestions for further research are also discussed.Item Analysis on the characterization of multiphoton microscopy images for malignant neoplastic colon lesion detection under deep learning methods(2021-01-01) Terradillos, Elena; Saratxaga, CristinaL; Mattana, Sara; Cicchi, Riccardo; Pavone, FrancescoS; Andraka, Nagore; Glover, BenjaminJ; Arbide, Nagore; Velasco, Jacques; Etxezarraga, MªCarmen; Picon, Artzai; VISUALColorectal cancer has a high incidence rate worldwide, with over 1.8 million new cases and 880,792 deaths in 2018. Fortunately, its early detection significantly increases the survival rate, reaching a cure rate of 90% when diagnosed at a localized stage. Colonoscopy is the gold standard technique for detection and removal of colorectal lesions with potential to evolve into cancer. When polyps are found in a patient, the current procedure is their complete removal. However, in this process, gastroenterologists cannot assure complete resection and clean margins which are given by the histopathology analysis of the removed tissue, which is performed at laboratory. Aims: In this paper, we demonstrate the capabilities of multiphoton microscopy (MPM) technology to provide imaging biomarkers that can be extracted by deep learning techniques to identify malignant neoplastic colon lesions and distinguish them from healthy, hyperplastic, or benign neoplastic tissue, without the need for histopathological staining. Materials and Methods: To this end, we present a novel MPM public dataset containing 14,712 images obtained from 42 patients and grouped into 2 classes. A convolutional neural network is trained on this dataset and a spatially coherent predictions scheme is applied for performance improvement. Results: We obtained a sensitivity of 0.8228 ± 0.1575 and a specificity of 0.9114 ± 0.0814 on detecting malignant neoplastic lesions. We also validated this approach to estimate the self-confidence of the network on its own predictions, obtaining a mean sensitivity of 0.8697 and a mean specificity of 0.9524 with the 18.67% of the images classified as uncertain. Conclusions: This work lays the foundations for performing in vivo optical colon biopsies by combining this novel imaging technology together with deep learning algorithms, hence avoiding unnecessary polyp resection and allowing in situ diagnosis assessment.Item Application of full-reference video quality metrics in IPTV(IEEE, 2017-07-20) Sedano, Inigo; Prieto, Gorka; Brunnstrom, Kjell; Kihl, Maria; Montalban, Jon; FACTORYExecuting an accurate full-reference metric such as VQM can take minutes in an average computer for just one user. Therefore, it can be unfeasible to analyze all the videos received by users in an IPTV network for example consisting of 10.000 users using a single computer running the VQM metric. One solution can be to use a lightweight no-reference metrics in addition to the full-reference metric mentioned. Lightweight no-reference metrics can be used for discarding potential situations to evaluate because they are accurate enough for that task, and then the full-reference metric VQM can be used when more accuracy is needed. The work in this paper is focused on determining the maximum number of situations/users that can be analyzed simultaneously using the VQM metric in a computer with good performance. The full-reference metric is applied on the transmitter using a method specified in the recommendation ITU BT.1789. The best performance achieved was 112.8 seconds per process.Item Approaching maker's phenomenon(2016) Tabarés-Gutiérrez, Raúl; Tecnalia Research & Innovation; BIGDATAThe rising of maker's movement in recent years has been spoiled by the popularization of open source technologies like 3d printing and many others. The expiration of a set of patents have made possible the emergence of several and different communities that play and tinker with technology. At the same time, these new sociotechnology based collectivities have its origins in other pre-existing ones such as "Do It Yourself" and "Hackers". Our goal in this paper is to perform a comprehensive analysis of all these trends reviewing the existing literature and identifying the main features, values and aspirations. Moreover, we argue some policy recommendations in order to maximize the impact of these spaces into the urban sphere trying to boost its potential in education and social innovation.Item Augmented Reality for Supporting Workers in Human–Robot Collaboration(2023-04-10) Moya, Ana; Bastida, Leire; Aguirrezabal, Pablo; Pantano, Matteo; Abril-Jiménez, Patricia; ADV_INTER_PLAT; VISUALThis paper discusses the potential benefits of using augmented reality (AR) technology to enhance human–robot collaborative industrial processes. The authors describe a real-world use case at Siemens premises in which an AR-based authoring tool is used to reduce cognitive load, assist human workers in training robots, and support calibration and inspection tasks during assembly tasks. The study highlights the potential of AR as a solution for optimizing human–robot collaboration and improving productivity. The article describes the methodology used to deploy and evaluate the ARContent tool, which demonstrated improved usability, reduced task load, and increased efficiency in the assembly process. However, the study is limited by the restricted availability of workers and their knowledge of assembly tasks with robots. The authors suggest that future work should focus on testing the ARContent tool with a larger user pool and improving the authoring tool based on the shortcomings identified during the study. Overall, this work shows the potential for AR technology to revolutionize industrial processes and improve collaboration between humans and robots.Item Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case(2017-06-01) Johannes, Alexander; Picon, Artzai; Alvarez-Gila, Aitor; Echazarra, Jone; Rodriguez-Vaamonde, Sergio; Navajas, Ana Díez; Ortiz-Barredo, Amaia; Tecnalia Research & Innovation; COMPUTER_VISION; VISUALDisease diagnosis based on the detection of early symptoms is a usual threshold taken into account for integrated pest management strategies. Early phytosanitary treatment minimizes yield losses and increases the efficacy and efficiency of the treatments. However, the appearance of new diseases associated to new resistant crop variants complicates their early identification delaying the application of the appropriate corrective actions. The use of image based automated identification systems can leverage early detection of diseases among farmers and technicians but they perform poorly under real field conditions using mobile devices. A novel image processing algorithm based on candidate hot-spot detection in combination with statistical inference methods is proposed to tackle disease identification in wild conditions. This work analyses the performance of early identification of three European endemic wheat diseases – septoria, rust and tan spot. The analysis was done using 7 mobile devices and more than 3500 images captured in two pilot sites in Spain and Germany during 2014, 2015 and 2016. Obtained results reveal AuC (Area under the Receiver Operating Characteristic –ROC– Curve) metrics higher than 0.80 for all the analyzed diseases on the pilot tests under real conditions.Item Benchmarking Bipedal Locomotion: A Unified Scheme for Humanoids, Wearable Robots, and Humans: A Unified Scheme for Humanoids, Wearable Robots, and Humans(2015-09-10) Torricelli, Diego; Gonzalez-Vargas, Jose; Veneman, Jan F.; Mombaur, Katja; Tsagarakis, Nikos; del-Ama, Antonio J.; Gil-Agudo, Angel; Moreno, Juan C.; Pons, Jose L.; Tecnalia Research & InnovationIn the field of robotics, there is a growing awareness of the importance of benchmarking [1], [2]. Benchmarking not only allows the assessment and comparison of the performance of different technologies but also defines and supports the standardization and regulation processes during their introduction to the market. Its importance has been recently emphasized by the adoption of the technology readiness levels (TRLs) in the Horizon 2020 information and communication technologies by the European Union as an important guideline to assess when a technology can shift from one TRL to the other. The objective of this article is to define the basis of a benchmarking scheme for the assessment of bipedal locomotion that could be applied and shared across different research communities.Item BETaaS: A Platform for Development and Execution of Machine-to-Machine Applications in the Internet of Things: A Platform for Development and Execution of Machine-to-Machine Applications in the Internet of Things(2016-04-01) Vallati, Carlo; Mingozzi, Enzo; Tanganelli, Giacomo; Buonaccorsi, Novella; Valdambrini, Nicola; Zonidis, Nikolaos; Martinez-Rodriguez, Belen; Mamelli, Alessandro; Sommacampagna, Davide; Anggorojati, Bayu; Kyriazakos, Sofoklis; Prasad, Neeli; Nieto, Francisco Javier; Barreto, Oliver; Rodriguez, Oliver Barreto; Tecnalia Research & Innovation; BIGDATAThe integration of everyday objects into the Internet represents the foundation of the forthcoming Internet of Things (IoT). Such “smart” objects will be the building blocks of the next generation of applications that will exploit interaction between machines to implement enhanced services with minimum or no human intervention in the loop. A crucial factor to enable Machine-to-Machine (M2M) applications is a horizontal service infrastructure that seamlessly integrates existing IoT heterogeneous systems. The authors present BETaaS, a framework that enables horizontal M2M deployments. BETaaS is based on a distributed service infrastructure built on top of an overlay network of gateways that allows seamless integration of existing IoT systems. The platform enables easy deployment of applications by exposing to developers a service oriented interface to access things (the Things-as-a-Service model) regardless of the technology and the physical infrastructure they belong.Item Central lessons from the historical analysis of 24 reinforced-concrete structures in northern Spain(2016-07-01) Marcos, Ignacio; San-José, José Tomás; Garmendia, Leire; Santamaría, Amaia; Manso, Juan Manuel; Tecnalia Research & InnovationSince the late-nineteenth century, the use of reinforced-concrete as a structural material has proliferated and is now commonplace in the modern built environment. Some of the structures from that century are even considered cultural heritage. In the early stages of its technical development, concrete was seen as practically immutable over time; however, prolonged exposure to environmental agents has revealed its very significant problems of weakening strength and durability. A total of 24 aging reinforced-concrete structures in the Basque Country (northern Spain) and their behavior over time are analyzed in this paper. Reference is made to pathological reports, categorized for the purposes of this study, which characterize their concrete and steel components. This contribution greatly enhances our knowledge of each structure for future studies and for the improvement of their conservation strategies.Item Characterization of Optical Coherence Tomography Images for Colon Lesion Differentiation under Deep Learning(2021-04-01) Saratxaga, Cristina L.; Bote, Jorge; Ortega-Morán, Juan F.; Picón, Artzai; Terradillos, Elena; del Río, Nagore Arbide; Andraka, Nagore; Garrote, Estibaliz; Conde, Olga M.; VISUAL; COMPUTER_VISION; Quantum(1) Background: Clinicians demand new tools for early diagnosis and improved detection of colon lesions that are vital for patient prognosis. Optical coherence tomography (OCT) allows microscopical inspection of tissue and might serve as an optical biopsy method that could lead to in-situ diagnosis and treatment decisions; (2) Methods: A database of murine (rat) healthy, hyperplastic and neoplastic colonic samples with more than 94,000 images was acquired. A methodology that includes a data augmentation processing strategy and a deep learning model for automatic classification (benign vs. malignant) of OCT images is presented and validated over this dataset. Comparative evaluation is performed both over individual B-scan images and C-scan volumes; (3) Results: A model was trained and evaluated with the proposed methodology using six different data splits to present statistically significant results. Considering this, 0.9695 (_0.0141) sensitivity and 0.8094 (_0.1524) specificity were obtained when diagnosis was performed over B-scan images. On the other hand, 0.9821 (_0.0197) sensitivity and 0.7865 (_0.205) specificity were achieved when diagnosis was made considering all the images in the whole C-scan volume; (4) Conclusions: The proposed methodology based on deep learning showed great potential for the automatic characterization of colon polyps and future development of the optical biopsy paradigm.Item CloudOps: Towards the Operationalization of the Cloud Continuum: Concepts, Challenges and a Reference Framework: Towards the Operationalization of the Cloud Continuum: Concepts, Challenges and a Reference Framework(2022-04-25) Alonso, Juncal; Orue-Echevarria, Leire; Huarte, Maider; HPA; Tecnalia Research & InnovationThe current trend of developing highly distributed, context aware, heterogeneous computing intense and data-sensitive applications is changing the boundaries of cloud computing. Encouraged by the growing IoT paradigm and with flexible edge devices available, an ecosystem of a combination of resources, ranging from high density compute and storage to very lightweight embedded computers running on batteries or solar power, is available for DevOps teams from what is known as the Cloud Continuum. In this dynamic context, manageability is key, as well as controlled operations and resources monitoring for handling anomalies. Unfortunately, the operation and management of such heterogeneous computing environments (including edge, cloud and network services) is complex and operators face challenges such as the continuous optimization and autonomous (re-)deployment of context-aware stateless and stateful applications where, however, they must ensure service continuity while anticipating potential failures in the underlying infrastructure. In this paper, we propose a novel CloudOps workflow (extending the traditional DevOps pipeline), proposing techniques and methods for applications’ operators to fully embrace the possibilities of the Cloud Continuum. Our approach will support DevOps teams in the operationalization of the Cloud Continuum. Secondly, we provide an extensive explanation of the scope, possibilities and future of the CloudOps.Item Compact and cost effective instrument for detecting drug precursors in different environments based on fluorescence polarization(SPIE-INT SOC OPTICAL ENGINEERING, 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA, 2013) Antolín-Urbaneja, Juan Carlos; Eguizabal, I.; Briz, N.; Dominguez, A.; Estensoro, Patxi; Secchi, A.; Varriale, A.; Di Giovanni, S.; D´Auria, S.; Tecnalia Research & Innovation; ROBOTICA_AUTOMA; TECNOLOGÍAS DE HIDRÓGENO; SG; MAQUINAS; GENERALSeveral techniques for detecting chemical drug precursors have been developed in the last decade. Most of them are able to identify molecules at very low concentration under lab conditions. Other commercial devices are able to detect a fixed number and type of target substances based on a single detection technique providing an absence of flexibility with respect to target compounds. The construction of compact and easy to use detection systems providing screening for a large number of compounds being able to discriminate them with low false alarm rate and high probability of detection is still an open concern. Under CUSTOM project, funded by the European Commission within the FP7, a stand-alone portable sensing device based on multiple techniques is being developed. One of these techniques is based on the LED induced fluorescence polarization to detect Ephedrine and Benzyl Methyl Keton (BMK) as a first approach. This technique is highly selective with respect to the target compounds due to the generation of properly engineered fluorescent proteins which are able to bind the target analytes, as it happens in an “immune-type reaction”. This paper deals with the advances in the design, construction and validation of the LED induced fluorescence sensor to detect BMK analytes. This sensor includes an analysis module based on high performance LED and PMT detector, a fluidic system to dose suitable quantities of reagents and some printed circuit boards, all of them fixed in a small structure (167mm x 193mm x 228mm) with the capability of working as a stand-alone application.Item A Comparison Between Coupled and Decoupled Vehicle Motion Controllers Based on Prediction Models(IEEE, 2019-06) Matute, Jose A.; Lattarulo, Ray; Zubizarreta, Asier; Perez, Joshue; CCAMIn this work, a comparative study is carried out with two different predictive controllers that consider the longitudinal jerk and steering rate change as additional parameters, as additional parameters, so that comfort constraints can be included. Furthermore, the approaches are designed so that the effect of longitudinal and lateral motion control coupling can be analyzed. This way, the first controller is a longitudinal and lateral coupled MPC approach based on a kinematic model of the vehicle, while the second is a decoupled strategy based on a triple integrator model based on MPC for the longitudinal control and a double proportional curvature control for the lateral motion control. The control architecture and motion planning are exhaustively explained. The comparative study is carried out using a test vehicle, whose dynamics and low-level controllers have been simulated using the realistic simulation environment Dynacar. The performed tests demonstrate the effectiveness of both approaches in speeds higher than 30 km/h, and demonstrate that the coupled strategy provides better performance than the decoupled one. The relevance of this work relies in the contribution of vehicle motion controllers considering the comfort and its advantage over decoupled alternatives for future implementation in real vehicles.Item Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions(2019-12) Picon, Artzai; Seitz, Maximiliam; Alvarez-Gila, Aitor; Mohnke, Patrick; Ortiz-Barredo, Amaia; Echazarra, Jone; Tecnalia Research & Innovation; COMPUTER_VISION; VISUALConvolutional Neural Networks (CNN) have demonstrated their capabilities on the agronomical field, especially for plant visual symptoms assessment. As these models grow both in the number of training images and in the number of supported crops and diseases, there exist the dichotomy of (1) generating smaller models for specific crop or, (2) to generate a unique multi-crop model in a much more complex task (especially at early disease stages) but with the benefit of the entire multiple crop image dataset variability to enrich image feature description learning. In this work we first introduce a challenging dataset of more than one hundred-thousand images taken by cell phone in real field wild conditions. This dataset contains almost equally distributed disease stages of seventeen diseases and five crops (wheat, barley, corn, rice and rape-seed) where several diseases can be present on the same picture. When applying existing state of the art deep neural network methods to validate the two hypothesised approaches, we obtained a balanced accuracy (BAC=0.92) when generating the smaller crop specific models and a balanced accuracy (BAC=0.93) when generating a single multi-crop model. In this work, we propose three different CNN architectures that incorporate contextual non-image meta-data such as crop information onto an image based Convolutional Neural Network. This combines the advantages of simultaneously learning from the entire multi-crop dataset while reducing the complexity of the disease classification tasks. The crop-conditional plant disease classification network that incorporates the contextual information by concatenation at the embedding vector level obtains a balanced accuracy of 0.98 improving all previous methods and removing 71% of the miss-classifications of the former methods.Item CURIE: a cellular automaton for concept drift detection: a cellular automaton for concept drift detection(2021-11) Lobo, Jesus L.; Del Ser, Javier; Osaba, Eneko; Bifet, Albert; Herrera, Francisco; IA; QuantumData stream mining extracts information from large quantities of data flowing fast and continuously (data streams). They are usually affected by changes in the data distribution, giving rise to a phenomenon referred to as concept drift. Thus, learning models must detect and adapt to such changes, so as to exhibit a good predictive performance after a drift has occurred. In this regard, the development of effective drift detection algorithms becomes a key factor in data stream mining. In this work we propose CURIECURIE, a drift detector relying on cellular automata. Specifically, in CURIECURIE the distribution of the data stream is represented in the grid of a cellular automata, whose neighborhood rule can then be utilized to detect possible distribution changes over the stream. Computer simulations are presented and discussed to show that CURIECURIE, when hybridized with other base learners, renders a competitive behavior in terms of detection metrics and classification accuracy. CURIECURIE is compared with well-established drift detectors over synthetic datasets with varying drift characteristics.Item Cyber Hygiene Maturity Assessment Framework for Smart Grid Scenarios(2021-03-10) Skarga-Bandurova, Inna; Kotsiuba, Igor; Velasco, Erkuden Rios; CIBERSEC&DLTCyber hygiene is a relatively new paradigm premised on the idea that organizations and stakeholders are able to achieve additional robustness and overall cybersecurity strength by implementing and following sound security practices. It is a preventive approach entailing high organizational culture and education for information cybersecurity to enhance resilience and protect sensitive data. In an attempt to achieve high resilience of Smart Grids against negative impacts caused by different types of common, predictable but also uncommon, unexpected, and uncertain threats and keep entities safe, the Secure and PrivatE smArt gRid (SPEAR) Horizon 2020 project has created an organization-wide cyber hygiene policy and developed a Cyber Hygiene Maturity assessment Framework (CHMF). This article presents the assessment framework for evaluating Cyber Hygiene Level (CHL) in relation to the Smart Grids. Complementary to the SPEAR Cyber Hygiene Maturity Model (CHMM), we propose a self-assessment methodology based on a questionnaire for Smart Grid cyber hygiene practices evaluation. The result of the assessment can be used as a cyber-health check to define countermeasures and to reapprove cyber hygiene rules and security standards and specifications adopted by the Smart Grid operator organization. The proposed methodology is one example of a resilient approach to cybersecurity. It can be applied for the assessment of the CHL of Smart Grids operating organizations with respect to a number of recommended good practices in cyber hygiene.