Nikolaos Grammalidis - Academia.edu (original) (raw)

Papers by Nikolaos Grammalidis

Remote Sensing

Nowadays, different machine learning approaches, either conventional or more advanced, use input ... more Nowadays, different machine learning approaches, either conventional or more advanced, use input from different remote sensing imagery for land cover classification and associated decision making. However, most approaches rely heavily on time-consuming tasks to gather accurate annotation data. Furthermore, downloading and pre-processing remote sensing imagery used to be a difficult and time-consuming task that discouraged policy makers to create and use new land cover maps. We argue that by combining recent improvements in deep learning with the use of powerful cloud computing platforms for EO data processing, specifically the Google Earth Engine, we can greatly facilitate the task of land cover classification. For this reason, we modify an efficient semantic segmentation approach (U-TAE) for a satellite image time series to use, as input, a single multiband image composite corresponding to a specific time range. Our motivation is threefold: (a) to improve land cover classification ...

Euro-Mediterranean Journal for Environmental Integration

Today’s remote sensing data and technologies offer the capability to effectively monitor diverse ... more Today’s remote sensing data and technologies offer the capability to effectively monitor diverse and challenging environments around the world, such as coastal river and riparian zones. Coastal riparian zones and river deltas usually demonstrate extreme coastline changes in terms of the extent of water coverage of inland territories due to flood events, low and high tides, the climate, specific environmental characteristics, etc. In this paper, we exploit freely available multispectral time series data for previous decades, utilizing Landsat missions in order to develop an open-source-based image processing pipeline for the extraction of the actual yearly average coastline status of riparian river delta areas. The latter present significant temporal coastline changes between years, semesters, and months. Average mean maps are generated and then compared to several temporal levels in order to distinguish long-term significant changes and ecosystem threats. Additionally, a custom long...

Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments

Innovative interventions for Parkinson's disease patients using the iPrognosis Games: An evaluati... more Innovative interventions for Parkinson's disease patients using the iPrognosis Games: An evaluation analysis by medical experts. In Proceedings of ACM PETRA conference (PETRA'20).

Journal of Marine Science and Engineering

Nowadays, coastal areas are exposed to multiple hazards of increasing severity, such as coastal f... more Nowadays, coastal areas are exposed to multiple hazards of increasing severity, such as coastal floods, erosion, subsidence due to a combination of natural and anthropogenic factors, including climate change and urbanisation. In order to cope with these challenges, new remote sensing monitoring solutions are required that are based on knowledge extraction and state of the art machine learning solutions that provide insights into the related physical mechanisms and allow the creation of innovative Decision Support Tools for managing authorities. In this paper, a novel user-friendly monitoring system is presented, based on state-of-the-art remote sensing and machine learning approaches. It uses processes for collecting and analysing data from various heterogeneous sources (satellite, in-situ, and other auxiliary data) for monitoring land cover and land use changes, coastline changes soil erosion, land deformations, and sea/ground water level. A rule-based Decision Support System (DSS)...

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)

The use of serious games in health care interventions sector has grown rapidly in the last years,... more The use of serious games in health care interventions sector has grown rapidly in the last years, however, there is still a gap in the understanding on how these types of interventions are used for the management of the Parkinson Disease (PD), in particular. Targeting intelligent early detection and intervention in PD area, the Personalized Game Suite (PGS) design process approach is presented as part of the H2020 i-PROGNOSIS project that introduces the integration of different serious games in a unified platform (i.e., ExerGames, DietaryGames, EmoGames, and Handwriting/Voice Games). From the methodological point of view, to facilitate the visualization of 14 game-scenarios, the system interface and the PD contexts, the storyboarding technique was adopted here. Overall, the realization of the PGS sets the basis for establishing a holistic framework that could aim at improving motor and nonmotor symptoms, in order to inform health care providers and policy makers for its inclusion in routine management for PD.

2017 9th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games), 2017

As of the early 20 th century, a significant body of research has been published that shows how e... more As of the early 20 th century, a significant body of research has been published that shows how effective game-based learning and gamification techniques can be compared to other methods. However, creating games can be time consuming and usually demands a significant effort. Therefore, this paper focuses on the design and development of a novel framework for the rapid design of body-motion-based customizable game-like applications. This framework consists of two components: i) an interface that allows the user to design the game and capture the motion data, and ii) a customizable game for learning and training using off-the-shelf motion capture sensors like the Microsoft Kinect. The game is automatically configured based on the output of the game design interface. In order to evaluate the proposed system, a pilot use case for the Latin dance Salsa has been selected. Preliminary small-scaled experiments with latin dance students have shown the great potential of the proposed application.

Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments, 2019

Parkinson's disease (PD) is a progressive neurological disorder and the second most common age-re... more Parkinson's disease (PD) is a progressive neurological disorder and the second most common age-related neurodegenerative disease after Alzheimer's disease. The primary symptoms of the disease are associated with the loss of motor skills affecting patients' movement and coordination and disrupting their daily life. Unfortunately, such motor symptoms cannot be fully relieved by therapeutic options. On the other hand, studies have shown that regular training and exercising can prove neuroprotective in PD patients helping them maintain independent longer. Based on recent studies stating that computer-based physical therapy games can be used as an option for facilitating PD rehabilitation exercise programs, we present the development of a body motion based videogame, using the Kinect sensor, targeted for PD patients. We tested twelve patients with advanced forms of PD motor symptoms (UPDRS motor score>20) and six initial stage PD patients (UPDRS motor score<20). All participants underwent an (UPDRS) motor skills pretest and afterwards performed three training sessions. In this paper, we will present part of our research aiming to analyze the movement patterns of PD patients in order to detect statistical significant differences between groups of different impairment level based on their UPDRS motor score and their performance. Consequently, we adopt a deep learning approach by analyzing the recorded human skeleton sequences for predicting the players' level of motor skills decline. Such methods and data can serve as preliminary evidence for further larger and controlled studies to propose such an exergame that can independently detect and adapt its difficulty level to better match players' ability providing a more targeted and personalized rehabilitation option.

Publication in the conference proceedings of EUSIPCO, Lisbon, Portugal, 2014

Publication in the conference proceedings of EUSIPCO, Lausanne, Switzerland, 2008

Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments, 2019

Hypomimia, i.e. reduction in the expressiveness of the face, is a cardinal sign of the PD, often ... more Hypomimia, i.e. reduction in the expressiveness of the face, is a cardinal sign of the PD, often present at its early stages. Within the EU-funded i-Prognosis project (http://www.i-prognosis.eu), early and unobtrusive Parkinson's disease detection tests are developed, based on the interaction of users with everyday technological devices. The selfie analysis module translates facial expression features into an index reflecting the severity of PD hypomimia symptoms that affect the variability of patients' facial expressions. Monitoring of such an index over time holds the promise to detect the onset of hypomimia symptoms in an unobtrusive way. Our approach proposes a methodology for detecting and quantifying the progressive decrease of variability of facial expressions in early PD patients by analysing patterns emerging from photos (selfies) during daily life. Promising results are presented from both a) a small development set of 36 users (both PD patients and healthy controls) and b) a large set of selfie photos obtained from 1292 users that were analysed by the iPrognosis cloud analysis module.

Publication in the conference proceedings of EUSIPCO, Barcelona, Spain, 2011

Proceedings of the 9th International Conference on Computer Vision Theory and Applications, 2014

In this paper we present a method for body motion analysis in dance using multiple Kinect sensors... more In this paper we present a method for body motion analysis in dance using multiple Kinect sensors. The proposed method applies fusion to combine the skeletal tracking data of multiple sensors in order to solve occlusion and self-occlusion tracking problems and increase the robustness of skeletal tracking. The fused skeletal data is split into five different body parts (torso, left hand, right hand, left leg and right leg), which are then transformed to allow view invariant posture recognition. For each part, a posture vocabulary is generated by performing k-means clustering on a large set of unlabeled postures. Finally, body part postures are combined into body posture sequences and Hidden Conditional Random Fields (HCRF) classifier is used to recognize motion patterns (e.g. dance figures). For the evaluation of the proposed method, Tsamiko dancers are captured using multiple Kinect sensors and experimental results are presented to demonstrate the high recognition accuracy of the proposed method.

Journal on Educational Technology, 2018

The safeguarding of the Intangible Cultural Heritage (ICH) has acquired a growing relevance in th... more The safeguarding of the Intangible Cultural Heritage (ICH) has acquired a growing relevance in the last decades and in particular after the promulgation of the “Convention for the Safeguarding of the Intangible Cultural Heritage” (UNESCO, 2003). The convention itself highlights the importance of education to this scope and, for this reason, some projects have been proposed at national and international level. Nevertheless, ICH education remains a scarcely explored topic and poses several challenges, due to the intrinsic characteristics of the cultural expressions and the traditional transmission methods. Considering these challenges, we developed our research question related to which technologies and methods could be effectively adopted in the field of Cultural Heritage, to open up new opportunities for teaching and learning. In this paper, we discuss both the main challenges to be faced in the specific field and the results of our research carried out in the framework of the i-Tre...

Alexandros Savvaidis (1), Nektarios Chrysoulakis (2), Nikolaos Grammalidis (3), Evangelos Lagios ... more Alexandros Savvaidis (1), Nektarios Chrysoulakis (2), Nikolaos Grammalidis (3), Evangelos Lagios (4), Vassilios Lykousis (5), Ioannis Manakos (3), Konstantinos Nikolakopoulos (6), Panagiotis Papadimitriou (7), Christos Papaioannou (1), George Papatheodorou (8), Constantinos Papazachos (9), Demitris Paradisis (10), Isaak Parcharidis (11), Christos Pikridas (12), Dimitris Sakellariou (5), and Apostolos Sarris (2)

2014 International Conference on Computer Vision Theory and Applications (VISAPP), 2014

Cultural expression is not limited to architecture, monuments or collections of artifacts. It als... more Cultural expression is not limited to architecture, monuments or collections of artifacts. It also includes fragile intangible live expressions, which involve knowledge and skills such as music, dance, singing, theatre, human skills and craftsmanship. These manifestations of human intelligence and creativeness constitute our Intangible Cultural Heritage (ICH), a basic factor of local cultural identity and a guaranty for sustainable development. In this paper, we briefly introduce the i-Treasures research project, which aims at developing an open and extendable platform to provide access to ICH resources, enable knowledge exchange and contribute to the transmission of rare know-how. The project goes beyond digitization of cultural content; it creates new knowledge that has never been analysed or studied before through novel methodologies for the analysis and modelling of ICH based on multisensory technology. High-level semantics are extracted enabling researchers to identify possible...

Remote Sensing, 2020

The environmental challenges the world faces have never been greater or more complex. Global area... more The environmental challenges the world faces have never been greater or more complex. Global areas that are covered by forests and urban woodlands are threatened by large-scale forest fires that have increased dramatically during the last decades in Europe and worldwide, in terms of both frequency and magnitude. To this end, rapid advances in remote sensing systems including ground-based, unmanned aerial vehicle-based and satellite-based systems have been adopted for effective forest fire surveillance. In this paper, the recently introduced 360-degree sensor cameras are proposed for early fire detection, making it possible to obtain unlimited field of view captures which reduce the number of required sensors and the computational cost and make the systems more efficient. More specifically, once optical 360-degree raw data are obtained using an RGB 360-degree camera mounted on an unmanned aerial vehicle, we convert the equirectangular projection format images to stereographic images....

Proceedings of the 10th International Conference on Computer Vision Theory and Applications, 2015

An important cue that can assist towards an accurate building detection and segmentation is 3D in... more An important cue that can assist towards an accurate building detection and segmentation is 3D information. Because of their height, buildings can easily be distinguished from the ground and small objects, allowing for their successful segmentation. Unfortunately, 3D knowledge is not always available, but there are ways to infer 3D information from 2D images. Shape-from-shading techniques extract height and surface normal information from a single 2D image by taking into consideration knowledge about illumination, reflectance and shape. In this paper, a novel feature is proposed that can describe the 3D information of reconstructed images based on a shape-from-shading technique in order to successfully acquire building boundaries. The results are promising and show that such a 3D feature can significantly assist in a correct building boundary detection and segmentation.

Proceedings of the 10th International Conference on Computer Vision Theory and Applications, 2015

The main objective of the EU FP7 ICT i-Treasures project is to build a public and expandable plat... more The main objective of the EU FP7 ICT i-Treasures project is to build a public and expandable platform to enable learning and transmission of rare know-how of intangible cultural heritage. A core part of this platform consists of game-like applications able to support teaching and learning processes in the ICH field. We have designed and developed four game-like applications (for Human Beat Box singing, Tsamiko dancing, pottery making and contemporary music composition), each corresponding to one of the ICH use cases of i-Treasures project. A first preliminary version of these applications is currently available for further validation, evaluation and demonstration within the project. We have encountered a number of issues, most of which derive from the peculiarities of the ICH domains addressed by the project, and many have already been resolved/ The evaluation results are expected to lead to further optimization of these games. .

Computer Networks, 2020

This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Biomedical Signal Processing and Control, 2019

Background and Objective: Electrocardiogram is commonly used as a diagnostic tool for the monitor... more Background and Objective: Electrocardiogram is commonly used as a diagnostic tool for the monitoring of cardiac health and the detection of possible heart diseases. However, the procedure followed for the diagnosis of heart abnormalities is time consuming and prone to human errors. Thus, the development of computer-aided techniques for the automatic analysis of electrocardiogram signals is of vital importance for the diagnosis and prevention of heart diseases. The most serious outcome of coronary heart disease is the myocardial infarction, i.e. the rapid and irreversible damage of cardiac muscles, which, if not diagnosed and treated in time, continues to damage further the myocardial structure and function. In this paper we propose a novel approach for the automatic detection and localization of myocardial infarction from multi-lead electrocardiogram signals. Methods: The proposed method initially reshapes the multidimensional signal into a thirdorder tensor structure and subsequently extracts feature representations in both Euclidean and Grassmannian space. In addition, two different methods are proposed for the mapping of the two different feature representations into a common Hilbert space before the final classification of signals. The first approach is based on the mapping of both Grassmannian and Euclidean features in a Reproducing Kernel Hilbert Space (RKHS), while the second one attempts to initially apply Vector of Locally Aggregated Descriptors (VLAD) encoding directly to Grassmann manifold and then concatenate the two VLAD representations. Results: For the evaluation of the proposed method, we have conducted extensive tests using a publicly available dataset, namely PTB Diagnostic ECG database, containing 549 multi-lead ECG data recordings from 290 subjects and from different diagnostic classes. The method provides an excellent detection rate of 100%, and localization rate, i.e., 100% with the first fusion method and 99.7% with the second one. Conclusions: The Experimental results presented in this paper show the superiority of the proposed methodology against a number of state-of-the-art approaches. The main advantage of the proposed approach is that it exploits better the intercorrelations between signals of different ECG leads, by extracting feature representations that lie in different geometrical spaces and contain complementary information with regard to the dynamics of signals.