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19 Papers

Fusion of Machine Learning and Threshold-Based Approaches for Fall Detection in Healthcare Using Inertial Sensors

Topics: Medical Signal Acquisition, Analysis and Processing; Motion and Activity Analysis; Biometrics, Detection and Identification; Monitoring and Telemetry, IOT, Wearable Sensors and Systems

Authors: Ya Wang 1 , Peiman Sarvari 2 and Djamel Khadraoui 2

Affiliations: 1 Faculty of Science, Technology and Medicine, University of Luxembourg, Esch sur Alzette, Luxembourg , 2 IT for Innovative Services, Luxembourg University of Science and Technology, Esch sur Alzette, Luxembourg

Digital Object Identifier: 10.5220/0012250500003657

Proceedings of BIOSIGNALS 2024, ISBN: 978-989-758-688-0

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In the healthcare sector, specifically for elderly care, accurate and efficient fall detection is crucial. We present an advanced fall detection methodology tailored for wearable systems. Our approach blends threshold-based screening with machine learning models like Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, and XGBoost. Utilizing 65 features extracted from the gyroscope and accelerometer data from Inertial Measurement Units, our method addresses the class imbalance often found between Activities of Daily Living and actual fall events. Threshold-based pre-screening serves to mitigate the class imbalance of the fall dataset, making the subsequent machine-learning classification more effective. Validation on two open-source IMU datasets, Sisfall and FallAllD, achieving high accuracy rates of 99.55%, 99.68% (wrist), 99.76% (waist), and 99.52% (neck), shows our model surpassing existing solutions in detection accuracy. Furthermore, our strategic feature extraction not only enhances the model’s performance but also allows for a fourfold reduction by using the 15 most important features in data transmission without sacrificing accuracy. These findings underscore the efficiency and potential of our methodology, indicating that wearables can indeed be powerful tools for high-precision fall detection with minimal data overhead.

Astronomical Images Quality Assessment with Automated Machine Learning

Topics: Information Quality; Deep Learning

Authors: Olivier Parisot , Pierrick Bruneau and Patrik Hitzelberger

Affiliation: Luxembourg Institute of Science and Technology (LIST), 5 Avenue des Hauts-Fourneaux, 4362 Esch-sur-Alzette, Luxembourg

Digital Object Identifier: 10.5220/0012073800003541

Proceedings of DATA 2023, ISBN: 978-989-758-664-4

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Electronically Assisted Astronomy consists in capturing deep sky images with a digital camera coupled to a telescope to display views of celestial objects that would have been invisible through direct observation. This practice generates a large quantity of data, which may then be enhanced with dedicated image editing software after observation sessions. In this study, we show how Image Quality Assessment can be useful for automatically rating astronomical images, and we also develop a dedicated model by using Automated Machine Learning.

Applying Genetic Algorithm and Image Quality Assessment for Reproducible Processing of Low-light Images

Topics: Image Restoration; Computer Vision Algorithms

Authors: Olivier Parisot and Thomas Tamisier

Affiliation: Luxembourg Institute of Science and Technology (LIST), 5 Avenue des Hauts-Fourneaux, 4362 Esch-sur-Alzette, Luxembourg

Digital Object Identifier: 10.5220/0011082400003209

Proceedings of IMPROVE 2022, ISBN: 978-989-758-563-0

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Reproducible images preprocessing is fundamental in computer vision, whether to fairly compare process algorithms or to prepare new images corpus. In this paper, we propose an approach based on genetic algorithm combined to Image Quality Assessment methods to obtain a reproducible sequence of transformations for improving low-light images. Preliminary tests have been performed on state-of-the-art benchmarks.

Automated Machine Learning for Wind Farms Location

Topics: Advanced Learning Methods; Industry Related Applications; Machine Learning Methods; Classification and Clustering

Authors: Olivier Parisot and Thomas Tamisier

Affiliation: Luxembourg Institute of Science and Technology (LIST), Luxembourg

Digital Object Identifier: 10.5220/0010232102220227

Proceedings of ICPRAM 2021, ISBN: 978-989-758-486-2

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Automated Machine Learning aims at preparing effective Machine Learning models with little or no data science expertise. Tedious tasks like preprocessing, algorithm selection and hyper-parameters optimization are then automatized: end-users just have to apply and deploy the model that best suits the real world problem. In this paper, we experiment Automated Machine Learning to leverage open data sources for predicting potential next wind farms location in Luxembourg, France, Belgium and Germany.

A Novel 2.5D Shadow Calculation Algorithm for Urban Environment

Topics: Computational Geometry; Spatio-Temporal Data Acquisition; Spatial Modeling and Reasoning; Geospatial Information and Technologies; Geocomputation; Urban and Regional Planning; Spatial Information Management

Authors: Sukriti Bhattacharya , Christian Braun and Ulrich Leopold

Affiliation: Department for Environmental Research and Innovation (ERIN), Luxembourg Luxembourg Institute of Science and Technology (LIST) and Luxembourg

Digital Object Identifier: 10.5220/0007748602740281

Proceedings of GISTAM 2019, ISBN: 978-989-758-371-1

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This paper proposes a novel efficient algorithm to calculate a 2.5D shadow map based on a coherent mathematic formula concerning the sun’s position in a specific location, date and time. This work attempts to improve the understanding of the underlying equations and data structures from an analytical, a geometric and a dynamical systems perspective. By using scalable tensor data structure and inherent parallelism offered by data-flow based implementation the proof of concept is developed to test the technical feasibility of the proposed algorithm. Results show noticeable and significant improvements in overall performance keeping accuracy at negligible differences.
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Computational Geometry

Computer Vision, Visualization and Computer Graphics

Image Formation and Preprocessing

Storing and Processing Personal Narratives in the Context of Cultural Legacy Preservation

Topics: Data Modeling and Visualization; Data Structures and Data Management Algorithms; Nosql Databases

Authors: Pierrick Bruneau , Olivier Parisot and Thomas Tamisier

Affiliation: Luxembourg Institute of Science and Technology, Luxembourg

Digital Object Identifier: 10.5220/0006422800480055

Proceedings of DATA 2017, ISBN: 978-989-758-255-4

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An important, yet underestimated, aspect of cultural heritage preservation is the analysis of personal narratives told by citizens. In this paper, we present a data model and implementation towards facilitating narratives storage and sharing. The proposed solution aims at collecting textual narratives in raw form, processing them to extract and store structured content, and then exposing results through a RESTful interface. We apply it to a corpus related to the time of the European construction in Luxembourg. We disclose details about our conceptual model and implementation, as well as evidence supporting the interest of our approach.
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Data Engineering

Data Modeling and Visualization

Databases and Data Security

Data Management and Quality

Data Structures and Data Management Algorithms

Nosql Databases

Subspace Clustering and Visualization of Data Streams

Topics: Visual Data Analysis and Knowledge Discovery; Visual Representation and Interaction; Interactive Visual Interfaces for Visualization; Interface and Interaction Techniques for Visualization; Information and Scientific Visualization

Authors: Ibrahim Louhi 1 , Lydia Boudjeloud-Assala 2 and Thomas Tamisier 3

Affiliations: 1 Université de Lorraine and Luxembourg Institute of Science and Technology, France , 2 Université de Lorraine, France , 3 Luxembourg Institute of Science and Technology, Luxembourg

Digital Object Identifier: 10.5220/0006169702590265

Proceedings of IVAPP 2017/VISIGRAPP, ISBN: 978-989-758-228-8

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In this paper, we propose a visual subspace clustering approach for data streams, allowing the user to visually track data stream behavior. Instead of detecting elements changes, the approach shows visually the variables impact on the stream evolution, by visualizing the subspace clustering at different levels in real time. First we apply a clustering on the variables set to obtain subspaces, each subspace consists of homogenous variables subset. Then we cluster the elements within each subspace. The visualization helps to show the approach originality and its usefulness in data streams processing.
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Abstract Data Visualization

Information and Scientific Visualization

Visual Data Analysis and Knowledge Discovery

Computer Vision, Visualization and Computer Graphics

Interactive Visual Interfaces for Visualization

Visual Representation and Interaction
General Data Visualization

Interface and Interaction Techniques for Visualization

Using Visualisation Techniques to Acquire a Better Understanding of Storytelling for Cultural Heritage

Topics: Data Analytics; Information and Scientific Visualization; Data Science

Authors: Paulo Carvalho , Olivier Parisot and Thomas Tamisier

Affiliation: Luxembourg Institute of Science and Technology, Luxembourg

Digital Object Identifier: 10.5220/0006415801570164

Proceedings of DATA 2017, ISBN: 978-989-758-255-4

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Historical information has an important role regarding cultural heritage. It is used to interpret facts occurred in the past and also to understand the present. Storytelling, when applied in the narrative of true events and resulting from different personal views and anecdotal stories, act as an important source of historical information. In this paper, we discuss the problems we encounter in the field of historical information storytelling and we present a software architecture to facilitate the comprehension of stories. More precisely, the proposed solution helps to analyse a story, examine its composition identifying existing entity classes and computing possible relations with other stories, to finally build a visual representation of these stories.
Collapse Button Related Ontology Subjects/Areas/Topics (10)
Artificial Intelligence

Data Engineering

Knowledge Discovery and Information Retrieval

Business Analytics

General Data Visualization

Knowledge-Based Systems

Computer Vision, Visualization and Computer Graphics

Information and Scientific Visualization

Symbolic Systems
Data Analytics

A Visual Technique to Assess the Quality of Datasets - Understanding the Structure and Detecting Errors and Missing Values in Open Data CSV Files

Topics: Information Quality; Data Modeling and Visualization; Data Structures and Data Management Algorithms; Open Data

Authors: Paulo Da Silva Carvalho 1 , Patrik Hitzelberger 1 , Fatma Bouali 2 and Gilles Venturini 2

Affiliations: 1 Luxembourg Institute of Science and Technology, Luxembourg , 2 University François Rabelais of Tours, France

Digital Object Identifier: 10.5220/0005496601340141

Proceedings of DATA 2015, ISBN: 978-989-758-103-8

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Nowadays, more and more information is flowing in and is provided on the Web. Large datasets are made available covering many fields and sectors. Open Data (OD) plays an important role in this field. Thanks to the volumes and the variety of the released datasets, OD brings high societal and business potential. In order to realize this potential, the reuse of the datasets (e.g. in internal business processes) becomes primordial. However, if the aim is to reuse OD, it is also necessary to be able of assessing its quality. This paper demonstrates how Information Visualization may help on this task and presents Stacktab chart - a new chart to analyse and assess CSV files in order to understand their structure, identify the location of relevant information and detect possible problems in the datasets.
Collapse Button Related Ontology Subjects/Areas/Topics (6)
Data Engineering

Data Modeling and Visualization

Information Quality

Data Management and Quality

Data Structures and Data Management Algorithms

Open Data

Preserving Prediction Accuracy on Incomplete Data Streams

Topics: Datamining; Data Analytics; Data Curation; Predictive Modeling

Authors: Olivier Parisot , Yoanne Didry , Thomas Tamisier and Benoît Otjacques

Affiliation: Luxembourg Institute of Science and Technology (LIST), Luxembourg

Digital Object Identifier: 10.5220/0005553500910096

Proceedings of DATA 2015, ISBN: 978-989-758-103-8

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Model tree is a useful and convenient method for predictive analytics in data streams, combining the interpretability of decision trees with the efficiency of multiple linear regressions. However, missing values within the data streams is a crucial issue in many real world applications. Often, this issue is solved by pre-processing techniques applied prior to the training phase of the model. In this article we propose a new method that proceeds by estimating and adjusting missing values before the model tree creation. A prototype has been developed and experimental results on several benchmarks show that the method improves the accuracy of the resulting model tree.
Collapse Button Related Ontology Subjects/Areas/Topics (19)
Artificial Intelligence

Data Mining

Knowledge-Based Systems

Biomedical Engineering

Databases and Information Systems Integration

Predictive Modeling

Business Analytics

Datamining

Sensor Networks

Data Analytics

Enterprise Information Systems

Signal Processing

Data Curation

Health Information Systems

Soft Computing

Data Engineering

Knowledge Discovery and Information Retrieval

Symbolic Systems
Data Management and Quality

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