Martinez-Villaseñor, Lourdes
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Preferred name
Martinez-Villaseñor, Lourdes
Official Name
Martínez Villaseñor, María de Lourdes
Alternative Name
lmartine
Main Affiliation
ORCID
Scopus Author ID
55521085100
Researcher ID
N-7607-2018

Research Output
Now showing 1 - 10 of 23
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Application of Convolutional Neural Networks for Fall Detection Using Multiple Cameras

2020 , Espinosa Loera, Ricardo Abel , Ponce, Hiram , Gutiérrez, Sebastián , Martinez-Villaseñor, Lourdes , Moya-Albor, Ernesto , Brieva, Jorge

Currently one of the most important research issue for artificial intelligence and computer vision tasks is the recognition of human falls. Due to the current exponential increase in the use of cameras is it common to use vision-based approach for fall detection and classification systems. On another hand deep learning algorithms have transformed the way that we see vision-based problems. The Convolutional Neural Network (CNN) as deep learning technique offers more reliable and robust solutions on detection and classification problems. Focusing only on a vision-based approach, for this work we used images from a new public multimodal data set for fall detection (UP-Fall Detection dataset) published by our research team. In this chapter we present fall detection system using a 2D CNN analyzing multiple camera information. This method analyzes images in fixed time window frames extracting features using an optical flow method that obtains information of relative motion between two consecutive images. For experimental results, we tested this approach in UP-Fall Detection dataset. Results showed that our proposed multi-vision-based approach detects human falls achieving 95.64% in accuracy with a simple CNN network architecture compared with other state-of-the-art methods.

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Interoperability in Electronic Health Records Through the Mediation of Ubiquitous User Model

2016 , Martinez-Villaseñor, Lourdes , Miralles-Pechuán, Luis , González-Mendoza, Miguel

Martínez Villaseñor, M. de L., Miralles Pechuan, L. J. y González Mendoza, M. (2016). Interoperability in electronic health records through the mediation of ubiquitous user model. En: En: García, C, Caballero Gil, P., Burmester, M. y Quesada Arencibia, A. (editores), Ubiquitous Computing and Ambient Intelligence : 10th International Conference, UCAmI 2016, San Bartolomé de Tirajana, Gran Canaria, Spain, November 29 - December 2, 2016 (vol. 1), (pp. 190-200). Cham : Springer International Publishing. DOI: 10.1007/978-3-319-48746-5_19

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Towards Constant Calculation in Disjunctive Inequalities Using Wound Treatment Optimization

2019 , Ponce, Hiram , Marmolejo Saucedo, José Antonio , Martinez-Villaseñor, Lourdes

When using the mixed-integer programming to model situations where the limit of the variables follows a box constraint, we find nonlinear problems. To solve this, linearization techniques of these disjunctive inequality constraints are typically used, including constants associated to the variable bounds called M-constants or big-M. Calculation of these constants is an open problem since their values affect the reliability of the optimal solution and convergence of the optimization algorithm. To solve this problem, this work proposes a new population-based metaheuristic optimization method, namely wound treatment optimization (WTO) for calculating the M-constant in a typical domain known as the fixed-charge transportation problem. WTO is inspired on the social wound treatment present in ants after raids. This method allows population diversity that allows to find near-optimal solutions. Experiments of the WTO method on the fixed-charge transportation problem validated its performance and efficiency to find tighten solutions of the M-constant that minimizes the objective function of the problem. © Springer Nature Switzerland AG 2019.

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Ethical Design Framework for Artificial Intelligence Healthcare Technologies

2024-01-01 , Martinez-Villaseñor, Lourdes , Ponce, Hiram

The healthcare industry has undergone a profound transformation with the integration of artificial intelligence(AI) and emerging technologies, leading in a new era of personalized treatments and healthcare solutions. However, this technological advancement has not been without its ethical and practical challenges, which have hindered the real-world application of intelligent systems in clinical settings. Numerous international entities have published principles, guidelines, and regulations to tackle these issues, yet a significant gap persists between theoretical initiatives and the practical incorporation of ethical design in intelligent systems. Within this study, we delineate the substantial transformation taking place in the healthcare landscape due to artificial intelligence. We provide a condensed overview of the opportunities and challenges that accompany this disruptive shift. The main goal of this work is introducing an ethical design framework tailored to healthcare technologies and delineating the ethical design process for a machine learning-based support tool designed for Age-related Macular Degeneration risk assessment. To the best of our knowledge, this work represents one of the few documented instances of the practical implementation of ethical design principles in intelligent healthcare systems. © 2024 Springer Nature

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Advances in soft computing : 18th Mexican International Conference on Artificial Intelligence, MICAI 2019, Xalapa, Mexico, October 27 - November 2, 2019, Proceedings : Preface

2019 , Martinez-Villaseñor, Lourdes

The Mexican International Conference on Artificial Intelligence (MICAI) is a yearly international conference series that has been organized by the Mexican Society of Artificial Intelligence (SMIA) since 2000. MICAI is a major international artificial intelligence (AI) forum and the main event in the academic life of the country’s growing AI community. The proceedings of MICAI 2019 contains 59 papers structured into four sections: Machine Learning, Fuzzy Systems, Reasoning, and Intelligent Applications, Computer Vision and Robotics, Optimization and Planning This book should be of interest to researchers in all fields of AI, students specializing in related topics, and for the public in general interested in recent developments in AI. ©Springer Nature Switzerland AG 2019.

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A Survey on Freezing of Gait Detection and Prediction in Parkinson’s Disease

2020 , Martinez-Villaseñor, Lourdes , Ponce, Hiram , Miralles-Pechuán, Luis

Most of Parkinson’s disease (PD) patients present a set of motor and non-motor symptoms and behaviors that vary during the day and from day-to-day. In particular, freezing of gait (FOG) impairs their quality of life and increases the risk of falling. Smart technology like mobile communication and wearable sensors can be used for detection and prediction of FOG, increasing the understanding of the complex PD. There are surveys reviewing works on Parkinson and/or technologies used to manage this disease. In this review, we summarize and analyze works addressing FOG detection and prediction based on wearable sensors, vision and other devices. We aim to identify trends, challenges and opportunities in the development of FOG detection and prediction systems. © 2020, Springer Nature Switzerland AG.

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Approaching Fall Classification Using the UP-Fall Detection Dataset: Analysis and Results from an International Competition

2020 , Ponce, Hiram , Martinez-Villaseñor, Lourdes

This chapter presents the results of the Challenge UP – Multimodal Fall Detection competition that was held during the 2019 International Joint Conference on Neural Networks (IJCNN 2019). This competition lies on the fall classification problem, and it aims to classify eleven human activities (i.e. five types of falls and six simple daily activities) using the joint information from different wearables, ambient sensors and video recordings, stored in a given dataset. After five months of competition, three winners and one honorific mention were awarded during the conference event. The machine learning model from the first place scored$$82.47\%$$ in$$F:1$$-score, outperforming the baseline of$$70.44\%$$. After analyzing the implementations from the participants, we summarized the insights and trends of fall classification. © 2020, Springer Nature Switzerland AG.

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Artificial Hydrocarbon Networks for Online Sales Prediction

2015 , Ponce, Hiram , Miralles-Pechuán, Luis , Martinez-Villaseñor, Lourdes

Online retail sales have been growing worldwide in the last decade. In order to cope with this high dynamicity and market share competition, online retail sales prediction and online advertising have become very important to answer questions of pricing decisions, advertising responsiveness, and product demand. To make adequate investment in products and channels it is necessary to have a model that relates certain features of the product with the number of sales that will occur in the future. In this paper we describe a comparative analysis of machine learning techniques against a novel supervised learning technique called artificial hydrocarbon networks (AHN). This method is a new type of machine learning that have proved to adapt very well to a wide spectrum of problems of regression and classification. Thus, we use artificial hydrocarbon networks for predicting the number of online sales, and then we compare their performance with other ten well-known methods of machine learning regression, obtaining promising results.

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Process of Concept Alignment for Interoperability between Heterogeneous Sources

2013 , Martinez-Villaseñor, Lourdes , González-Mendoza, Miguel

Some researchers in the community of user modeling envision the need to share and reuse information scattered over different user models of heterogeneous sources. In a multi-application environment each application and service must repeat the effort of building a user model to obtain just a narrow understanding of the user. Sharing and reusing information between models can prevent the user from repeated configurations, help deal with application and services’ “cold start” problem, and provide enrichment to user models to obtain a better understanding of the user. But gathering distributed user information from heterogeneous sources to achieve user models interoperability implies handling syntactic and semantic heterogeneity. In this paper, we present a process of concept alignment to automatically determine semantic mapping relations that enable the interoperability between heterogeneous profile suppliers and consumers, given the mediation of a central ubiquitous user model. We show that the process of concept alignment for interoperability based in a two-tier matching strategy can allow the interoperability between social networking applications, FOAF, Personal Health Records (PHR) and personal devices.

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Mass Segmentation in Digital Mammograms

2015 , Carreras Cruz, María Victoria , Martinez-Villaseñor, Lourdes , Rosas-Pérez, Kevin Nataniel

Digital mammograms are among the most difficult medical images to read, because of the differences in the types of tissues and their low contrasts. This paper proposes a computer aided diagnostic system for mammographic mass detection that can distinguish between tumorous and healthy tissue among various parenchymal tissue patterns. This method consists in extraction of regions of interest, noise elimination, global contrast improvement, combined segmentation, and rule-based classification. The evaluation of the proposed methodology is carried out on Mammography Image Analysis Society (MIAS) dataset. The achieved results increased the detection accuracy of the lesions and reduced the number of false diagnoses of mammograms.

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