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
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Preface : Advances in Computational Intelligence : 22nd Mexican International Conference on Artificial Intelligence, MICAI 2023, Yucatán, Mexico, November 13–18, 2023, Proceedings, Part I

2024-01-01 , Calvo, Hiram , Martinez-Villaseñor, Lourdes , Ponce, Hiram , Zatarain-Cabada, Ramón , Montes Rivera, Martín , Mezura-Montes, Efrén

Conference proceedings front matter may contain various advertisements, welcome messages, committee or program information, and other miscellaneous conference information. This may in some cases also include the cover art, table of contents, copyright statements, title-page or half title-pages, blank pages, venue maps or other general information relating to the conference that was part of the original conference proceedings.

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Open Source Implementation for Fall Classification and Fall Detection Systems

2020 , Ponce, Hiram , Martinez-Villaseñor, Lourdes , Nuñez Martínez, José Pablo , Moya-Albor, Ernesto , Brieva, Jorge

Distributed social coding has created many benefits for software developers. Open source code and publicly available datasets can leverage the development of fall detection and fall classification systems. These systems can help to improve the time in which a person receives help after a fall occurs. Many of the simulated falls datasets consider different types of fall however, very few fall detection systems actually identify and discriminate between each category of falls. In this chapter, we present an open source implementation for fall classification and detection systems using the public UP-Fall Detection dataset. This implementation comprises a set of open codes stored in a GitHub repository for full access and provides a tutorial for using the codes and a concise example for their application. © 2020, Springer Nature Switzerland AG.

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Human Activity Recognition on Mobile Devices Using Artificial Hydrocarbon Networks

2018 , Ponce, Hiram , Miralles-Pechuán, Luis , González Mora, José Guillermo , Martinez-Villaseñor, Lourdes

Human activity recognition (HAR) aims to classify and identify activities based on data-driven from different devices, such as sensors or cameras. Particularly, mobile devices have been used for this recognition task. However, versatility of users, location of smartphones, battery, processing and storage limitations, among other issues have been identified. In that sense, this paper presents a human activity recognition system based on artificial hydrocarbon networks. This technique have been proved to be very effective on HAR systems using wearable sensors, so the present work proposes to use this learning method with the information provided by the in-sensors of mobile devices. Preliminary results proved that artificial hydrocarbon networks might be used as an alternative for human activity recognition on mobile devices. In addition, a real dataset created for this work has been published. © Springer Nature Switzerland AG 2018.

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Preface : Advances in Soft Computing : 22nd Mexican International Conference on Artificial Intelligence, MICAI 2023, Yucatán, Mexico, November 13–18, 2023, Proceedings, Part II

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

The Mexican International Conference on Artificial Intelligence (MICAI) is a yearly international conference series that has been organized by the Mexican Society for 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. This year, MICAI 2023 was graciously hosted by the Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS) and the Universidad Autónoma del Estado de Yucatán (UAEY). The conference presented a cornucopia of scientific endeavors. ©Springer.

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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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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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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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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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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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Modeling and Control Balance Design for a New Bio-inspired Four-Legged Robot

2019 , Ponce, Hiram , Acevedo, Mario , Morales, Elizabeth , Martinez-Villaseñor, Lourdes , Mayorga-Acosta, Carlos , Díaz Ramos, Gabriel

Bio-inspired robots have chosen to propose novel developments aiming to inhabit and interact complex and dynamic environments. Bio-inspired four-legged robots, typically inspired on animal locomotion, provide advantages on mobility, obstacle avoidance, energy efficiency and others. Balancing is a major challenge when legged robots require to move over uncertain and sharp terrains. It becomes of particular importance to solve other locomotion tasks such as walking, running or jumping. In this paper, we present a preliminary study on the modeling and control balance design of a bio-inspired four-legged robot for standing on its aligned legs in a straight line. The proposed robot is loosely inspired on the bio-mechanics of the chameleon. Thus, a mathematical modeling, simulation, intelligent control strategy, prototype implementation and preliminary results of control balance in our robot are presented and discussed. © Springer Nature Switzerland AG 2019.

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