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 15
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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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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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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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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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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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Comparative Analysis of Artificial Hydrocarbon Networks and Data-Driven Approaches for Human Activity Recognition

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

In recent years computing and sensing technologies advances contribute to develop effective human activity recognition systems. In context-aware and ambient assistive living applications, classification of body postures and movements, aids in the development of health systems that improve the quality of life of the disabled and the elderly. In this paper we describe a comparative analysis of data-driven activity recognition techniques against a novel supervised learning technique called artificial hydrocarbon networks (AHN). We prove that artificial hydrocarbon networks are suitable for efficient body postures and movements classification, providing a comparison between its performance and other well-known supervised learning methods.

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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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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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Versatility of Artificial Hydrocarbon Networks for Supervised Learning

2018 , Ponce, Hiram , Martinez-Villaseñor, Lourdes

Surveys on supervised machine show that each technique has strengths and weaknesses that make each of them more suitable for a particular domain or learning task. No technique is capable to tackle every supervised learning task, and it is difficult to comply with all possible desirable features of each particular domain. However, it is important that a new technique comply with the most requirements and desirable features of as many domains and learning tasks as possible. In this paper, we presented artificial hydrocarbon networks (AHN) as versatile and efficient supervised learning method. We determined the ability of AHN to solve different problem domains, with different data-sources and to learn different tasks. The analysis considered six applications in which AHN was successfully applied. © Springer Nature Switzerland AG 2018.

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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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