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

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UP-fall detection dataset : a multimodal approach

2019 , Martinez-Villaseñor, Lourdes , Ponce, Hiram , Brieva, Jorge , Moya-Albor, Ernesto , Nuñez-Martínez, José , Peñafort Asturiano, Carlos J.

Falls, especially in elderly persons, are an important health problem worldwide. Reliable fall detection systems can mitigate negative consequences of falls. Among the important challenges and issues reported in literature is the difficulty of fair comparison between fall detection systems and machine learning techniques for detection. In this paper, we present UP-Fall Detection Dataset. The dataset comprises raw and feature sets retrieved from 17 healthy young individuals without any impairment that performed 11 activities and falls, with three attempts each. The dataset also summarizes more than 850 GB of information from wearable sensors, ambient sensors and vision devices. Two experimental use cases were shown. The aim of our dataset is to help human activity recognition and machine learning research communities to fairly compare their fall detection solutions. It also provides many experimental possibilities for the signal recognition, vision, and machine learning community. ©2019 NLM (Medline).

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Challenges in Data Acquisition Systems: Lessons Learned from Fall Detection to Nanosensors

2018 , Peñafort Asturiano, Carlos J. , Santiago, Nestor , Nuñez-Martínez, José , Ponce, Hiram , Martinez-Villaseñor, Lourdes

Falls are a major public health problem in elderly people often causing fatal injuries. It is important to assure that injured people receive assistance as quick as possible. Fall detection systems have gain more relevance nowadays. As more databases and fall detection systems are developed, there is more need to identify the challenges encountered in building and creating them. This paper addresses pre-processing, inconsistency and synchronization challenges that occur when creating a multimodal database for fall detection. We present different algorithms used to tackle these issues. We describe the issues and the corresponding solutions in order to document the lessons learned that could help others in data acquisition for multimodal databases. Applying the solutions to the issues found so far, we acquired an organized multimodal database for fall detection with 17 subjects. Furthermore, these lessons learned can be applied for data nanosensors acquisition and storage. © 2018 IEEE.

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Online Testing in Machine Learning Approach for Fall Detection

2020 , Martinez-Villaseñor, Lourdes , Ponce, Hiram , Nuñez-Martínez, José , Pacheco, Sofia

Robust fall detectors are needed to reduce the time in which a person can receive medical assistance, and mitigate negative effects when a fall occurs. Robustness in fall detection systems is difficult to achieve given that there are still many challenges regarding performance in real conditions. Fall detection systems based on smartphones present good results following a traditional methodology of collecting data, training and evaluating classification models using the same sensors and subjects, yet fail to experiment and succeed in different realistic conditions. In this paper, we propose a methodology to build a solution for fall detection, and online testing changing the sensors and subjects of evaluation in order to provide a more flexible and portable fall detector. © 2020 IEEE.

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