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Martinez-Villaseñor, Lourdes
Challenges in Data Acquisition Systems: Lessons Learned from Fall Detection to Nanosensors
Online Testing in Machine Learning Approach for Fall Detection
2018
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Peñafort Asturiano, Carlos J.
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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.
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.