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

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

doi:10.1109/NANOFIM.2018.8688609

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

Journal

2018 Nanotechnology for Instrumentation and Measurement (NANOfIM)

Date Issued

2018

Author(s)

Peñafort Asturiano, Carlos J.

Santiago, Nestor

Nuñez-Martínez, José

Type

Resource Types::text::conference output::conference proceedings::conference paper

DOI

10.1109/NANOFIM.2018.8688609

URL

https://scripta.up.edu.mx/handle/20.500.12552/4145

Abstract

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.