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AnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks

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Architecture and Computer Technology Department, ETSII-EPS, University of Seville, 41004 Sevilla, Spain
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Robotics and Technology of Computers Laboratory, University of Seville, 41004 Sevilla, Spain
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Research Institute of Computer Engineering (I3US), University of Seville, 41004 Sevilla, Spain
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Author to whom correspondence should be addressed.
Academic Editors: Ki H. Chon and Marco Iosa
Sensors 2021, 21(5), 1889; https://doi.org/10.3390/s21051889
Received: 25 January 2021 / Revised: 26 February 2021 / Accepted: 5 March 2021 / Published: 8 March 2021
(This article belongs to the Topic Scientific Advances in STEM: From Professor to Students)
Falls are one of the leading causes of permanent injury and/or disability among the elderly. When these people live alone, it is convenient that a caregiver or family member visits them periodically. However, these visits do not prevent falls when the elderly person is alone. Furthermore, in exceptional circumstances, such as a pandemic, we must avoid unnecessary mobility. This is why remote monitoring systems are currently on the rise, and several commercial solutions can be found. However, current solutions use devices attached to the waist or wrist, causing discomfort in the people who wear them. The users also tend to forget to wear the devices carried in these positions. Therefore, in order to prevent these problems, the main objective of this work is designing and recollecting a new dataset about falls, falling risks and activities of daily living using an ankle-placed device obtaining a good balance between the different activity types. This dataset will be a useful tool for researchers who want to integrate the fall detector in the footwear. Thus, in this work we design the fall-detection device, study the suitable activities to be collected, collect the dataset from 21 users performing the studied activities and evaluate the quality of the collected dataset. As an additional and secondary study, we implement a simple Deep Learning classifier based on this data to prove the system’s feasibility. View Full-Text
Keywords: accelerometer; deep learning; embedded system; fall detection; wearable; recurrent neural networks accelerometer; deep learning; embedded system; fall detection; wearable; recurrent neural networks
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MDPI and ACS Style

Luna-Perejón, F.; Muñoz-Saavedra, L.; Civit-Masot, J.; Civit, A.; Domínguez-Morales, M. AnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks. Sensors 2021, 21, 1889. https://doi.org/10.3390/s21051889

AMA Style

Luna-Perejón F, Muñoz-Saavedra L, Civit-Masot J, Civit A, Domínguez-Morales M. AnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks. Sensors. 2021; 21(5):1889. https://doi.org/10.3390/s21051889

Chicago/Turabian Style

Luna-Perejón, Francisco, Luis Muñoz-Saavedra, Javier Civit-Masot, Anton Civit, and Manuel Domínguez-Morales. 2021. "AnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks" Sensors 21, no. 5: 1889. https://doi.org/10.3390/s21051889

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