Paper Info
Title
A Novel Monitoring System for Fall Detection in Older People.
Abstract
Each year, more than 30% of people over 65 years-old suffer some fall. Unfortunately, this can generate physical and psychological damage, especially if they live alone and they are unable to get help. In this field, several studies have been performed aiming to alert potential falls of the older people by using different types of sensors and algorithms. In this paper, we present a novel non-invasive monitoring system for fall detection in older people who live alone. Our proposal is using very-low-resolution thermal sensors for classifying a fall and then alerting to the care staff. Also, we analyze the performance of three recurrent neural networks for fall detections: long short-term memory (LSTM), gated recurrent unit, and Bi-LSTM. As many learning algorithms, we have performed a training phase using different test subjects. After several tests, we can observe that the Bi-LSTM approach overcome the others techniques reaching a 93% of accuracy in fall detection. We believe that the bidirectional way of the Bi-LSTM algorithm gives excellent results because the use of their data is influenced by prior and new information, which compares to LSTM and GRU. Information obtained using this system did not compromise the user's privacy, which constitutes an additional advantage of this alternative.
Year
DOI
Venue
2018
10.1109/ACCESS.2018.2861331
IEEE ACCESS
Keywords
Field
DocType
Fall detection,older people,artificial neural networks
Monitoring system,Computer science,Recurrent neural network,Artificial intelligence,Artificial neural network,Thermal sensors,Machine learning,Distributed computing
Journal
Volume
ISSN
Citations 
6
2169-3536
3
PageRank 
References 
Authors
0.40
0
8
Name
Order
Citations
PageRank
Carla Taramasco13910.55
Tomas Rodenas240.77
Felipe Martinez340.77
Paola Fuentes440.77
Roberto Muñoz-Soto573.18
Rodrigo Olivares6459.07
Victor Hugo C. de Albuquerque791483.30
Jacques Demongeot837068.80