Recurrent Neural Networks Architectures for Accidental Fall Detection on Wearable Embedded Devices

2021 ◽  
pp. 81-98
Author(s):  
Mirto Musci ◽  
Marco Piastra
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Fall Detection ◽  
Embedded Devices ◽  
Accidental Fall

scholarly journals Online Fall Detection using Recurrent Neural Networks on Smart Wearable Devices

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Mirto Musci ◽  
Daniele De Martini ◽  
Nicola Blago ◽  
Tullio Facchinetti ◽  
Marco Piastra
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Fall Detection ◽  
Wearable Devices ◽  
Smart Wearable

Embedding Recurrent Neural Networks in Wearable Systems for Real-Time Fall Detection

2019 ◽  
Vol 71 ◽  
pp. 102895 ◽  
Author(s):  
Emanuele Torti ◽  
Alessandro Fontanella ◽  
Mirto Musci ◽  
Nicola Blago ◽  
Danilo Pau ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Recurrent Neural Networks ◽  
Fall Detection ◽  
Wearable Systems ◽  
Time Fall

scholarly journals Wearable Fall Detector Using Recurrent Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4885 ◽  
Author(s):  
Francisco Luna-Perejón ◽  
Manuel Jesús Domínguez-Morales ◽  
Antón Civit-Balcells
Keyword(s):  
Neural Networks ◽  
Low Power ◽  
Real Time ◽  
Recurrent Neural Networks ◽  
Response Times ◽  
Fall Detection ◽  
High Energy ◽  
Great Accuracy ◽  
Public Health Issue ◽  
Fall Detector

Falls have become a relevant public health issue due to their high prevalence and negative effects in elderly people. Wearable fall detector devices allow the implementation of continuous and ubiquitous monitoring systems. The effectiveness for analyzing temporal signals with low energy consumption is one of the most relevant characteristics of these devices. Recurrent neural networks (RNNs) have demonstrated a great accuracy in some problems that require analyzing sequential inputs. However, getting appropriate response times in low power microcontrollers remains a difficult task due to their limited hardware resources. This work shows a feasibility study about using RNN-based deep learning models to detect both falls and falls’ risks in real time using accelerometer signals. The effectiveness of four different architectures was analyzed using the SisFall dataset at different frequencies. The resulting models were integrated into two different embedded systems to analyze the execution times and changes in the model effectiveness. Finally, a study of power consumption was carried out. A sensitivity of 88.2% and a specificity of 96.4% was obtained. The simplest models reached inference times lower than 34 ms, which implies the capability to detect fall events in real-time with high energy efficiency. This suggests that RNN models provide an effective method that can be implemented in low power microcontrollers for the creation of autonomous wearable fall detection systems in real-time.

scholarly journals Lightweight Online Noise Reduction on Embedded Devices Using Hierarchical Recurrent Neural Networks

2020 ◽  
Author(s):  
H. Schröter ◽  
T. Rosenkranz ◽  
A.N. Escalante-B. ◽  
P. Zobel ◽  
Andreas Maier
Keyword(s):  
Neural Networks ◽  
Noise Reduction ◽  
Recurrent Neural Networks ◽  
Embedded Devices

scholarly journals An Automated Fall Detection System Using Recurrent Neural Networks

2019 ◽  
pp. 36-41
Author(s):  
Francisco Luna-Perejon ◽  
Javier Civit-Masot ◽  
Isabel Amaya-Rodriguez ◽  
Lourdes Duran-Lopez ◽  
Juan Pedro Dominguez-Morales ◽  
...  
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Detection System ◽  
Fall Detection
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