Wearable Fall Detector Using Recurrent Neural Networks

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 22; p. 4885
• Main Authors: Luna-Perejón, Francisco, Domínguez-Morales, Manuel Jesús, Civit-Balcells, Antón
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08.11.2019; MDPI
• Subjects: accelerometer; Accelerometers; Accelerometry - methods; Accidental Falls - prevention & control; Accuracy; Algorithms; Decision trees; Deep learning; Discriminant analysis; embedded system; Embedded systems; Energy consumption; Fall detection; Humans; Machine learning; Monitoring, Ambulatory - methods; Neural networks; Neural Networks, Computer; Older people; Power management; Public health; Real time; Recurrent neural networks; Sensors; Support vector machines; System effectiveness; Systems analysis; wearable; Wearable computers; Wearable Electronic Devices; Wearable technology; embedded system; deep learning; wearable; recurrent neural networks; fall detection; accelerometer
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19224885

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.