Open AccessCNN-LSTM Combined Network for IoT Enabled Fall Detection Applications
Author(s) -
Jun Xu,
Zhiyuan He,
Yan Zhang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1267/1/012044
Subject(s) - computer science , internet of things , convolutional neural network , support vector machine , long short term memory , artificial intelligence , acceleration , feature (linguistics) , feature extraction , pattern recognition (psychology) , data mining , artificial neural network , machine learning , real time computing , recurrent neural network , embedded system , linguistics , philosophy , physics , classical mechanics
An accidental fall could do a great damage to the health of elderly. Failure to provide timely assistance after a fall may cause injury or even death. In this paper, a fall detection algorithm based on Convolutional Neural Network (CNN)-Long Short Term Memory (LSTM) combined network is proposed, which makes full use of the powerful feature extraction ability of CNN and the excellent time series processing ability of LSTM. Data required by the algorithm is only the resultant acceleration from a low cost three-axis acceleration sensor. The experimental results show that compared with the algorithms based on Support Vector Machine (SVM) and CNN, the proposed algorithm has higher detection accuracy with a small data volume, which is very suitable for Internet of Things (IoT) enabled fall detection applications.