Paper Info
Title
A New Approach to Fall Detection Based on Improved Dual Parallel Channels Convolutional Neural Network.
Abstract
Falls are the major cause of fatal and non-fatal injury among people aged more than 65 years. Due to the grave consequences of the occurrence of falls, it is necessary to conduct thorough research on falls. This paper presents a method for the study of fall detection using surface electromyography (sEMG) based on an improved dual parallel channels convolutional neural network (IDPC-CNN). The proposed IDPC-CNN model is designed to identify falls from daily activities using the spectral features of sEMG. Firstly, the classification accuracy of time domain features and spectrograms are compared using linear discriminant analysis (LDA), k-nearest neighbor (KNN) and support vector machine (SVM). Results show that spectrograms provide a richer way to extract pattern information and better classification performance. Therefore, the spectrogram features of sEMG are selected as the input of IDPC-CNN to distinguish between daily activities and falls. Finally, The IDPC-CNN is compared with SVM and three different structure CNNs under the same conditions. Experimental results show that the proposed IDPC-CNN achieves 92.55% accuracy, 95.71% sensitivity and 91.7% specificity. Overall, The IDPC-CNN is more effective than the comparison in accuracy, efficiency, training and generalization.
Year
DOI
Venue
2019
10.3390/s19122814
SENSORS
Keywords
Field
DocType
fall detection,surface electromyography,spectrograms,convolutional neural network,pattern recognition
Time domain,Pattern recognition,Convolutional neural network,Spectrogram,Support vector machine,Communication channel,Electronic engineering,Artificial intelligence,Engineering,Linear discriminant analysis
Journal
Volume
Issue
ISSN
19
12
1424-8220
Citations 
PageRank 
References 
1
0.41
0
Authors
6
Name
Order
Citations
PageRank
Xiaoguang Liu184.27
Huanliang Li210.41
Cunguang Lou321.18
Tie Liang410.41
Xiuling Liu510.41
Hongrui-Wang611.76