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Open AccessArticle

A New Approach to Fall Detection Based on Improved Dual Parallel Channels Convolutional Neural Network

1
College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
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Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(12), 2814; https://doi.org/10.3390/s19122814
Received: 5 May 2019 / Revised: 15 June 2019 / Accepted: 21 June 2019 / Published: 24 June 2019
(This article belongs to the Section Biosensors)
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. View Full-Text
Keywords: fall detection; surface electromyography; spectrograms; convolutional neural network; pattern recognition fall detection; surface electromyography; spectrograms; convolutional neural network; pattern recognition
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MDPI and ACS Style

Liu, X.; Li, H.; Lou, C.; Liang, T.; Liu, X.; Wang, H. A New Approach to Fall Detection Based on Improved Dual Parallel Channels Convolutional Neural Network. Sensors 2019, 19, 2814.

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