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LSTM-CNN
Architecture
for Human Activity Recognition
Xia, Kun ; Huang, Jianguang ; Wang, Hanyu
IEEE access, 2020, Vol.8, p.56855-56866
[Periódico revisado por pares]
Piscataway: IEEE
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Título:
LSTM-CNN
Architecture
for Human Activity Recognition
Autor:
Xia, Kun
;
Huang, Jianguang
;
Wang, Hanyu
Assuntos:
Acceleration
;
Accelerometers
;
Activity recognition
;
Architecture
;
Artificial neural networks
;
Convolution
;
Datasets
;
Deep learning
;
Feature extraction
;
Human activity recognition
;
Human motion
;
long short-term memory
;
Machine learning
;
Mathematical models
;
Mobile computing
;
mobile sensors
;
Model accuracy
;
Moving object recognition
;
Neural networks
;
Parameters
;
Pattern recognition
;
Performance evaluation
;
Recurrent neural networks
;
Sensor phenomena and characterization
É parte de:
IEEE access, 2020, Vol.8, p.56855-56866
Descrição:
In the past years, traditional pattern recognition methods have made great progress. However, these methods rely heavily on manual feature extraction, which may hinder the generalization model performance. With the increasing popularity and success of deep learning methods, using these techniques to recognize human actions in mobile and wearable computing scenarios has attracted widespread attention. In this paper, a deep neural network that combines convolutional layers with long short-term memory (LSTM) was proposed. This model could extract activity features automatically and classify them with a few model parameters. LSTM is a variant of the recurrent neural network (RNN), which is more suitable for processing temporal sequences. In the proposed
architecture
, the raw data collected by mobile sensors was fed into a two-layer LSTM followed by convolutional layers. In addition, a global average pooling layer (GAP) was applied to replace the fully connected layer after convolution for reducing model parameters. Moreover, a batch normalization layer (BN) was added after the GAP layer to speed up the convergence, and obvious results were achieved. The model performance was evaluated on three public datasets (UCI, WISDM, and OPPORTUNITY). Finally, the overall accuracy of the model in the UCI-HAR dataset is 95.78%, in the WISDM dataset is 95.85%, and in the OPPORTUNITY dataset is 92.63%. The results show that the proposed model has higher robustness and better activity detection capability than some of the reported results. It can not only adaptively extract activity features, but also has fewer parameters and higher accuracy.
Editor:
Piscataway: IEEE
Idioma:
Inglês
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