Abstract
A novel multichannel dilated convolution neural network for improving the accuracy of human activity recognition is proposed. The proposed model utilizes the multichannel convolution structure with multiple kernels of various sizes to extract multiscale features of high-dimensional data of human activity during convolution operation and not to consider the use of the pooling layers that are used in the traditional convolution with dilated convolution. Its advantage is that the dilated convolution can first capture intrinsical sequence information by expanding the field of convolution kernel without increasing the parameter amount of the model. And then, the multichannel structure can be employed to extract multiscale gait features by forming multiple convolution paths. The open human activity recognition dataset is used to evaluate the effectiveness of our proposed model. The experimental results showed that our model achieves an accuracy of 95.49%, with the time to identify a single sample being approximately 0.34 ms on a low-end machine. These results demonstrate that our model is an efficient real-time HAR model, which can gain the representative features from sensor signals at low computation and is hopeful for the effective tool in practical applications.
Highlights
Human activity recognition (HAR) is a typical multiclassification problem, which acquires and analyzes human activity-related data to identify human activity status [1, 2]
With the development of smartphones and wearable sensor technologies, smart devices with built-in sensors are characterized by low cost, convenient carrying, and good realtime performance. erefore, HAR based on sensor signals has become the focus of research in this field
HAR based on sensor signals includes two methods: the traditional method and the deep learning method. e traditional method based on sensor signal for HAR needs complex preprocessing of the raw data and relies on manual experience to extract the required time-domain features [14,15,16], frequency-domain features [16,17,18,19], and other features [20, 21]. ese hand-craft features are shallow features, which would inevitably lose some implicit key features
Summary
Human activity recognition (HAR) is a typical multiclassification problem, which acquires and analyzes human activity-related data to identify human activity status [1, 2]. Different deep learning methods have been proposed for human activity recognition based on sensor signals, including autoencoders [26], fully connected deep neural network (DNN) [27, 28], recurrent neural network (RNN), convolutional neural networks (CNN), and the hybrid deep learning model. E shortcoming of these works is that it is difficult to maintain the balance between activity recognition accuracy and running time All of these challenges have led researchers to develop efficient recognition methods with high recognition accuracy and low computational complexity effectively solving these problems. E model can get a larger receptive field to extract global features of long-time series from the raw sensor data by using dilated convolution rather than traditional convolution structure.
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