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.

中文翻译：

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一种新颖的多通道扩张卷积神经网络用于人类活动识别

提出了一种新颖的多通道扩张卷积神经网络，以提高人类活动识别的准确性。所提出的模型利用具有各种大小的多个核的多通道卷积结构来提取卷积操作期间人类活动的高维数据的多尺度特征，并且不考虑使用传统卷积和扩张卷积中使用的池化层。它的优点是，扩展的卷积可以通过扩展卷积核的范围而首先捕获固有序列信息，而无需增加模型的参数量。然后，通过形成多个卷积路径，可以将多通道结构用于提取多尺度步态特征。开放的人类活动识别数据集用于评估我们提出的模型的有效性。实验结果表明，我们的模型达到了95.49％的精度，在低端机器上识别单个样本的时间约为0.34 ms。这些结果表明我们的模型是一种高效的实时HAR模型，可以在低计算量的情况下从传感器信号中获得代表性特征，并有望成为实际应用中的有效工具。