![]() Human activity recognition 1 is to classify and recognize the movement behaviors by analyzing the information of human activities, which has great commercial value and scientific research significance in the fields of human-computer interaction, covid-19 tracking and public safety. The results show that the ConvBLSTM-PMwA model performs better than the existing CNN and RNN models in both classification accuracy (96.71%) and computational time complexity (1.1 times faster at least), even if facing HAR data with noise. The performance is evaluated on public UCI and WISDM HAR datasets. At last, the final classification results are obtained with the full connection layer. The weights of extracted features are redistributed by attention mechanism and integrated into complete features. The original features of sensors are segmented into sub-segments by well-designed equal time step sliding window, and fed into 1-D CNN-based bi-directional LSTM parallel layer to accelerate feature extraction with noisy and missed data. In this paper, a 1-D Convolution Neural Network (CNN)-based bi-directional Long Short-Term Memory (LSTM) parallel model with attention mechanism (ConvBLSTM-PMwA) is proposed. One of the main challenges is the classification time and accuracy in processing long-time dependent sequence samples with noisy or missed data. Boosted by mobile communication technologies, Human Activity Recognition (HAR) based on smartphones has attracted more and more attentions of researchers. ![]()
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