Human locomotion activity recognition using spectral analysis and convolutional neural networks

Amer, Ahmad; Ji, Ze

doi:10.1504/ijmr.2021.119633

Cited by 5 publications

(1 citation statement)

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“…But they came across the problem of extracting temporal features. Thus, Amer et al 13 proposed two-step approach to recognize HAR. One was to transform the data into time-frequency representation by applying spectral analysis.…”

Section: Introductionmentioning

confidence: 99%

A Novel CNN-based Bi-LSTM parallel model with attention mechanism for human activity recognition with noisy data

Yin

Liu

et al. 2022

Sci Rep

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Boosted by mobile communication technologies, Human Activity Recognition (HAR) based on smartphones has attracted more and more attentions of researchers. One of the main challenges is the classification time and accuracy in processing long-time dependent sequence samples with noisy or 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. 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. The weights of extracted features are redistributed by attention mechanism and integrated into complete features. At last, the final classification results are obtained with the full connection layer. The performance is evaluated on public UCI and WISDM HAR datasets. 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.

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Section: Introductionmentioning

confidence: 99%