Background & Objective:
This paper proposes a Fourier transform inspired method to classify
human activities from time series sensor data.
Methods:
Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by
the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which
captures the temporal dependency from the signal and then produces encoded sequences. The sequences,
once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of
such transformation is that we can exploit the recent advances of the deep learning models for the image
classification such as Convolutional Neural Network (CNN).
Results:
The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the
model over two data sets. For the first data set, which is more standardized than the other, our model
outperforms previous works or at least equal. In the case of the second data set, we devise the schemes
to generate training and testing data by changing the parameters of the window size, the sliding size,
and the labeling scheme.
Conclusion:
The evaluation results show that the accuracy is over 95% for some cases. We also analyze
the effect of the parameters on the performance.
Free-breathing cardiac MR with a fixed navigator efficiency using adaptive gating window size