Simple BLSTM-MLP for sensor data classification
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Note: Data not added due to proprietary restrictions.
About the data: Thermocouples and epidermal sensors were placed at 8 location (see Fig. 1). Data contains 8 feature rows and a class label.
Important Notes:
- Sensor data is inherently noisy, as such, using a denoising autoencoder (dA) might be necessary. dA uses input
$x \in [0,1]^d$ and maps it to $ y \in [0,1]^d$, uing a deterministic mapping often levered by a sigmoid function ($s$ ):$y=s(Wx+b)$ - Here, we will employ bidirectional LSTM (BLSTM), which are capable of learning the context in both temporal directions, in addition to a multi-layer perceptron (MLP), stacked (Model 1)
- Future Implementation: Auxiliary Classifier Generative Adversarial Network (ACGAN) is also shown in Model 2
- As evaluation metrics we used F-measure in order to compare the results with previous works.
Fig. 1 Sensor Placement
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