AI News, guillaume-chevalier/LSTM-Human-Activity-Recognition

guillaume-chevalier/LSTM-Human-Activity-Recognition

Other research on the activity recognition dataset used mostly use a big amount of feature engineering, which is rather a signal processing approach combined with classical data science techniques.

The dataset's description goes like this: The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window).

The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity.

That said, I will use the almost raw data: only the gravity effect has been filtered out of the accelerometer as a preprocessing step for another 3D feature as an input to help learning.

It can be roughly pictured like in the image below, imagining each rectangle has a vectorial depth and other special hidden quirks in the image below.

In our case, the 'many to one' architecture is used: we accept time series of feature vectors (one vector per time step) to convert them to a probability vector at the output for classification.

And it can peak to values such as 92.73%, at some moments of luck during the training, depending on how the neural network's weights got initialized at the start of the training, randomly.

In another open-source repository of mine, the accuracy is pushed up to 94% using a special deep LSTM architecture which combines the concepts of bidirectional RNNs, residual connections and stacked cells.

If you want to learn more about deep learning, I have also built a list of the learning ressources for deep learning which have revealed to be the most useful to me here.

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