LSTMs for Human Activity Recognition Time Series Classification

Last Updated on August 28, 2020

Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements.

Classical approaches to the problem involve hand crafting features from the time series data based on fixed-sized windows and training machine learning models, such as ensembles of decision trees. The difficulty is that this feature engineering requires strong expertise in the field.

Recently, deep learning methods such as recurrent neural networks like as LSTMs and variations that make use of one-dimensional convolutional neural networks or CNNs have been shown to provide state-of-the-art results on challenging activity recognition tasks with little or no data feature engineering, instead using feature learning on raw data.

In this tutorial, you will discover three recurrent neural network architectures for modeling an activity recognition time series classification problem.

After completing this tutorial, you will know:

• How to develop a Long Short-Term Memory Recurrent Neural Network for human activity recognition.
• How to develop a one-dimensional Convolutional Neural Network LSTM, or CNN-LSTM, model.
• How to develop a one-dimensional Convolutional LSTM, or ConvLSTM, model for the same problem.

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