Evaluate Machine Learning Algorithms for Human Activity Recognition

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 deep expertise in the field.

Recently, deep learning methods such as recurrent neural networks and 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 how to evaluate a diverse suite of machine learning algorithms on the ‘Activity Recognition Using Smartphones‘ dataset.

After completing this tutorial, you will know:

• How to load and evaluate nonlinear and ensemble machine learning algorithms on the feature-engineered version of the activity recognition dataset.
• How to load and evaluate machine learning algorithms on the raw signal data for the activity recognition dataset.
• How to define reasonable lower and upper bounds on the expected performance of more sophisticated algorithms capable
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