TensorFlow 2.0 Tutorial for Beginners 14 - Human Activity Recognition using Accelerometer and CNN

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In this video we will learn about human activity recognition using Accelerometer and CNN. In this project we are going to use accelerometer data to train the model so that it can predict the human activity. We are going to use 2D Convolutional Neural Networks to build the model. This WISDM dataset contains data collected through controlled, laboratory conditions. The total number of examples is 1,098,207. The dataset contains six different labels (Downstairs, Jogging, Sitting, Standing, Upstairs, Walking).

From the data distribution shown above we can observe that the data is unbalanced. Standing has very less examples compared to Walking and Jogging'. If we use this data directly it will overfit and will be skewed towards Walking and Jogging'. As we saw earlier the data is in string data type. Here we have converted the x, y, z values into floating values using astype('float').

🔊 Watch till last for a detailed description
03:18 Understanding dataset
16:50 Balancing the data
29:52 Standardizing data
33:11 Frame preparation
41:14 Building 2D CNN model
47:50 Plotting the learning curve
48:37 Confusion matrix

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