AdityaGirishPawate/Time-series-classification-using-1-D-CNNs

This project is on how to Develop 1D Convolutional Neural Network Models for Human Activity Recognition Below is an example video of a subject performing the activities while their movement data is being recorded. The six activities performed were as follows: Walking Walking Upstairs Walking Downstairs Sitting Standing Laying The movement data recorded was the x, y, and z accelerometer data (linear acceleration) and gyroscopic data (angular velocity) from the smart phone, specifically a Samsung Galaxy S II. Observations were recorded at 50 Hz (i.e. 50 data points per second). Each subject performed the sequence of activities twice, once with the device on their left-hand-side and once with the device on their right-hand side. Pre-processing accelerometer and gyroscope using noise filters. Splitting data into fixed windows of 2.56 seconds (128 data points) with 50% overlap. Splitting of accelerometer data into gravitational (total) and body motion components.

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