Accelerometer and Gyroscope readings are taken from 30 volunteers(referred as subjects) while performing the following 6 Activities.
1.Walking 2.WalkingUpstairs 3.WalkingDownstairs 4.Standing 5.Sitting 6.Lying.
This project is to build a model that predicts the human activities
Readings are divided into a window of 2.56 seconds with 50% overlapping.
Accelerometer readings are divided into gravity acceleration and body acceleration readings, which has x,y and z components each.
Gyroscope readings are the measure of angular velocities which has x,y and z components.
Jerk signals are calculated for BodyAcceleration readings.
Fourier Transforms are made on the above time readings to obtain frequency readings.
Now, on all the base signal readings., mean, max, mad, sma, arcoefficient, engerybands,entropy etc., are calculated for each window.
We get a feature vector of 561 features and these features are given in the dataset.
Each window of readings is a datapoint of 561 features.
We use a simple LSTM architecture with the help of Keras to predict the activities.