Human activity recognition using the LSTM model
The Human Activity Recognition (HAR) database was built from the recordings of 30 study participants performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. The objective is to classify activities into one of the six activities performed.
Dataset description
The experiments have been carried out with a group of 30 volunteers within an age bracket of 19 - 48 years. Each person accomplished six activities, namely walking, walking upstairs, walking downstairs, sitting, standing, and laying by wearing a Samsung Galaxy S II smartphone on their waist. Using the accelerometer and gyroscope, the author captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50 Hz.
Only two sensors, that is, accelerometer and gyroscope, were used. The sensor signals were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50...
