Human activity recognition using LSTM model
The Human Activity Recognition (HAR) database was built by taking measurements from 30 participants who performed activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. The objective is to classify their activities into one of the six categories mentioned previously.
Dataset description
The experiments were carried out on a group of 30 volunteers within an age range of 19-48 years. Each person accomplished six activities (walking, walking upstairs, walking downstairs, sitting, standing, and laying) while wearing a Samsung Galaxy S II smartphone on their waist. Using an accelerometer and a gyroscope, the author captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50 Hz.
Only two sensors, an 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 with a 50...
