KNN model for human activity tracker
This project aims to build a model that predicts human activities such as Walking, Walking Upstairs, Walking Downstairs, Sitting, Standing and Laying from the Sensor data of smartphones.
The dataset can be downloaded from https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones
Human Activity Recognition database is built from the recordings of 30 persons performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors(accelerometer and Gyroscope). Activities
Walking Walking Upstairs Walking Downstairs Sitting Standing Laying
Accelerometers detect magnitude and direction of the proper acceleration, as a vector quantity, and can be used to sense orientation (because direction of weight changes)
GyroScope maintains orientation along a-axis so that the orientation is unaffected by tilting or rotation of the mounting, according to the conservation of angular momentum.
An accelerometer measures the directional movement of a device but will not be able to resolve its lateral orientation or tilt during that movement accurately unless a gyro is there to fill in that info. With an accelerometer, you can either get a really "noisy" info output that is responsive, or you can get a "clean" output that's sluggish. But when you combine the 3-axis accelerometer with a 3-axis gyro, you get an output that is both clean and responsive in the same time.