Human Activity Recognition by Wearable Sensors

The purpose of this project is to develop a model that is capable of recognizing daily basic human activities under real-world conditions, using data collected by a waist-mounted triaxial accelerometer and gyroscope built into a cellphone (in our study, a Samsung Galaxy S II). Activity recognition is formulated as a supervised classification problem, whose data is obtained via an experiment having 30 human subjects perform each of the activities. Our classification models have been trained and tested with data of subjects performing the following six physical activity patterns: Walking, Walking up-stairs, Walking down-stairs, Sitting, Standing and Laying down.