savitakohli / machinelearningproject

MachineLearningProject

Human Activity Recognition - HAR - has emerged as a key research area in the last years and is gaining increasing attention by the pervasive computing research community, especially for the development of context-aware systems. There are many potential applications for HAR, like: elderly monitoring, life log systems for monitoring energy expenditure and for supporting weight-loss programs, and digital assistants for weight lifting exercises. Using devices such as Jawbone Up, Nike FuelBand, and Fitbit it is now possible to collect a large amount of data about personal activity relatively inexpensively. These type of devices are part of the quantified self movement - a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. One thing that people regularly do is quantify how much of a particular activity they do, but they rarely quantify how well they do it. In this project, your goal will be to use data from accelerometers on the belt, forearm, arm, and dumbell of 6 participants. They were asked to perform barbell lifts correctly and incorrectly in 5 different ways. Data being analyzed here is downloaded from https://d396qusza40orc.cloudfront.net/predmachlearn/. Original source of data is http://web.archive.org/web/20161224072740/http:/groupware.les.inf.puc-rio.br/har. The goal of this project is to predict the manner in which they did the exercise. Outcome is ‘classe’ variable in the training set. You may be able to view html version of this analysis by clicking following link from RPubs: http://rpubs.com/savitakohli/37775 Please feel free to send me your comments - negative or positive.