DescriptionIn recent years, smartphones have become part and parcel of peoples life. Smartphones are used for all day-to-day critical tasks like money transfer, storing important documents and other information. This thesis presents, a user identification system based on smartwatch data. For identification of a user, walking activity and call receiving activity are analyzed when the phone is on the table and in pocket or bag. The recorded data from four smartwatch sensors enables the calculation of mean, variance, skewness, and gamma distribution parameters. These features are used to train the model. The presented system was tested on 20 participants and has an Equal Error Rate (EER) of 0.052.