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We are currently using smart phone sensors to detect physical activities. The sensors which are currently being used are accelerometer, gyroscope, barometer, etc. Recently, smart phones, equipped with a rich set of sensors, are explored as alternative platforms for human activity recognition. Automatic recognition of physical activities–commonly referred to as human activity recognition (HAR)–has emerged as a key research area in human-computer interaction (HCI) and mobile and ubiquitous computing. One goal of activity recognition is to provide information on a user’s behavior that allows computing systems to proactively assist users with their tasks. Human activity recognition requires running classification algorithms, originating from statistical machine learning techniques. Mostly, supervised or semi-supervised learning techniques are utilized and such techniques rely on labeled data, ie, associated with a specific class or activity. In most of the cases, the user is required to label the activities and this, in turn, increases the burden on the user. Hence, user-independent training and activity recognition are required to foster the use of human activity recognition systems where the system can use the training data from other users in classifying the activities of a new subject.