SLAR (Semantic Location and Activity Recognition) algorithms studied on iOS systems. This thesis provides an algorithm for concurrent detection of semantic location and activity of the user, within a range of nine different possibilities. They are the combination of two possible semantic location states ("indoor" and "outdoor") and five different human activities ("stationary", "walking", "running", "biking" and "automotive"). The final output values are {automotive, indoor stationary, indoor walking, indoor running, indoor biking, outdoor stationary, outdoor walking, outdoor running, outdoor biking}. The recognition of these nine possible states is based on data coming from different smartphone sensors, selected between the less consumptive ones and basing on previous research works, for the applicationto be feasible and implementable.