Inferring human activities from GPS tracks

The collection of huge amount of tracking data made possi- bile by the widespread use of GPS devices, enabled the anal- ysis of such data for several applications domains, ranging from traffic management to advertisement and social stud- ies. However, the raw positioning data, as it is detected by GPS devices, lacks of semantic information since these data do not natively provide any additional contextual in- formation like the places that people visited or the activities performed. Traditionally, this information is collected by hand filled questionnaire where a limited number of users are asked to annotate their tracks whith the activities they have done. With the purpose of getting large amount of semantically rich trajectories, we propose an algorithm for automatically annotating raw trajectories with the activi- ties performed by the users. To do this, we analyse the stops points trying to infer the Point Of Interest (POI) the user has visited. Based on the category of the POI and a probability law, we infer the activity performed. We exper- imented and evaluated the method in a real case study of car trajectories, manually annotated by users with their ac- tivities. We exploit the Gravity law and the nearby POIs for inferring the most probable activity performed by a user during a stop. Experimental results are encouraging and will drive our future works.