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The advances in electronic devices have entailed the development of smart environments which have the aim to help and make easy the life of their inhabitants. In this kind of environments, an important task is the process of activity recognition of an inhabitant in the environment in order to anticipate the occupant necessities and to adapt such smart environment. Due to the cost to checking activity recognition approaches in real environments, usually, they use datasets generated from smart environments. Although there are many datasets for activity recognition in smart environments, it is difficult to find single, interleaved or multioccupancy activity datasets, or combinations of these classes of activities according to the researchers’ needs. In this work, the design and development of a complete dataset with 14 sensors and 9 different activities daily living is described, being this dataset divided into partitions …