Recent technological advances and the ever-greater developments in sensing and computing continue to provide new ways of understanding our daily mobility. Smart devices such as smartphones or smartwatches can, for instance, provide an enhanced user experience based on different sets of built-in sensors that follow every user action and identify its environment. Monitoring solutions such as these, which are becoming more and more common, allow us to assess human behavior and movement at different levels. In this article, we focus on the concept of human mobility. With the participation of 13 individuals, we carried out an experiment to discover how groups of sensors currently available in smartphones and smartwatches can help to distinguish different profiles and patterns of human mobility. We show that it is possible to use not only motion sensors but also physiological sensors and environmental data provided, for instance, by Wi-Fi. Finally, detailed study of these categories enables us to offer a way of representing the mobility of individual users, based on anonymized traces and graph theory.