Smartphones collect a wealth of information about their users. This includes GPS tracks and the MAC addresses of devices around the user, and it can go as far as taking visual and acoustic samples of the user's environment. We present a framework to identify a smartphone user's activities in a Bayesian setting. As prior information, we us a random utility model that accounts for the type of activity a user is likely to perform at any given location and time; this model was estimated for the whole population using data from the 2005 Swiss Transport Microcensus. The smartphone measurements come from a preliminary 2-month period survey, where one user carried around a phone programmed to constantly record his GPS location and other context variables, including the MAC addresses of nearby bluetooth devices. In addition to this, the user answered a daily survey, where he described and geolocated all the activities performed during this period. An analysis of the recorded data shows that the bluetooth information is useful to identify other users or devices that are frequently observed when performing specific activities. The bluetooth data is therefore used to estimate the likelihood of observing certain devices when performing certain activities. Combining the prior activity information from the random utility model with these likelihoods allows to generate improved posterior distributions of the user's activities. Due to the limited amount of available data only exemplary results are given, which, however, clearly indicate that the accuracy of the predictions can be greatly improved by using bluetooth data.