This document outlines a study that presents a joint probabilistic model to improve activity detection by reasoning about object use, physical actions, and activities together. Sensors collected data from researchers performing 12 activities using 30 objects over 2 weeks. A dynamic Bayesian network model was used to infer the current action and object being used from the sensor data. Results showed the joint model produced better activity detection than models reasoning separately about object use, actions, and activities, and learned action models with less labeling than conventional approaches.