Shifting demographics in the U.S. has created an urgent need to reform the policies, practices, and technology associated with delivering healthcare to geriatric populations. Automated monitoring systems can improve the quality of life while reducing healthcare costs for individuals aging in place. For these systems to be successful, both activity detection and localization are important, but most existing research focuses on only one of these technologies and systems that do collect both data treat these data sources separately. Here, we present SLAD {Simultaneous Localization and Activity Detection a novel framework for simultaneously processing data collected from localization and activity classification systems. Using a hidden Markov model and machine learning techniques, SLAD fuses these two sources of data in realtime using a probabilistic likelihood framework, which allows activity data to refine localization, and vice-versa. To evaluate the system, a wireless sensor network was deployed to collect RSSI data and IMU data concurrently from a wrist-worn watch; the RSSI data was processed using a radial basis function neural network localization algorithm, and the resulting position likelihoods were combined with the likelihoods from an IMU acitivty classification algorithm. In an experiment conducted in an indoor office environment, the proposed method produces 97% localization accuracy and 85% activity classification.