Healthcare is the most expensive operations in the United States costing the providers and government. According to National Healthcare Expenditures in 2016, United States spends $3.3 trillion (which amounts to $10,438 per person). Hospitals are looking to improve automation in hospital operations, while driving quality. A significant portion (i.e. 32% of this amount (i.e. $1.1 trillion) is spent on hospital operations. The goal of this project is to develop a system to automatically detect and visualize techniques to understand stress and activity statistics of various hospital divisions and operations using data from wearables. The project has three major objectives (1) Develop techniques to detect stress signals in a hospital setting using wearables, machine learning, (2) Develop techniques to detect activities from accelerometer and position sensors, and (3) develop techniques to visualize stress data in a 3D building environment. The proposed techniques will be evaluated on actual human subjects to demonstrate the effectiveness and applicability of these techniques.