Despite decades of research into patient falls, falls and the injuries incurred continue to be a serious threat to patient safety. Fall rates continue to be unacceptably high. Advances in computational modeling and simulation will provide insight for designing a room that maximizes patient safety during ambulation. The purpose of this project is to increase the safety of a hospital room for patient mobility between the bed and the bathroom, using innovative simulation strategies and patient-centric design. An innovative simulation environment will be built to enable rapid assessment of room layout and fixture positioning and patient stability. The results from multiple simulations, informed by patient biomechanics and stakeholder input, will be used to fabricate an enhanced fidelity prototype room layout that will be tested by elderly participants at risk of falling and reviewed and updated with additional input from other relevant stakeholders. A final room prototype will be built and tested. A 3D augmented reality (AR) holographic application will be created to improve the translation of information about room design and patient safety. Results will be shared and disseminated for implementation. Hospital rooms are multi-functional rooms that should satisfy patient needs along with others who provide care or housekeeping. New healthcare equipment and laws, records of medical errors, infection control and injuries due to patient falls necessitate safer and more innovative hospital rooms. A more human-centered perspective can be used for room designs to meet everyone?s needs. What we propose will depart significantly from the status quo by formulating all these user needs as one multi- objective optimization problem. We will consider different constraints regarding patients and clinical care. Our method will combine, for the first time, new sampling-based motion planner ideas from biomechanically derived learning. Similar approaches have been highly successful in enabling robots to learn task constraints and imitate motion in unpredictable environments. This approach will serve as the model in simulation to predict the maximally safe environment for patient mobility. Our adapted framework will consist of a learning phase, the application of innovative cost metrics and constraints on the optimization problem, and will result in a spatial plan for the room and a motion plan for the patient and nurse. This safe patient room, once developed, will be always available, and not subject to caregiver error as is the case with existing surveillance technologies. Healthcare industry must demand safe rooms when planning and contracting for new facilities and renovation. Our configuration will have application to hospitals, nursing homes and extended care. Once incorporated into hospital units, the results of this research will have a profound impact on patient fall rate and reducing injuries from falls. The expected results from this research will direct future hospital fall intervention research and positively affect patients by reducing fall-related injuries, thereby resulting in substantial cost savings to the healthcare system.