Lighting the Patient Room of the Future: Evaluating Different Lighting Conditions From the Patient Perspective

A Review of Physical and Digital Mock-Up Applications in Healthcare Building Development

Mock-up simulation is a design or human factor research method to help designers identify key design issues and factors of a product or environment. This paper discusses physical mock-up (PMU) and digital mock-up (DMU) applications in healthcare building development through a narrative literature review. The following questions are addressed in this paper: what would the purposes of using PMU or DMU simulations be? At which phase of a hospital design would a PMU or DMU simulation be used? What methods can be used to conduct PMU and DMU simulations? The paper discusses the advantages and disadvantages of these two mock-up methods and highlights the importance of clinical staff’s involvement in mock-up simulations. It gives recommendations for the design practitioners or project managers of healthcare building development recommendations to implement these two mock-up methods in healthcare building development projects.

An Edge Computing and Ambient Data Capture System for Clinical and Home Environments

The non-contact patient monitoring paradigm moves patient care into their homes and enables long-term patient studies. The challenge, however, is to make the system non-intrusive, privacy-preserving, and low-cost. To this end, we describe an open-source edge computing and ambient data capture system, developed using low-cost and readily available hardware. We describe five applications of our ambient data capture system. Namely: (1) Estimating occupancy and human activity phenotyping; (2) Medical equipment alarm classification; (3) Geolocation of humans in a built environment; (4) Ambient light logging; and (5) Ambient temperature and humidity logging. We obtained an accuracy of 94% for estimating occupancy from video. We stress-tested the alarm note classification in the absence and presence of speech and obtained micro averaged F1 scores of 0.98 and 0.93, respectively. The geolocation tracking provided a room-level accuracy of 98.7%. The root mean square error in the temperature sensor validation task was 0.3°C and for the humidity sensor, it was 1% Relative Humidity. The low-cost edge computing system presented here demonstrated the ability to capture and analyze a wide range of activities in a privacy-preserving manner in clinical and home environments and is able to provide key insights into the healthcare practices and patient behaviors.