(IN)TANGIBLE HERITAGE(S)
Abstract
How a physical object in space appeals to us as humans are complex to quantify, as it depends on various factors, the status of which varies over time. An in-the-field study provides a quantitative understanding of the cognitive decision-making process when humans evaluate and select between a set of workspace-related artifacts. Academic students chose from six different workspace zones in a public space library. Each zone’s interior design varied due to differences in furniture type, surroundings such as window, wall, hallway passage, and table occupancy rates. Utilizing advanced non-invasive depth sensors made it possible to include the entire population of 638 visitors during 12 days of testing and record their individual choice of the respective zone, furniture, and seating orientation. Sensors are non-invasive and measure depth data that does not violate general data protection regulations. Therefore, the application becomes more feasible and comprehensive in cases to monitor people, as it is not necessary to obtain the consent of the users, and it manages to include the total population. With the precise and large amount of data, it has been possible to establish logical decision-making processes that contain critical criteria and threshold values for specific artifacts when choosing between workspaces. Combed with advanced data analytical approaches enables the opportunity to understand how different artifacts affect our cognitive processes. In addition, specific user preferences have been identified, such as choosing between table types with a statistical significance (p<0.01). It is possible to historically archive complex social events, including the encounter between humans, space, and associated artifacts, quantitatively. The technological setup provides a novel tool to understand and forecast how humans live and behave indoors and how we are affected by the complex physical and social components environment. Results are statistically significant evaluations of the interior design to understand users’ preferences and needs.
Andrew Khoudi is a third-year industrial Ph.D. student at Aarhus School of Architecture, Denmark, and industrial sponsor Schmidt Hammer Lassen Architects and Soren Jensen Consulting Engineers. The candidate’s academic background is within signal processing, and before commencing the Ph.D. program, Andrew Khoudi spends nearly ten years in the industry. Initially, Andrew Khoudi joined a consulting engineering company for six years. The main task was planning and designing large Danish hospitals. This was done in close collaboration with architects, which allowed the candidate to establish an in-depth understanding of the architects’ work methods and culture. Afterward, four years were spent at a tech start-up developing sensors and analyzing data to quantify human behavior in indoor spaces to improve the user experience. Primary spaces to be monitored and analyzed were learning environments such as universities and public schools. As an alternative Ph.D. student within the architectural environment, the candidate aims to research methods making the architectural design process of indoor spaces more data-driven. The main focus of the research is to enable the use of sensors and algorithms to extract information on human behavior and how to improve the user experience by adjusting the interior space design.
7:00 pm - 8:30 pm
Friday 17th June, 2022