Exploring Human Interaction with Projected Augmented Relief Model (PARM) - Arss, Nachnoer (2022)
The Projection Augmented Relief Model (PARM) design comprises a physical landscape model enhanced with digital map and image content using digital projection which allows engaging interaction while presenting geographical information to people. This research explored the ways in which people gain a better understanding of landscape through projection-enhanced physical models compared to flat surface representations using an upland terrain and an urban environment as the case studies. Participants were asked to judge identical geographical information displayed on the PARM and the flat map through a series of questions. The results showed that PARM helps participants to accurately interpret the landscape of an upland terrain (the Lake District model) with an accuracy of 78.9% compared to 66.3% for the flat map. However, the accuracy of the flat map was slightly better (74.8%) than the accuracy of PARM (73.6%) for the urban terrain (University Park Campus, Nottingham). For the Lake District model, the PARM was more accurate and the response time was faster than the flat map for all types of backdrops maps and questions. For the campus model, PARM has higher accuracy for participants that have known the campus for less than 6 months, but the flat map was better for participants who have known the campus for more than 6 months. Another aspect of this study was to explore the accuracy of touch-based interaction with PARM which had been seen to be something viewers expected from previous studies, as reported in Priestnall et al (2017) a finger tracking program was proposed based on a modified algorithm from an existing program developed for the Microsoft Kinect sensor. The program was able to detect and record fingertip coordinates up until the point where the finger merged with the physical model, which was taken as the point of touch. The accuracy of fingertip detection was tested using 8 target points on each of the PARM models (Lake District and campus). Results showed a similar offset, averaged over 50 participants for both models, of 2.48 cm for the Lake District model and 2.58 cm for the campus model. The implications of this level of accuracy between the two models are discussed but generally speaking it was considered that this technological solution would not offer a satisfactory user experience.