A Passive System for Quantifying Indoor Space Utilization

Berry, Jaclyn; Park, Kat

doi:10.52842/conf.acadia.2017.138

Cited by 3 publications

(4 citation statements)

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“…Our second research question, related to improving the precision levels of known occupancy detection systems (Berry and Park 2017;Sun, Zhao, and Zou 2020), is addressed with an outline of a technique employing two already developed but not sufficiently tested indoor poisoning solutions (Abbas et al 2019;Kulmer et al 2017) to help manage occupation density and implementation of social distancing measures. The paper outlines how CTs would be used to compensate for sensing devices' insufficiencies and improve indoor positioning accuracy while maintaining a noninvasive approach.…”

Section: Discussionmentioning

confidence: 99%

“…Occupancy data is elusive and difficult to capture with high-level accuracy (Sun, Zhao, and Zou 2020). The proximity between occupants and dealing with a high number of people in the same room are particularly challenging (Berry and Park 2017). Inherent noise and occlusion in dynamic environments pose challenges for reliable occupancy measuring (Abbas et al 2019;Großwindhager et al 2017).…”

Section: Related Workmentioning

confidence: 99%

“…Their limitations are also related to dealing with multiple occupants. Previous studies involving these two systems (Abbas et al 2019;Kulmer et al 2017) and other research efforts related to indoor positioning (Berry and Park 2017) report challenges in establishing an occupant count when as little as three people are present in the same room.…”

Section: Computational Frameworkmentioning

confidence: 99%

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Indoor Positioning System for Occupation Density Control

Stojanovic,

Vujovic,

Miloradovic

2020

ACADIA Proceedings

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The reported research focuses on occupational density as an increasingly important architectural measure and uses occupancy simulation to optimize distancing criteria imposed by the COVID-19 pandemic. The paper addresses the following questions: How to engage computational techniques (CTs) to improve the accuracy of two existing types of indoor positioning systems? How to employ simulation methods in establishing critical occupation density to balance social distancing needs and the efficient use of resources? The larger objective and the aim of further research is to develop an autonomous system capable of establishing an accurate number of people present in a room and informing occupants if space is available according to prescribed sanitary standards. The paper presents occupancy simulation approximating input that would be provided by the outlined multisensor data fusion technique aiming to improve the accuracy of the existing indoor localization solutions. The projected capacity to capture information related to social distancing and occupants' positioning is used to ground a method for determining a room-specific occupational density threshold. Our early results indicate that the type of activities, equipment, and furniture in a room, addressed through occupants' positioning, may impact the frequency of distancing incidents. Our initial findings centered on simulation modeling indicate that data, composed of the two sets (occupant count and the number of recorded distancing incidents) can be overlapped to help establish room-specific standards rather than apply generic measures. In conclusion, we discuss the opportunities and challenges of the proposed system and its role after the pandemic.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Computational Frameworkmentioning

confidence: 99%

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Indoor Positioning System for Occupation Density Control

Stojanovic,

Vujovic,

Miloradovic

2020

ACADIA Proceedings

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“…Durmisevic et al (2001) address this by creating 'aspects' of design traits using the categories of attractiveness, wayfinding, daylight and physiology to quantitatively measure qualitative design elements. An alternative method by Berry & Park (2017) used sensory equipment to produce numerical data such as temperature and thermal comfort to rationalise the experience of architectural space. Some architectural contexts naturally lend themselves to rationalising qualitative experience such as sports architecture, where the success of geometric decisions in the design process is intrinsically linked with profit and therefore a machine-legible value of success (Joseph et al 2015).…”

Section: Methodsmentioning

confidence: 99%