Creating occupant-centered digital twins using the Occupant Feedback Ontology implemented in a smartwatch app

Abstract

Occupant feedback enables building managers to improve occupants’ health, comfort, and satisfaction. However, acquiring continuous occupant feedback and integrating this feedback with other building information is challenging. This paper presents a scalable method to acquire continuous occupant feedback and directly integrate this with other building information. Semantic web technologies were applied to solve data interoperability issues. The Occupant Feedback Ontology was developed to describe feedback semantically. Next to this, a smartwatch app – Mintal – was developed to acquire continuous feedback on indoor environmental quality. The app gathers location, medical information, and answers on short micro surveys. Mintal applied the Occupant Feedback Ontology to directly integrate the feedback with linked building data. A case study was performed to evaluate this method. A semantic digital twin was created by integrating linked building data, sensor data, and occupant feedback. Results from SPARQL queries gave more insight into an occupant’s perceived comfort levels in the Open Flat. The case study shows how integrating feedback with building information allows for more occupant-centric decision support tools. The approach presented in this paper can be used in a wide range of use cases, both within and without the architecture, building, and construction domain.

Special Issue on Semantic Web for Industrial Engineering: Research and Applications

“…The study emphasizes differentiating functions from their implementation methods, distinguishing between capabilities and capacities of a product, and elucidating their relationships within engineering systems. Donkers et al [7] propose a scalable method for acquiring continuous occupant feedback, integrating it with building information using Semantic Web technologies. The Occupant Feedback Ontology (OFO) facilitates semantic description of feedback.…”

“…Examples of these initiatives are the European project OntoCommons 1 (part of the Horizon 2020 research and innovation program), the global endeavors of the Industrial Ontologies Foundry (IOF), 2 the W3C Linked Building Data Community Group, 3 workshop series like FOMI (Formal Ontologies Meet Industry) 4 and LDAC (Linked Data in Architecture and Construction) 5 as well as digital twin-related initiatives like the UK National Digital Twin Programme (NDTP) 6 and the Building Digital Twin Association (BDTA). 7…”

Showcasing a digital twin for higher educational buildings: developing the concept toward human centricity

“…Gnecco et al claimed a human-centric digital twin approach by the means of wearable sensors and subjective survey data about indoor comfort (Gnecco et al, 2023). Donkers et al (2022b) presented a proof-ofconcept study where a method for assessing building occupants' experiences was evaluated. They collected data and feedback via a smartwatch application.…”

“…Given that digital twin technology is new to building design, construction, and operation, there are many challenges and research gaps yet to be discovered and resolved. What seems to be one of the major challenges is data integration (Donkers et al, 2022b;Hosamo et al, 2022b;Hosamo et al, 2023a;Arsiwala et al, 2023;Gnecco et al, 2023). This is especially relevant for commercial buildings that typically host several different types of building systems with different data formats, delivered by multiple system providers (Hosamo et al, 2022a).…”

The contextual information requirements for collection and use of occupant feedback in BIM-enabled FM

“…Other studies collected thermal comfort-related data (e.g. heart rate and body temperature) using smart watches along with occupant locations to integrate them with BIM (Abdelrahman and Miller, 2022) or visual, acoustic and air quality comfort data to integrate them with semantic web ontology (Donkers et al , 2022). Gray et al (2020) developed an application that collects user complaints and faults detected by the users along with the user locations.…”