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The eld of human building
interaction for convergent research
and innovation for intelligent built
environments
Burcin Becerik‑Gerber
1,24*, Gale Lucas
2,24, Ashrant Aryal
3, Mohamad Awada
1,
Mario Bergés 4, Sarah Billington
5, Olga Boric‑Lubecke
6, Ali Ghahramani
7,
Arsalan Heydarian
8, Christoph Höelscher
9,10, Farrokh Jazizadeh
11, Azam Khan
12,13,
Jared Langevin 14, Ruying Liu
1, Frederick Marks
15, Matthew Louis Mauriello
16,
Elizabeth Murnane
17, Haeyoung Noh
5, Marco Pritoni
18, Shawn Roll
19, Davide Schaumann
20,
Mirmahdi Seyedrezaei
1, John E. Taylor
21, Jie Zhao
22,23 & Runhe Zhu
1
Human‑Building Interaction (HBI) is a convergent eld that represents the growing complexities of
the dynamic interplay between human experience and intelligence within built environments. This
paper provides core denitions, research dimensions, and an overall vision for the future of HBI as
developed through consensus among 25 interdisciplinary experts in a series of facilitated workshops.
Three primary areas contribute to and require attention in HBI research: humans (human experiences,
performance, and well‑being), buildings (building design and operations), and technologies (sensing,
inference, and awareness). Three critical interdisciplinary research domains intersect these areas:
control systems and decision making, trust and collaboration, and modeling and simulation. Finally,
at the core, it is vital for HBI research to center on and support equity, privacy, and sustainability.
Compelling research questions are posed for each primary area, research domain, and core principle.
State‑of‑the‑art methods used in HBI studies are discussed, and examples of original research are
oered to illustrate opportunities for the advancement of HBI research.
Technology is rapidly advancing and, in doing so, is changing not only virtual realms but also our everyday
physical environments. Smart connected devices and advancements in sensing, actuation, and communication
are converging to bring new modes of interaction and experience within our built environment. is transforma-
tion is leading our society towards a dierent way of engaging with the spaces in which we live, work, play, and
OPEN
1Sonny Astani Department of Civil and Environmental Engineering, University of Southern California, Los Angeles,
USA. 2Institute for Creative Technologies, University of Southern California, Los Angeles, USA. 3Department
of Construction Science, Texas A&M University, College Station, USA. 4Department of Civil and Environmental
Engineering, Carnegie Mellon University, Pittsburgh, USA. 5Department of Civil and Environmental Engineering,
Stanford University, Stanford, USA. 6Department of Electrical and Computer Engineering, University of Hawaii
at Manoa, Honolulu, USA. 7Department of the Built Environment, National University of Singapore, Singapore,
Singapore. 8Department of Engineering Systems and Environment, Link Lab, University of Virginia, Charlottesville,
USA. 9Department of Humanities, Social and Political Sciences, ETH Zurich, Zurich, Switzerland. 10Future Cities
Laboratory Global, Singapore ETH Centre, Singapore, Singapore. 11Department of Civil and Environmental
Engineering, Virginia Tech, Blacksburg, USA. 12Trax.Co, Toronto, Canada. 13University of Toronto, Toronto,
Canada. 14Lawrence Berkeley National Laboratory, Berkeley, USA. 15Salk Institute for Biological Studies, La Jolla,
USA. 16Department of Computer and Information Sciences, University of Delaware, Newark, USA. 17Thayer School
of Engineering, Dartmouth College, Hanover, USA. 18Building Technology and Urban Systems Division, Lawrence
Berkeley National Laboratory, Berkeley, USA. 19Chan Division of Occupational Science and Occupational Therapy,
University of Southern California, Los Angeles, USA. 20Faculty of Architecture and Town Planning, Technion –
Israel Institute of Technology, Haifa, Israel. 21School of Civil and Environmental Engineering, Georgia Institute of
Technology, Atlanta, USA. 22Delos Labs, Delos, USA. 23Weitzman School of Design, University of Pennsylvania,
Philadelphia, USA. 24These authors contributed equally: Burcin Becerik-Gerber and Gale Lucas. *email: becerik@
usc.edu
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learn. Built environments of all shapes, sizes, and manifestations are becoming intelligent partners that handle
operational and repetitive tasks to support basic human needs, promote human health and well-being, and facili-
tate creative, intellectual, social, and emotional pursuits. Human-Building Interaction (HBI) represents the next
frontier in convergent research and innovation to enable the dynamic interplay of humans and intelligence within
a built environment. In this context, we dene a building’s intelligence as the overall awareness and respect for
the needs and preferences of humans and other stakeholders combined with the capabilities necessary to adapt
and respond in an environmentally conscious manner that enhances human health, well-being, and performance.
e HBI eld studies how humans perceive, interact with, navigate through, and spend time within built envi-
ronments and how reciprocating actions of humans and buildings can positively inuence each other’s behavior.
In this paper, we provide a vision for the convergent eld of HBI, intending to formalize critical principles,
methods, and outcomes. is paper builds on prior eorts to characterize the eld. Many authors have attempted
to dene HBI through the lens of Human–Computer Interaction (HCI)1–3 or by focusing solely on the temporal
constraints of HBI4. In a more concerted eort5, researchers in human factors engineering, HCI, and architecture
developed denitions, identied research directions, and established sub-agendas meant to guide the work of
the individual disciplines6 toward a shared mission and scope of work for HBI7. We were inspired to expand on
this previous work by establishing an even broader perspective that can promote both individual disciplinary
eorts and meaningful interdisciplinary research to advance eective HBI. is broad group of experts not only
included engineering, human factors, architecture, and computer scientists (within HCI and beyond) but also
involved perspectives from social, behavioral, health, cognitive and occupational scientists. Together, we provide
a shared mission and vision for HBI by establishing universal terminology, dening core dimensions, identifying
key research questions, and outlining approaches and methods. Using a broad interdisciplinary perspective to
shape the convergent eld of HBI can result in a more signicant impact by cultivating collaboration and coor-
dination among the scientic community that closes gaps in current disciplinary-specic research. is shared
language and collective vision set the groundwork for valuable disciplinary, interdisciplinary, and transdiscipli-
nary advancements in HBI that contribute to an improved fundamental understanding of and applications that
maximize how intelligent built environments interact with, adapt to, and support humans.
Consensus methods. A diverse group of 25 industry-based and academic scholars from various disci-
plines convened in a writing workshop meant to minimize isolation (which can be an issue in interdisciplinary
elds) and maximize cooperation (eliminating duplicate eorts across dierent domains) among individuals
interested in HBI. Research elds represented in the workshops included: built environment design, construc-
tion, and operations (including architecture and various engineering disciplines such as civil, mechanical, elec-
trical, environmental, and industrial engineering), energy, transportation, applied sciences, computer science,
articial intelligence and computer vision, communication and textual studies, information and communication
technologies, human factors, HCI, human-centered design (HCD), humanities and social sciences, cognitive
science, psychology, medical informatics, public health, environmental and occupational health, occupational
science, orthopedics, and rehabilitation.
e workshop was composed of four professionally facilitated online sessions, each 4-h long, held biweekly
from January 2022 to March 2022. Before and during the sessions, participants’ perspectives and ideas were
solicited using virtual “sticky notes” in the workshop-facilitation platform8 related to the denition of HBI, the
vision and scope of HBI, the research areas of HBI, and the development of this paper, including sections, title,
and appropriate publication venues. Participants interacted in several ways to share their perspectives, ideas,
and concerns, including verbal discussions in plenary sessions with all participants, oral discussions during
breakout sessions, post-hoc online meetings, and through Slack channels or email messages. Participants voted
on the various outputs generated by the group to identify, prioritize, and come to a consensus on each component
of this paper. All participants contributed to writing, editing, or commenting on this paper, primarily occur-
ring asynchronously between and aer the four workshop sessions. Participants contributed content specic
to their expertise and reviewed all content to ensure that the perspectives of their disciplines were represented
throughout.
In the following sections, we present the consensus outcomes of this HBI workshop, including a shared
interdisciplinary denition, vision, and impact for the eld; descriptions of the overarching areas and associ-
ated transversal dimensions of HBI research; and examples of research studies and methods within HBI. All
experimental protocols were approved by the respective universities’ ethic committees (University of Southern
California, Carnegie Mellon University, Rensselaer Polytechnic Institute, Technion—Israel Institute of Technol-
ogy). All methods were carried out in accordance with relevant guidelines and regulations and informed consents
were obtained from all participants.
HBI denition
Human-Building Interaction (HBI) is an interdisciplinary eld that aims to understand how built environments
aect human outcomes and experiences and how humans interact with, adapt to, and aect the built environ-
ment and its systems. At its core, HBI focuses on enabling built environments that can learn, adapt, and evolve at
dierent scales (individual building, community level, city level) to improve the quality of life of its users while
optimizing resource usage and service availability. As a result, HBI researchers and practitioners explore the
mutual impact between buildings and humans, observe how users interact with built environments, and design
technologies to support novel interactions in such spaces. Figure1 shows the critical elements of this denition.
Within HBI, we describe buildings as built environments created by humans to service our needs, such as
to ensure safety and comfort, improve the quality of life, and accomplish personal goals (e.g., work, play, relax,
sleep) and societal-driven goals (e.g., reducing energy consumption, recycling). e considered built environment
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may refer to a single building or numerous, interconnected, physically adjacent buildings that might create a
community, campus, city, or network of buildings. Here, we focus on buildings and their immediate external
surroundings but acknowledge that some work in HBI is more expansive in scopes, such as smart cities and
infrastructure. We also consider buildings to be complex systems that consist of, among other things, the building
envelope, heating, ventilation, and air conditioning (HVAC) systems, electrical systems, energy generation and
management, water and plumbing, lighting and daylighting, intelligent controls, entertainment, interior design,
furniture, and modes of movement in, out, and around the building. ese sub-systems actively and passively
interact with humans and, as a result, impact human experiences, interactions, activities, and other outcomes.
Interactions among humans and the built environment range from passive to active, with various direct
and indirect interfaces. Passive interactions aecting human users include adjustments to the building system’s
operations to accommodate the needs of the humans or minimize impacts on the environment. For example,
HVAC systems regulate the indoor environment’s temperature, humidity, and airow, impacting a human’s ther-
mal experience. Lighting/shading systems adjust light color and intensity, which could aect humans’ physical
and psychological well-being due to their impact on circadian rhythms9. Similarly, spatial conguration and
indoor environments are oen designed to meet the needs of activity, mobility, or other participation that can
aect building operation systems. For example, open-plan workplaces can encourage collaboration while walled
oces and meeting rooms provide privacy. Yet, each of these congurations can lead to inecient use of space
or ineectual operations by building systems. On the other hand, active interaction includes human action to
change building systems or the built environment. Operating windows and controlling heating setpoints via an
interface represent well-known forms of active interaction with building systems. Smoking cigarettes and cooking
represent indirect but active interaction with the built environment that can, in turn, impact human outcomes
and experiences and trigger reactions from building systems, such as safety sensors triggering an alarm when
detecting smoke. In the context of HBI, such passive and active interactions are facilitated through intelligent
interfaces10 and sensing11 and the building becomes aware of human attitudes, needs, habits, norms, motivations12
and chooses to learn, adapt, and evolve for best human outcomes and experiences13–15.
Individual and collective human interactions with buildings can take many dynamic forms across the passive-
active interaction spectrum. Humans and buildings can interact with one another through new and adaptive
multimodal interfaces enriched by sensing and computing16, 17, including the use of motor functions, such as
speech, gesture, and navigation18. Yet, these interactions are inherently dierent than the ones in the eld of
HCI, where there is a dened mode of interaction (a computer), oen through hardware explicitly created for
the interactive purpose. In buildings, this interaction is facilitated through embodied and built-in intelligence
within building elements2, 5 that may not be initially designed for intelligent interaction, as well as other tech-
nologies added to the environment (e.g., windows, doors, building furniture like desks or chairs, light switches,
thermostats, smart paint19, 20). Not only is the signicant range of available features relevant to HBI widely varied
in design, but these features are also typically multifaceted in function and application. For example, interactions
Figure1. Primary HBI research areas, intersecting interdisciplinary research domains, and core principles of all
HBI research. Image credit: Basma Altaf.
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can target many physical and sensory outcomes (e.g., thermal, and visual comfort21–25, satisfaction26–32, health33),
and one person’s interaction with a building can easily inuence many others34.
HBI vision
We envision that the eld of HBI will enable a future where widespread synergistic relationships between humans
and built environments exist to support individuals and advance broader societal goals. is vision involves the
study of humans not solely as occupants, end-users, or homogenous actors within a built space but as individu-
als with unique lived experiences, personalities, capabilities, and goals. HBI research aims to support positive
engagements as these diverse individuals participate in activities within and around the built environments in
which they live, work, and play. By understanding human attitudes, motivations, habits, and norms and account-
ing for occupants’ safety, security, and privacy, HBI seeks to engage buildings with occupants to produce better
personal and societal outcomes. Similarly, HBI involves studying built environments not only as physical spaces
and operational systems but as the foundation for technologies that can sense and infer the needs of, provide
support to, and interact with humans. As such, the eld of HBI is interested in enhancing both new and existing
physical spaces and being deeply engaged in supporting the future of intelligent built environments.
HBI research dimensions
As the foundation, HBI scholarship merges three primary research areas related to humans, buildings, and
technology, each rooted in the scholarship of individual disciplines across social, behavioral, health, building,
and computer sciences: Human Experiences, Performance, and Well-being; Building Design and Operations; and
Sensing, Inference, and Awareness. Intersecting interests between pairs of these research areas give rise to three
interdisciplinary research domains: Trust and Collaboration, Decision-making and Control, and Modeling and
Simulation. At the center of all HBI research are core principles such as Equity, Privacy, and Sustainability that
should be incorporated or considered in every aspect of HBI scholarship. In this section, we provide descriptions
of these research dimensions to establish a shared language for the interdisciplinary eld of HBI.
Primary research areas in HBI. Human experiences, performance, and well-being. Research on technol-
ogy integration into buildings has focused chiey on top-down technology developments that largely neglect
the dynamics of dierent building users with diverse preferences and needs35. Human aspects of HBI research
should consider the impact of an environment and its constituent elements on human’s physiological and psy-
chological experiences, activities, and performance across a spectrum of short and long-term horizons. Most
of our daily activities take place inside buildings used as homes, workplaces, schools, retail stores, healthcare
facilities, and other social or business spaces. Signicant opportunities exist to employ HBI approaches that en-
able a seamless connection between users and buildings to support positive human experiences. Buildings that
produce an ongoing sense of fulllment5, 36, even during mundane activities37, will require an ambitious human-
centered focus to dene the goals of engineered systems that promote a wide variety of human experiences,
such as comfort, satisfaction, convenience, health, well-being, safety, lifelong learning, communication, social
engagement, and productivity. Within our HBI infrastructure, we imagine a future in which buildings become
perceptual and cognitive environments, encompassing both users and the physical infrastructure that shapes
and supports human intent, perception, and behavior. In such a cognitive environment, humans are integral to
the system rather than a variable to work around38–41.
ere are signicant opportunities for the built environment to aect humans and for humans to impact built
environments, represented by research focused on human experiences and responses. Such studies have quanti-
ed and modeled the impact of comfort on human satisfaction and performance26–32 or have investigated con-
tributors to the physical, mental, social, and spiritual health and well-being of building occupants33. In addition
to examining occupant or building-focused outcomes, HBI research can also investigate complex intersections
between human behavior and these individual or shared outcomes. In a well-studied HBI example problem,
behavioral scientists have pointed to an energy eciency gap between the design intent of technologies and their
realized performance in actual built environments42, primarily due to human behavior. Research in this area
investigated, among others, the drivers of human behavior (e.g., norms, attitudes)12, social practices that impact
energy use43–45 and interventions to produce behavioral change46–48. is example highlights the importance of
understanding the complex intersections among diverse, individualized human experiences and human behavior
throughout daily activities as these complexities inform, shape, and inuence the built environment.
Interdisciplinary collaboration among social, health, and behavioral scientists with architects, engineers, and
computer scientists is critical to advancing the theoretical and practical impacts of HBI research49. A primary
example of such collaborations is the study of energy feedback50 and user interfaces for energy devices51, which
stemmed from the interface between social science and HCI. ese interdisciplinary collaborations are further
facilitated by developing more granular and human-centric sensing technologies. Technologies that can iden-
tify occupant activities, emotional states, and needs can be used by intelligent built environments to support
engagement, well-being, and performance. Moreover, these technologies can provide the means for a synergistic
or cooperative approach between the building and occupants to inuence synergistic behaviors by both sides
that can achieve individual and shared goals. It is vital that future research leverage this synergistic perspective
to understand how humans and building technologies ascribe shared value. Synergistic approaches are critical
as AI begins to automate tasks previously under the occupant’s control (e.g., opening blinds, preparing coee,
updating thermostat schedules). Although such actions by the built environments are oen meant to support
the user or address a broader human-dened objective (e.g., reducing energy), some humans may value personal
control over other benets49, 52. As such, interdisciplinary approaches that investigate the distributed interaction
and agency across all the human users and building technologies are vital.
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Building design and operations. Whether it is designed to be a smart built environment from the outset or may
be augmented/retrotted to include technology aspects, the physical building and its operations are critical com-
ponents of HBI. Disciplinary knowledge within architecture and various engineering elds such as mechanical
engineering, electrical engineering, architectural engineering, civil engineering, and building science provide
a strong foundation for HBI research. Research on building processes and practices over the last decade has
led to innovations in design methods53–55, engineering systems such as HVAC equipment and appliances56, 57,
lighting58, 59, building materials like glazing systems60, 61, construction processes62, 63, and building management
and controls64, 65. In HBI, it is important to move beyond these building-centric foci to leverage sensed aware-
ness of the needs and behaviors of their occupants so that user experiences and outcomes are optimized against
oen-competing external variables such as energy availability, extreme events (e.g., wildres and public health
concerns), weather events, schedules, or the needs of other users.
Incorporating social, behavioral, health, and computer scientists in HBI research will ensure that the dynamic
behavior of building occupants is accounted for in the design process and day-to-day operations of physical
buildings. Human-centered design approaches enable the creation of environments that support evolving needs
of their occupants. One such approach is cognitive-grounded design, which is based on human perception and
cognition research. Using this approach, one study examined how people process and respond to spatial and
social stimuli to inform design decisions related to waynding scenarios66. Another example is biophilic design,
which includes the design of spaces that reect the innate anity humans have for nature to support human
well-being that incorporates considerations such as access to natural light, views of nature, and use of natural
materials67. Similar approaches in HBI research could explore how to best connect built environments to com-
munities, cultures, and contexts, including considerations for designing the space where the building meets the
external urban or rural area68.
From an operations perspective, AI-driven adaptations of built environments include changes in building
operations or features in response to occupants’ short- or long-term needs. ese operations include physical
environmental components such as adequate lighting, optimal indoor air quality, and thermal comfort, as well
as human performance considerations such as promoting social engagement, productivity, and safety13. In the
eld of HBI, building operations should consider incorporating technology and human performance in day-
to-day resource management, such as for the improvement of HVAC and other physical building operations, as
well as improving waynding activities to manage eciency in human mobility within busy environments18. It
is also vital that HBI research consider support for building operations for extreme scenario responses such as
re accidents69 or violent attacks70.
Sensing, inference, and awareness. Technology aspects of HBI research incorporate the primary areas of sens-
ing, inference, and awareness that underpin actuation, communication, and other interfaces with human and
building components of the HBI research infrastructure. Interdisciplinary research in these areas falls at the
intersection of computer science and other areas of computing and engineering, including civil engineering,
mechanical engineering, electrical engineering, and industrial and system engineering.
Sensing technologies include various physical environmental devices, personal or portable wearable devices,
and sensor deployment strategies. HBI research has examined numerous environmental sensors that can
quantify human activities, experiences, and performance, and monitor changes within the physical or social
environment16, 71–73. Internet of ings (IoT) provides a way to connect building operations such as security
and access control, predictive maintenance, structural health, re detection and so on74. Similarly, wearable
sensors that measure physiological variables, such as skin temperature, perspiration rate, and heart rate, can
assist in understanding human health and well-being related to engagement within built environments31, 75.
ese methods rely on sensing technologies that provide robust data throughout daily activities that require the
direct engagement of the human or building. On the other hand, non-intrusive technologies including infrared
thermography32, 76, optical imaging30, 77, 78, ambient sensing17, and real-time visual perception using human eye
pupil size measurements79 have been proposed as alternative non-contact methods to evaluate human experi-
ence within built environments.
Regardless of the sensing method, data from these systems can be used to infer higher-level knowledge, such
as recognizing human presence, activities, and interactions, as has been done in activity recognition research80–86.
Reasoning out broader constructs can be achieved through direct inference from data gathered by the sensing
hardware or through virtual sensing supported by inference algorithms87. Using these methods, intelligent build-
ings can develop, calibrate, and rene models of human occupants to infer the emotional state, physical abilities,
or social dynamics of individual occupants or groups to create opportunities for buildings to interact with people
more appropriately. For example, if an intelligent building infers that someone is operating a wheelchair, it could
recommend alternative paths that accommodate their needs. In an emergency, when inferring relationships,
such as a mother holding the hands of young children, a more appropriate egress path may be presented to the
mother that accommodates the children’s abilities. Alternatively, a building could infer the emotional state of its
occupants and operate the systems accordingly.
When captured over time or across a network of buildings, sensing and related inference data can be aggre-
gated to develop activity patterns, thus giving the building occupant-awareness. Broad awareness of human needs,
environmental needs, or other aspects of social context can allow the building to leverage predictive functions to
maximize support required in the environment while minimizing disruptions to building operations or human
engagement. Given this awareness, when an intelligent environment is aware that it currently is not supporting
and cannot fully support the needs of occupants, it can recommend optimal changes or improvements to the
owner/operator of the building to better meet social needs and environmental goals.
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Overarching research opportunities. Current studies mainly consider built environments as static containers
rather than proactive partners. New HBI research opportunities involve analyzing the collaborative interactions
between humans and intelligent buildings equipped with sensing, inferring, and communication abilities. More
human-aware and cognizant building design and control systems could inform dynamic building adaptations in
response to and anticipation of people’s emerging needs14. For example, intelligent environments equipped with
sensing and awareness technologies could potentially consider the dynamic state of the building and its occu-
pants at a given time to recommend optimal resource allocation scenarios, including spaces, people, equipment,
and energy services. Research can support such involvement in day-to-day operations, in response to unfolding
situations, and in predicting and preventing undesired situations. ese approaches can simultaneously improve
human engagement while considering the building’s impact on its occupants and broader impact on the natural
environment. Table1 summarizes research needs across these three primary research areas in the form of sample
research questions.
Interdisciplinary research domains in HBI. ree interdisciplinary research domains sit at the intersec-
tions of the primary research areas in HBI (see Fig.1). Trust and collaboration at the intersection of human and
building aspects of HBI include human-building teaming, synergistic human behavior and building operation
changes, and other actions that require trust and collaboration to support shared goals between the building and
its occupants. e domain of decision-making and control includes topics at the intersection of the technology
and human aspects of HBI, such as occupant-centric controls, context-aware operations, intelligent automa-
tion, bidirectional adaptation, articial intelligence, machine learning, and data analytics. Finally, the interdis-
ciplinary research domain of modeling and simulation includes topics at the intersection of the building and
technology aspects of HBI, such as physical and digital system adaptations, design intent, design intervention,
computational modeling, and predictive maintenance. Table2 summarizes research needs pertaining to these
interdisciplinary research domains within HBI.
Table 1. Sample research questions related to human, building, and technology aspects of HBI.
Human experiences, performance, and well-being
How can built environments augment user performance (such as productivity or safety)?
What are the eects of HBI on joint human-automation performance?
How do built environments’ design and operations passively and/or actively impact occupants’ health and well-being?
How can interfaces in the built environment actively “nudge” occupants towards adopting changes for healthier behavior, and how can
“nudging” be converted to active awareness and participation in positive actions among the occupants?
How can psychological, social, and economic theories help building systems and AI to enhance occupant experience, performance, and
well-being?
What role does AI play in addition to humans in making decisions about building operations (e.g., should the AI system be in charge based
on predetermined human requirements or should the human be in charge based on AI suggestions?)
How might built environments help humans, families, and organizations achieve their goals?
Building design and operations
What is the dierence between designing non-intelligent buildings vs. intelligent ones that are aware and proactively cater to the needs of
their occupants?
What approaches could be used to perform cognitive-grounded analyses of the collaborative interactions between humans and intelligent
buildings equipped with sensing, inferring, and communication abilities?
How might we design spaces that are intuitive to navigate for humans with dierent physical and cognitive abilities both in the day-to-day
and emergency scenarios (e.g., to help their occupants to shelter during an active shooter incident)?
How could natural elements be seamlessly integrated into the building design process to enhance the well-being of dierent populations over
time?
How can AI-driven adaptations of built features be programmed to respond to and anticipate occupants’ short- or long-term needs?
What are the opportunities and challenges for incorporating automated operational adaptations in existing buildings vs. building intelligent
adaptation methods into new designs?
To what degree does enabling occupant awareness and interactivity in built environments promote low-energy and low-carbon building
operations?
Sensing, inference, and awareness
What are optimal modalities and congurations of sensors that result in high accuracy and robust data collection and inference in a privacy-
preserving and unbiased manner?
What sensor modalities are needed to be added to the mix of Internet of ings (IoT) devices to facilitate more ambitious goals in the HBI
eld?
Given the combination of xed and dynamic sensors within the built environment, with a varying set of IoT devices used by occupants, how
can a sensor fusion system best normalize the data features to support higher-level functionality such as activity recognition?
How could buildings infer and quantify each specic occupant’s habits, experiences, and social interactions through the emerging sensing
technologies?
How could a general graph of human activities be generated and populated given myriads of sensor fusion results from various building
types? How could the social, temporal, and semi-hierarchical nature of the relations between human activities be represented?
What representations could be used to develop an awareness of human needs, social contexts, and environmental needs so that levels of
compliance to those goals can be estimated? How can recommender systems be developed to automate the suggestion of optimal modica-
tions to increase compliance?
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Trust and collaboration. When the built environments become infused with intelligence, there will be new
opportunities for human-building teamwork that calls for collaboration and trust. Prior interdisciplinary work
has considered factors that encourage trust in and collaboration with automation for various tasks (e.g., in man-
ufacturing, aviation88, 89). ere is a developing empirical body of research for trust in automation for intelligent
built environments, which requires collaborations among many disciplines, including architecture, cognitive sci-
ence, engineering, computer science, and human factors and industrial-organizational psychology experts. With
more intelligent buildings1, there will be an increased level of automation based on the type and function of the
environment88. In addition, new modes and types of interaction will be needed to fulll the goals of both humans
and the built environment. For built environments to develop the intelligence required for collaboration, users
need to trust the environment with data acquisition, processing, storage, and decision-making. Ambitious HBI
goals also require user trust in accepting the actions, decisions, and recommendations from the built environ-
ments’ features. Research on trust could be built on similar research eorts involving the privacy and security
of intelligent systems and environments, resulting in a better understanding of how users and communities
perceive the role and functionalities of such systems. Additionally, it is necessary to consider the implications of
trust based on how the sensing, inference, and awareness solutions support goals and objectives that align with
those of dierent stakeholders. For example, the goals of building occupants may be counter to those of building
managers/owners, who could lead reduce trust among various stakeholders. Finally, building design and opera-
tions might have an impact on trust and collaboration. According to the literature, layout design and nature
stimuli can provide a sense of trust for occupants. For instance, physical proximity promotes collaboration,
bonding and trust89, and trust bond among residents of a place can be associated with the sense of satisfaction
about sharing environments90. In this regard, open-plan oce environments with assigned work spaces help
participants foster trust and respect among each other91. On the other hand, shared work environments, and in
particular hot-desking, are associated with distrust in work environments92. As another example in regard to the
biophilic design of buildings, exposure to more beautiful images of nature led participants to be more generous
and trusting in comparison to exposure to less beautiful images of nature93.
Decision-making and control. As a classical HBI framework, occupant-centered controls in buildings aim at
improving occupant satisfaction and perceived service quality (e.g., thermal and visual comfort) while account-
ing for sustainable practices (e.g., minimizing energy consumption94 or integration of renewable resources68). A
wide range of approaches have been explored, from simple presence-responsive systems (e.g., lighting triggered
by an occupancy sensor) to complex predictive techniques (e.g., model predictive controls) based on human
activity and preferences95. Challenges remain in the development of decision-making and control models that
Table 2. Sample research questions related to interdisciplinary research areas of HBI.
Trust and collaboration
How might we better design novel interactions that increase trust and collaboration between built environments and their users?
How will these interactions change the behavior of humans and built environments?
How do, and what type of, interactions with building structures, systems, and operations inuence how humans perceive, experience, and
use spaces?
What types of user interfaces and modes of communication promote eective collaboration and trust to advance shared goals between the
building and human occupants?
How does the level of automation aect trust in technology?
How does trust in technology change based on users’ tasks in built environments?
How will novel HBIs change people’s expectations of the built environment?
How will the type of collaboration aect the way that humans use the built environments?
Decision-making and control
How will control systems respond to a diversity of human needs?
Should a bottom-up approach (intelligence growing from individual smart devices) or a top-down approach (a smart central building
manager) be taken? Or is it this goal dependent, and for some goals, the system should consider a top-down approach, and for other goals, a
bottom-up approach might be a better option
How do users react to intelligent and aware spaces or building (e.g., buildings that dynamically adjust the color temperature according to
user activities or building geometry support users)?
How could smart systems be developed to be scalable and transferable?
Modeling and simulation
How can we generalize and incorporate well-being considerations into design tools (e.g., Building Information Modeling)?
Which physical and digital adaptations are most eective for specic populations/cultures/building styles?
How can the physical layout be designed to reect the organizational structure, levels of sharing, and visibility (physically and conceptually)?
What are the tradeos between physical materials and digital representations?
How can computational models of human behavior account for the building occupants’ cultural, physical, and psychological traits?
How can generative design approaches account for designers’ tacit knowledge?
How can Machine Learning (ML) and Deep Learning (DL) approaches replace or integrate traditional Multi Agent Systems (MAS), which
oen require long computational times?
What is the role of digital twins in HBI research? How can future building digital twins preserve building occupant privacy?
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can identify user routines and patterns, predict shiing or dynamic behaviors, and infer preferences to support
user-adaptive building operations. Interdisciplinary HBI research is necessary to identify more granular and
accurate inference across routine and dynamic human behaviors, diverse human preferences, and various emo-
tional and physical states across all human occupants. Improved inference, informed by algorithmic frameworks
and enabled by new sensing modalities, will lead to better decision-making and control to optimize building
operations, improve the experiences among diverse users within individual buildings, and balance such needs
against external variables. Building-level decision-making, control, and awareness can provide a foundation
for broader system-level operations, such as interconnections among buildings and the surrounding neighbor-
hoods across communities or within more extensive infrastructures. To this end, future interdisciplinary HBI
research is necessary to identify human-centered AI solutions that are scalable, sample ecient, and safe. It will
be essential to develop studies of building-level intelligent agents that are informed not only by local objectives
and constraints but also by the intelligent system’s role in larger-scale coordination. System-level operations
might include community-based energy management for demand-response and renewable energy integrations
or smart building-city coordination to inform and enhance rst responders’ response to emergencies.
Modeling and simulation. Architects and engineers have long used digital representation tools to model
building features, evaluate features relative to design intentions, and enhance communication across design
stakeholders96 such as Building Information Modeling (BIM)97. BIM, however, lacks information about the
behaviors of building occupants as well as their interactions with the built environment98. Since these aspects are
at the core of HBI, it is thus imperative to extend existing modeling tools and practices with this new emphasis.
To this end, a new research area involves the computational simulation of human behavior in buildings using
multi-agent systems (MAS). In MAS, prospective building occupants are modeled as agents that interact among
them as well as with the surrounding built environments to goals related to the type of organization that occu-
pies the building99. ese approaches have been applied to model operational performance in process-driven
facilities like hospitals100, waynding in multi-level buildings101, occupancy patterns in university campuses102,
thermal and acoustic comfort in oce spaces103, and emergency evacuation scenarios104. Generative design
approaches also show promise in coupling human-centered evaluation metrics with parametric models of build-
ings to automatically generate, sort through, and evaluate several design options and recommend the ones that
balance tradeos between building and human performance metrics105. is approach requires interdisciplinary
research eorts that bring together architects, engineers, social scientists and building scientists. BIM models
have also been coupled with real-time sensing technologies to dynamically update a digital representation (oen
called a Digital Twin) of a building or its parts/systems106. Digital twins of the built environment provide a foun-
dation for real-time evaluation of building systems and data-driven forecasting of future performance. Compu-
tational simulation of building performance could also be incorporated into digital twins to inform real-time
decision-making in day-to-day and emergency scenarios. Interdisciplinary HBI research could use the existing
approaches as a starting point to develop more comprehensive tools and techniques that better inform the design
and operational adaptations at dierent time scales while also accounting for more detailed occupant proles,
which include cultural, geographical, psychological, physical, and economic traits, as well as passive and active
interactions with intelligent control systems.
Core principles in HBI. Leveraging interdisciplinary perspectives, the eld of HBI emphasizes greater
understanding and enabling of positive two-way interactions between humans and built environments that
include passive solutions while promoting active engagements. By supporting bi-directional human-building
synergies and promoting responsible innovation (similar to calls in HCI107), successful HBI eorts can result
in meaningful changes to the design, operation, use, and assessment of built environments that have profound
eects on our lives, our society, and our environment. Among many areas of broad impact, thoughtful HBI
research should be conducted with consideration of core principles that reect contemporary societal goals: (1)
promoting equity and inclusion of individuals who engage within and around buildings, (2) addressing evolving
concerns of privacy and security related to the increased use of technologies within buildings, and (3) support-
ing sustainability and resilience in the face of environmental, social, or other hardships (e.g., disaster response,
homelessness). Table3 summarizes the research questions pertaining to the core principles of HBI.
Equity and inclusion. HBI research must consider diversity across the many types of individuals who occupy
or use buildings to promote accessibility, inclusion, and equity in applying technological supports. ere is a
long-standing underrepresentation and consideration of user diversity in building-related research that includes
many marginalized groups and minorities108, 109. In the study and development of HBI, there is a need to under-
stand dierences in engagement and support required across dierent genders, age ranges (i.e., including chil-
dren, youth, and older adults), and socioeconomic statuses110. Moreover, HBI solutions and technologies should
determine how support is equitable and inclusive for individuals from communities of color and people with
varied physical abilities or mental health conditions111, 112. Finally, there is an opportunity to expand HBI eorts
to support the needs of displaced groups, migrants, and unsheltered individuals113. HBI professionals can lead
standards that encourage inclusive behavior and recognize ongoing exclusions and societal biases to ensure that
diverse populations are represented in user studies and in testbed or real-world deployments. Applying a diver-
sity lens within research practices is a signicant consideration to avoid bias inherent to humans, which can be
inadvertently carried forward into the resulting technology109. Universal and accessible design is necessary to
support an inclusive culture and is key to ensuring that built environments support diverse human needs and
eective activity engagement108. HBI technology has the potential to maximize the adaptability of design for
individuals with diering physical, social, and sensory abilities114. Although many HBI innovations will be tech-
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nologically advanced, HBI eorts should also consider applications for economically disadvantaged regions and
low-tech spaces. In fact, because low-tech or no-tech built environments constitute most of the current building
stock, the extrapolation and adaptation of innovations from high-tech built environments is an opportunity for
the eld of HBI. Across all opportunities, it is vital for HBI researchers to carefully consider the source of data
that informs automated decision-support systems to reect the knowledge, opinions, and needs of broad, diverse
communities to ensure that outcomes promote equity and inclusion.
Privacy and security. Privacy is a core component of any socio-technical system that collects personal data
from its users, and the security of that information is vital, particularly when simultaneously incorporating
and sharing information across multiple data sources. Physical spaces in which humans engage have inherent
privacy and security concerns related to exchanging information (e.g., meetings) and other sensitive activities
(e.g., physical examinations in healthcare settings). As we move towards more intelligent, aware, and adaptive
buildings, it is critical to responsibly manage captured data, especially highly personal and potentially sensi-
tive, stigmatic, and exploitable health-associated data. One current example is the rapidly expanding number of
distributed IoT devices monitoring building applications (e.g., indoor environmental quality23, 29, 78, 115) that are
connected to cloud services. is increase in cloud computing is paralleled by an increase in security vulner-
abilities and concerns of inappropriate use of data obtained by sensing devices. For example, managers gaining
access to cloud-sourced behavioral data of their employees as a means for monitoring performance could lead
to employment termination. HBI research should seek to unify and extend current research on security- and
privacy-related issues such as smart grid security116 and IoT security117. Dierential privacy118 and federated
learning119 are technical solutions that may be viable solutions to accessing aggregated individual data that pre-
vent access by entities with power over the individuals. Furthermore, increased use of human-centered, par-
ticipatory design, and generative techniques120 are promising ways to advance the security and privacy aspects
of HBI by identifying user concerns and accounting for individual preferences. HBI research should consider
providing personal user control over privacy settings, identifying policy solutions, and developing new instru-
ments for appropriate data stewardship, such as data trusts121. Timely incorporation of privacy and security as
considerations in HBI research can ensure eective sharing of data across third-party building applications and
between buildings on dierent networks while maintaining the trust, safety, and security of occupants.
Sustainability and resilience. HBI research has an opportunity to move beyond satisfying basic requirements
of shelter, safety, and security toward serving broader societal needs such as environmental sustainability, cli-
mate resilience, and societal well-being. Building operations are one of the largest sources of global greenhouse
gas emissions, with heating, cooling, and water heating comprising most energy-related emissions, particu-
larly in existing built environments. Resource consumption and emissions from built environments are strongly
determined by occupant service requirements (temperature, lighting, etc.) and usage patterns. erefore, HBI
research must leverage a deep understanding of these service needs and usage patterns to develop fundamentally
sustainable and adaptive design and operation solutions for the built environment that substantially mitigate the
risks of catastrophic climate change. e built environment also serves as a primary means of adaptation to the
climate crisis and its potential eects on personal comfort, health, and happiness. Even as we work to prevent
the disastrous eects of climate change, resilience to these unavoidable events is a must. ese impacts oen
unequally aect communities of color and low-income, emphasizing the necessity for equitable and proactive
Table 3. Sample research questions related to core principles of HBI.
Equity and inclusion
How could universal and inclusive design concepts be integrated with the theoretical disciplines and practical applications of HBI?
How can the dynamicity of interactions between occupants and buildings enable us to consider the specic needs of some groups of societies
that are more susceptible to features of physical environments?
How can we include and adapt to the needs of dierent groups in the design and implementation of HBI-based solutions?
How can we rethink making low-cost HBI solutions to extend the usability of these solutions among the public?
Privacy and security
What new security and privacy concerns do device-human-building interactions raise?
Can we develop new adaptive and exible control schemes to secure an expanded dynamic, diverse, and heterogeneous environment with
good user experience and application performance?
How do we address the issues raised by more sub-systems being added into buildings or conicts between dierent application require-
ments?
What are the tradeos between security/privacy and application performance or user experiences?
How can building systems respond to personalized privacy proles?
Sustainability and resilience
How might we balance conicting objectives and tradeos of comfort, well-being, energy and CO2 reductions, and resilience?
How do we move beyond individual buildings and coordinate buildings at a neighborhoods or city scale?
How do we plan for the inevitable need to renovate, retrot, and retire buildings?
How could HBI technologies and solutions lead to increased resilience and operational exibility of buildings and communities?
How could HBI research assist with resilience towards extreme events due to climate change such as wildres, heat waves, ooding, etc.?
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resilience planning and resource allocation122. For instance, the city of Phoenix in Arizona, has developed a
heat action plan that relies on natural cooling techniques to cool the neighborhoods most in need123. In another
example, the city of New Orleans is prone to severe storm events that include hurricanes and ood waters.
erefore, the local authorities issued guidelines for storm preparedness and resilience that include regular
maintenance and basic structural improvements strategies124. To that end, proponents of the eld of HBI should
be not only the stewards of our environment but also human health and well-being. Technology’s harmful and
unintended impacts on human psychology should be carefully considered. is is even more important with the
blurring lines of work and home life with the increase in hybrid work125, 126; thus, there needs to be accountability
for human well-being, safety, comfort, and productivity in HBI-enabled spaces, and HBI innovations should
start with a human-centered purpose to inuence the development of technology that leads to sustainable and
resilient societies.
Research and practice in HBI
HBI researchers conduct studies in several high-level areas, including (1) discovering and quantifying the pas-
sive interaction that impacts users’ personal experiences and outcomes from the built environment and building
systems, (2) observing and learning human behaviors in built environments to quantify the active actions and
their impact on the built environment and building systems, as well as potentials for technology adoption and
success, and (3) designing, building, and evaluating new technology that allows built environments to become
more exible and more responsive to human needs over their operational lifetime. is section provides an
overview of methods and practical applications for HBI research. We then present examples of original research
in HBI that reect the dierent transversal themes outlined above.
HBI research methods. Research approaches are adopted or adapted from the multiple disciplines and
research elds that compose the interdisciplinary eld of HBI. Design, development, testing, and user experi-
ence research within HBI employs both qualitative (e.g., focus groups, interviews, ethnographies, narratives)127
and quantitative (e.g., randomized control trials, experimental studies, observational measurement, surveys)
research methods. A variety of AI techniques, optimization methods, including supervised learning (regres-
sion, classication, deep learning), unsupervised learning (dimensionality reduction, clustering, anomaly/event
detection), and reinforcement learning techniques, are used for predictive modeling, inference, and control. HBI
research is conducted in lab-controlled environments30, through eld studies in existing built environments15
and systems128, and by the use of immersive virtual reality environments (virtual prototyping)129. Simulation
is frequently used to understand the impact of HBI technologies and evaluate algorithmic frameworks when
human data is unavailable or to develop mathematical models that can be tested against human data/observa-
tions65, 94.
Examples of existing HBI applications. Multiple examples of robust HBI applications exist. For exam-
ple, the Cortellucci Vaughan Hospital in Ontario, Canada, opened in 2021, uses real-time locating systems to
track assets and monitor patients’ locations and movements. e hospital also relies on an integrated smart tech-
nology grid to maximize information exchange, allowing for ecient logistics management, medical sta work-
ow, and a pleasant patient experience130. e EDGE building in Amsterdam, Holland, is considered one of the
most intelligent buildings in the world according to the British rating agency BREEAM131. e commercial oce
building was established to make its users’ work experience as smooth as possible. e building knows workers’
schedules, assigns the most convenient parking spot upon arrival, and customizes the light and temperature
of their workstations according to personal preferences132. Some Target retail stores in the United States have
advanced LED light xtures with visible light communication (VLC) capabilities. When partnered with Visible
Light Positioning (VLP)133 of image sensors on smartphones, these LEDs allow customers to use the Target app
to navigate around the store to a specic product location134, 135. Smart residential buildings equipped with vari-
ous smart home gadgets (e.g., smart video doorbells, stove controller, smart pillbox, automated emergency call
system) provide a comfortable, safe, and secure space, allowing seniors to remain in their homes136. Recently, big
tech companies have created smart home hubs (e.g., Amazon Echo, Google Home, and Samsung Smartings),
which act as a central house monitor controlling all devices at home and creating a more comfortable living
environment for seniors137. Finally, during the recent COVID-19 pandemic, the Dubai airport installed smart
gates that use the traveler’s face and iris biometrics to reduce passport control procedures and increase security.
Airport ocers were not required anymore to scan, examine, and check the passports of every traveler, which
reduced the human–human interaction during the pandemic and promoted eortless travel138.
Examples of novel applications, proof‑of‑concepts, and test cases of HBI. As the examples above
demonstrate, HBI-centered applications have achieved a maturity level that supports the implementation of
automation and service delivery through increasingly available commercial products. State-of-the-art research
is being conducted to push the envelope and contribute to more advanced use cases. Examples of research across
selected areas of the HBI framework follow.
Research example in human experiences, performance, and well-being. With the outbreak of the SARS-CoV-2
virus, stress levels have increased across the globe, to the extent that building professionals consider stress among
the most important mental well-being issues that need to be the focus of design, construction, and operation of
buildings139. While there are many reasons for stress, one of the top stressors is work pressure. Research suggests
that when workers have low control over their job demands (e.g., unfamiliarity with task, limited resources,
degraded workspace conditions, etc.) they develop negative stress named distress, which have detrimental
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psychological and physical health consequences and leads to degraded productivity. On the other hand, when
workers feel condent about handling their work stressors, they develop a feeling of eustress known as the posi-
tive stress, responsible for motivating people to pursue their goals and to face challenges. us, dierentiating
between eustress and distress is a necessity for work organizations to promote eustress and limit distress among
their workers. HBI plays a key role in this innovative research; for example, integrating the appropriate sensors
(e.g., wearable sensors, cameras, etc.) into an oce workstation sets the foundation for automated systems that
rely on physiological and behavioral data to identify whether a worker is witnessing eustress or distress. is
helps work mangers shape a strategic plan to assign workers with the appropriate tasks thus sustaining eustress
and proactively eliminating distress. In a recent research eort, physiological, behavioral, and human–computer
interaction data from 50 oce workers have been collected and used to dierentiate distress from eustress using
machine learning methods (Fig.2). Furthermore, nature contact has been shown to reduce distress among oce
workers, therefore the HBI research about stress must investigate the appropriate approaches to deliver nature
related interventions to restore distressed oce workers to a relaxed state. Finally, unpleasant indoor environ-
mental quality and oce interior design have been found to increase workers’ physiological stress, therefore
HBI researchers should study the means to create personalized responsive oce conditions that can change its
ambiance driven by the physiological state of workers.
Research example in building design and operations. Building design can impact the behaviors of building
occupants. For example, security countermeasures in a building have inuenced participants’ response time
and decisions in an active shooter incident where participants responded to the emergency in virtual oces
and schools70 (Fig.3). e study also revealed the importance of building and social context such that teachers
concerned more for others’ safety than oce workers70. ese explorations reveal the necessity of customizing
building design and operations to provide resilient solutions to extreme events for dierent users, which can be
accomplished using virtual reality. Given its sucient ecological validity and exibility, virtual reality can also
be an ideal platform to train building occupants on emergency safety, where trainees immerse into the scenarios
Figure2. (a) Openface application for real-time facial features extraction, (b) Human–computer interaction
monitoring application, (c) real-time physiological data monitoring.
Figure3. Empirical assessment of the impact of security countermeasures on human behavior during active
shooter incidents. e images are from the participants’ perspective in a virtual reality-based experiment.
Frosted glasses and access control are implemented in the right image. Non-player characters are included to
represent social inuence during building emergencies.
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and interact with the environment that can be customized per needs (e.g., oce, school, hospitals). Similarly, the
training outcomes will then be tested in a virtual emergency scenario where people’s performance in the envi-
ronment is evaluated by customizable systematic metrics developed based on training contents (e.g., evacuation
time and execution of safety actions).
Research example in sensing, inference, and awareness. Doppler radar physiological sensing using dedicated
radar systems and wireless infrastructure-based systems has been shown to be eective in sensing physiological
parameters from several meters to several tens of meters with a high degree of accuracy in controlled settings32.
Physiological parameters include heart and respiratory rates, respiratory tidal volume, heart rate variability
(HRV), pulse pressure, diagnostic patterns, activity level, and body orientation. ese parameters can be used to
assess user response to environmental conditions. One example is True Presence Occupancy Detection Sensor
(TruePODS™), which was developed by a start-up company Adnoviv, Inc. in collaboration with the University
of Hawaii. TruePODS™ is a Doppler radar-based occupancy sensor that detects breathing to accurately indicate
whether a space is occupied, even when the occupant is sedentary. TruePODS™ modules have been tested at
Rensselaer Polytechnic Institute LESA (Light Enabled Systems & Applications) Center, focused on using light
eciently in built environments, and healthcare, among others. e LESA smart conference room (Fig.4) senses
occupancy, pose (sitting, falling, standing), and has a mesh network of color sensors for coarse occupancy sens-
ing and measuring reected sunlight and solar heat ux for improved HVAC control. e TruePODS™ module
has been validated for occupancy/vacancy detection and occupant count and can successfully measure respira-
tory and heart rate when used in certain positions and orientations (Fig.4). is is the rst demonstration of an
occupancy/vacancy sensor that also provides occupant count and physiological parameters. e sensor output
may enable more energy-ecient building control while ensuring that occupant comfort is not compromised.
Research example in trust and collaboration. Centralized environmental controls in built environments are fre-
quently unable to meet the needs of most occupants140. Alternatively, automated control of local environmental
factors (e.g., thermal, lighting) and equipment positioning (e.g., a motorized sit/stand desk141) shows promise in
supporting worker health and well-being. In one study, multiple examples of trust and collaboration are emerg-
ing in the development of an intelligent oce workstation to address these concerns19. Facilitated by learning
from interactions between an intelligent workstation (Fig.5) and the occupant, an intelligent oce workstation
would understand and adapt to the worker’s changing needs in terms of thermal, lighting, and posture comfort,
share control with the user to build trust, and will coevolve with its user to promote healthier workplace behavior
(A short video describing this research can be found at: https:// youtu. be/ psfzI DTgK5g). at is, the smart desk
and user collaborate to nd and use the best settings for important outcomes in addition to comfort, including
health and productivity. A real-world evaluation showed that sharing the control between the smart desk and the
user to control the thermal environment led to higher satisfaction than in manually controlled environments20,
opening an avenue for collaborative control and trust between a building and its occupants. In addition to col-
laboration in making changes, a user-centered approach using focus groups has highlighted numerous trust
considerations linked to the privacy and security of the data collected by the workstation142.
Research example in modeling and simulation. Pre-occupancy analyses are a valuable tool for architects and
other stakeholders to forecast how a proposed design will aect the behavior of a building’s future inhabitants.
eir goal is to augment the knowledge and intuition of architects to reduce the gap between the expected
and actual performance of a built environment. Among them, multi-agent simulation approaches represent the
dynamic interactions between occupants and a physical environment. A recent study used a narrative-based
Figure4. e 3.4m × 8.5m LESA Smart Conference Room with the TruePODS™ sensor mounted on the
ceiling (le), and locations of successful physiological detection (right). Heart rates were detected at all the seats
around the table, and in 3 of the 4 corners. In all locations, respiratory rate was detected.
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approach to predict and evaluate the impact of two alternative design congurations for an internal medicine
ward on the day-to-day behavior of the occupants (e.g., doctors, nurses, patients, visitors)143. A combination of
eld observations and expert interviews informed the modeling of a digital ‘place,’ composed of spaces, actors,
activities, and narrative models where synthetic people (modeled as agents) inhabit a virtual space and perform
a set of individual or collaborative activities consistent with the function of the organization that occupies the
built environment. e space model includes physical and non-physical attributes, which determine the range
of behaviors that can be hosted at a given time contingent upon physical (e.g., geometry) and social (e.g., pres-
ence and activities of other occupants in the same space) contingencies. Actor models include specic occu-
pant roles and dynamic attributes (e.g., walked distances, time spent in specic activities). e activity model
determines interactions between spaces and occupants (e.g., moving, queuing, interacting), while the narrative
model assigns actors to one or more spaces to achieve a goal-oriented task. is simulation approach distributes
intelligence not only in agents but also in spaces, activity, and narrative models to ease the process of modeling
complex and collaborative behaviors. e simulation results shown in Fig.6 reveal the implications of two alter-
native designs for an outpatient clinic on people’s travel paths, occupancy density, and frequency of sta-visitor
interactions. Specically, the presence of a dayroom reduces visitors’ density in corridors, which could cause
spatial bottlenecks, and it diminishes the number of unplanned sta–visitor interactions that can delay the per-
formance of routine medical procedures.
Research example in equity and inclusion. A new class of occupancy estimation sensing based on active infra-
red stereo technologies obtains a depth image from a sensor located in a doorway and uses this image to detect
entrance and exit events11. ough much more accurate than traditional passive-infrared (PIR) sensors at
detecting and estimating occupancy, the conguration of the sensing solution is such that the entrance and exit
events of certain occupants are misrepresented in the output. In particular, the depth-imaging system relies on
Figure5. Intelligent workstation with embedded sensors to learn and adapt indoor environment based on user
preferences.
Figure6. Comparative evaluation of the impact of two design strategies on the inhabitants’ behavior. e
design dierence relates to the presence (Design A) or absence (Design B) of a dayroom (marked with a white
dotted line on the oorplan) where patients and visitors can gather to engage in social interactions. Results
indicate that the presence of a dayroom reduces people density in corridors and leads to fewer interruptions for
the medical sta involved in patient care activities. Figure adapted from143.
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infrared light, which is absorbed dierently by dierent hair types. Since the soware processing the images is
tracking heads as they move through the doorway, some hair types create a pattern that confuses the algorithm
that oversees detecting and tracking heads leading to a misestimation of the occupancy in the room. Figure7
shows a sample depth map obtained from the sensor with two subjects with dierent hair types. is example
shows the importance of developing and selecting sensors that do not result in biased results. Similarly, algo-
rithm bias is a well-acknowledged issue due to erroneous assumptions in machine learning process and training
on biased data sets.
A path forward
As an emerging eld that incorporates the dynamic interplay of human experience and building intelligence,
the primary aim of this paper was to specify the denition, vision, and research dimensions of HBI. Toward that
goal, this paper unpacked three primary areas that contribute to and require attention in HBI research: humans
(human experiences, performance, and well-being), buildings (building design and operations), and technology
(sensing, inference, and awareness). We have presented the critical interdisciplinary research domains at the
intersections of these three primary areas: trust and collaboration, decision-making and control, and modeling
and simulation. Finally, we have described core principles for all HBI research to consider and address, including
equity, privacy, and sustainability. Across the framework, we provide questions meant to stimulate collaborative
and widespread HBI research eorts. Similarly, we have presented examples of existing HBI applications and
emerging original research to inspire individuals interested in advancing HBI research and application. Taken
together, this information is meant to support HBI researchers, designers, and practitioners in considering the
wide range of symbiotic and interactive possibilities for humans and the built environment. Ultimately, promot-
ing thoughtful interdisciplinary approaches to HBI research will encourage the development of systems sensitive
to the needs of occupants while maximizing the operational goals of built environments, resulting in inclusive,
safe, and exciting places for people to live, work, and play.
Data availability
e data that support the ndings of this study are available from the authors, but restrictions apply to the
availability of these data, which were used under license for the current study, and so are not publicly available.
Data are however available from the corresponding author upon reasonable request and with permission of the
respective universities (USC, CMU, University of Technion, University of Hawaii).
Received: 29 August 2022; Accepted: 23 November 2022
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Acknowledgements
is research was supported by National Science Foundation [Grant #CMMI-2001742 and Grant #IIP-1831303].
Any opinions, ndings, and conclusions, or recommendations expressed in this material are those of the authors
and do not necessarily reect the views of the National Science Foundation.Dr. Boric-Lubecke holds equity and
serves as president of Adnoviv, Inc, the company that is the prime awardee of that NSF STTR grant. e Univer-
sity of Hawaii has granted a license to Adnoviv, Inc, to commercialize Doppler radar technology for occupancy
sensing purposes, and owns equity in Adnoviv, Inc.Portions of the material presented here are based upon work
supported by the U.S. Department of Energy’s Oce of Energy Eciency and Renewable Energy (EERE) under
the Building Technologies Oce Award Number DE- EE0007682.e views expressed in the article do not
necessarily represent the views of the U.S. Department of Energy or the United States Government.
Author contributions
B.B.G. and G.L. managed the overall process of manuscript preparations, organized the workshops through which
this manuscript was developed and they provided oversight. All authors draed the manuscript in a collabora-
tive fashion. All authors provided comments and approved the nal version of the manuscript for submission.
Competing interests
e authors have no competing interests as dened by Nature Research, or other interests that might be perceived
to inuence the results and/or discussion reported in this paper.
Additional information
Correspondence and requests for materials should be addressed to B.B.-G.
Reprints and permissions information is available at www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
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