The growing aging population raises critical challenges for mental health and independent living among older adults. Indoor comfort metrics - encompassing lighting, temperature, air quality, and ventilation - have been recognized as a key determinant of psychological well-being. Despite the evolution of Ambient Assisted Living (AAL) technologies, few systems directly address how indoor comfort afects mental health in older adults, a task requiring a multidisciplinary approach. This paper introduces OAIC (Older Adults Indoor Comfort), an ontology-based framework designed to bridge this gap by enabling intelligent, personalized indoor comfort management tailored to the mental health needs of the elderly. OAIC integrates standard ontologies such as SOSA, ICF, and ICD and models both general and tailored comfort settings based on expert knowledge and clinical metrics like the Geriatric Depression Scale. Through semantic rules, the system enables detection of deviations from comfort thresholds and triggers appropriate environmental adjustments via actuators. Two use cases illustrate the system's functionality in supporting individuals with depression and sleep disorders. The ontology was developed following the AgiSCOnt methodology, ensuring iterative collaboration with domain experts. Although in a prototypical phase, OAIC demonstrates strong potential to enhance mental health in aging populations by supporting non-pharmacological interventions through intelligent environmental adaptation. Future validation and stakeholder engagement are essential to assess its eficacy and broaden its adoption.
The progressive aging of the population is becoming a relevant issue for many countries worldwide.
In 2023, the World Health Organization (WHO) observed an increase in worldwide life expectancy
up to 73 years old [
Aging implies older adults often sufer from chronic conditions, including sensory loss, cognitive
decline, cardiovascular conditions, and impaired mobility, which can ultimately culminate in disability
[
In the past decade the WHO promoted Active Aging, a multidimensional and holistic approach that includes physical, mental, and social well-being, as well as ongoing engagement in social, economic, cultural, and civic activities. Active Aging is expected to make older adults healthier, socially-connected, and make them live independently for longer periods of time [9, 10]. From a technological perspective, Ambient Assisted Living (AAL) [11] emerged in the early 2000s as a technological paradigm to support older adults’ independent living – particularly in Activities of Daily Living. AAL received a significant push during the last ten years by the further development of other technological paradigms (i.e., Context Awareness, Internet of Things, Ambient Intelligence), which contributed to the development of prototypical solutions in the areas of smart homes and assistive devices.
Among the features of interest, AAL solutions also touch on indoor environmental quality (IEQ) and its conditions. IEQ plays a significant role in human health, with key factors including thermal comfort, indoor air quality and ventilation, visual and acoustic comfort, hygiene, and safety. In particular, researchers have focused their attention on the efects air pollutants and air quality have on health [ 12], which can also include mental issues. The efects of indoor temperature and lighting have also been intensively investigated [13, 14]. The problems generated from indoor environmental conditions are also a concern for older adults, particularly for those characterized by chronic conditions. In particular, air quality and pollution have been found to significantly exacerbate respiratory conditions and to impact mental health as well, contributing to increased depressive and anxious symptoms [15, 16]. Several studies underline the correlations between noise levels, indoor temperature, lighting levels, and the emotional well-being of older adults. These environmental factors can play a pivotal role in exacerbating mental illnesses, decreasing sleep eficiency, and accelerating cognitive decline (as summarized in Peralta et al. [17]). Indoor comfort-related issues are also a major concern for older adults in residential settings and nursing homes [18].
These considerations highlight the primary role AAL solutions can cover in supporting older adults living autonomously and in a healthy way by leveraging advanced technologies for indoor environmental monitoring and control. Specifically, the continuous assessment of key comfort parameters enables AAL systems to maintain optimal living conditions tailored to the needs of elderly individuals. By ensuring a stable and healthy indoor environment, these systems help mitigate the impact of environmental factors on mental health, thus contributing to the prevention of cognitive decline and mental health deterioration.
However, AAL solutions are mostly focused on physical impairments and mild cognitive disabilities (ranging from mild memory issues to early stages of dementia), with interventions devoted to identifying potential health risks, medication management, daily activities reminders, and behavioral support [19, 20, 21]. Indoor comfort metrics and their management in AAL solutions are often treated as a secondary aspect or assumed to be inherently adequate, with primary focus placed on safety, mobility, and medical monitoring. As a consequence, there is a notable lack of Ambient Assisted Living (AAL) solutions specifically designed to manage indoor comfort as a means of mitigating mental health risks among older adults. Moreover, addressing this gap requires a multidisciplinary approach that involves both clinical and non-clinical personnel in the monitoring and adaptation of indoor environments to support older adults’ mental health.
This work presents a prototypical ontology-based system to address the identified research gap. Specifically, this paper introduces an ontological framework (Older Adults Indoor Comfort - OAIC) and its engineering process, focusing on the modelling choices aimed at supporting the mental well-being of older adults. The remainder of this paper is organized as follows: Section 2 highlights some of the most relevant works in this field, while Section 3 provides the theoretical background on older adults’ mental health issues and presents the results of a literature survey on the topic of indoor environmental conditions and their efects on the elderly. Section 4 delves into the OAIC ontology engineering process, illustrating the resources involved in the process and the resulting model. Section 5 presents two use cases to describe the expected functioning of indoor comfort management performed with the ontology. Section 6 discusses some limitations of the proposed approach and outlines the future research directions, including proposals for validating OAIC and its inferences. Finally, the Conclusions summarize the main outcomes of this paper.
The use of ontologies for modelling indoor comfort metrics and to act as the backbone of AAL solutions is no novelty. In the past fifteen years, many articles have tackled the issue of modelling occupants, their needs, impairments, devices and actuators, and human activity within AAL frameworks. Indoor comfort metrics, however, were not often the main focus of ontology engineering, nor were older adults’ mental health needs. In most of the ontology-based AAL solutions, the main comfort metrics impacting human health – i.e., indoor air quality, temperature, humidity, lighting, and noise – were implicitly modelled as a collection of data (coming from sensors) [22]. Sometimes, occupants’ preferences in a smart environment include comfort metrics: in the AATUM ontology, a Preference class includes the possibility to specify a user’s preferred temperature value for a room or a lighting setting when the user performs specific activities (such as watching TV with lights of)[ 23]. In other ontologies, such as OntoDomus [24], comfort is nuanced as one of the contextual elements of a smart environment, and its management seems to be relegated to a matter of measurements performed by sensors.
After 2020, when research on the role played by indoor comfort on human health was being explored
more extensively, even AAL solutions started to pay more attention to comfort modelling and to carve
out a more relevant role for it. In [25] and [26], temperature, lighting, and air quality measurements
performed by sensors are used to trigger some actuations – aimed at avoiding the exacerbation of
occupant’s physical conditions, such as respiratory, visual, or cardiovascular ones. In these works,
occupants’ impairments are modelled leveraging international health standards, i.e., WHO’s
International Classification of Functioning, Disability and Health (ICF)[
The integration of comfort metrics and energy saving in ontology-based smart solutions is also
represented in more recent eforts. For example, IAQ focuses on modelling indoor air quality
(specifically, temperature, humidity, PM2.5, and PM10) to improve occupants’ health, comfort, and energy
consumption. Also in this case, ICF-based health profiles describing physical conditions (including
chronic ones) are linked to comfort settings via rules. Sustainable and eficient energy consumption
with indoor comfort is also the aim of the AAL solution described in [
Mental health challenges in older adults, including depression, anxiety, sleep disturbances, and cognitive
decline, are increasingly recognized as critical public health issues [
Sleep and circadian rhythm: In the elderly population, circadian rhythm disruption is a core
mechanism linking environmental stimuli to psychological outcomes. Light and temperature are key
“zeitgebers” (time cues) that help synchronize circadian rhythms [
Lighting exposure: Light exposure plays a pivotal role in regulating circadian rhythms, mood, and
cognitive function in older adults. Circadian lighting systems that simulate natural daylight cycles, with
gradual shifts in intensity and colour temperature, have demonstrated benefits in reducing depressive
symptoms and agitation while enhancing sleep quality [
Temperature: Depression, cognitive impairment, and dementia are significantly influenced by
environmental conditions, particularly temperature regulation. Elevated ambient temperatures and
greater temperature variability have been strongly associated with negative psychological and cognitive
outcomes in older adults. Meta-analytic evidence [
Air quality: Air quality is another essential determinant of mental health and cognitive function.
Exposure to fine particulate matter (PM2.5), PM10, nitrogen dioxide (NO), and ozone (O) has been
consistently linked to increased risks of depression, anxiety, cognitive decline, and neurodegenerative
diseases such as dementia [
The development of OAIC took advantage of an existing agile methodology (AgiSCOnt [49]), which relies on a collaborative approach involving domain experts and ontologists in acquiring a conceptual map of the domain(s) of interest. The knowledge elicitation and acquisition phases benefited from the involvement of domain experts, including clinical personnel (2 gerontologists working in a hospital, 2 psychologists, 2 biomedical engineers), architects and designers (3) involved in the reconfiguration of living environments for older adults, and computer scientists (2). These activities were conducted within the framework of the Italian PNRR Research Project Age-It 1. Following AgiSCOnt’s steps (i.e, Analysis and conceptualization, Development and test, and Ontology use and updating), domain experts were involved iteratively in the definition and refinement of a conceptual map. The prototypical OAIC ontology is available online 2.
During this step, the findings from diferent scientific and gray literature sources (see Section 3) were gathered and discussed with domain experts. The discussion brought to the definition of a conceptual map (sketched in Figure 1 and which does not rely on any formalism to allow experts’ maximum freedom), and the definition of some indoor comfort metrics’ specifics, as detailed in the following subsection. In particular, the domain experts deemed it essential to point out that there exists a neutral comfort setting, which is suitable for all older adults with mental conditions, although some more severe conditions may require a tailored set of comfort specifications. Also, experts highlighted that the selection of comfort settings (particularly, tailored ones) may be the result of a discussion with the older adult’s physician and that the older adult alone may not be self-suficient in setting the comfort of 1Project website: https://ageit.eu/wp/s-p-o-k-e-9/ 2Ontology available at: https://w3id.org/ontocare/oaic his/her house, thus requiring the assistance of a caregiver. All domain experts agreed that the scope of the OAIC ontology is to prevent the exacerbation of older adults’ mental conditions by modifying the indoor comfort when occupants are sojourning in a room: experts expected the ontology to identify the harmful comfort metrics and to trigger a proper response.
The conceptual map was then converted into an ontology, leveraging OWL 2 DL representation language [50] with the addition of rules written with Semantic Web Rule Language (SWRL) [51]. Considering the domains represented in the conceptual map and the existing ontological models, OAIC was developed based on the following modelling choices: • The health standards pertaining to psychological and a person’s functioning were defined by domain experts; this resulted in the reuse of (subsets of) the ICD and the ICF for the disease and functioning component, and brought to modelling some clinical standards adopted by psychologists to quantify common mental conditions, such as anxiety, depression, and sleep disorders. In particular, the need for quantifying some psychological and physical factors emerged as pivotal to understanding the type of adaptations the comfort should undergo. • The portion of the map depicting sensors, actuators, and their measurements and actuations (performed on some comfort metrics, which are physical magnitudes) relies on the reuse of the Sensor-Observation-Samples-Actuators (SOSA) ontology [52]. • The definition of neutral and tailored comfort settings should be made explicit, so that (if necessary) settings could be changed over time, following occupants’ health condition modification.
The reuse of SOSA, ICD, and ICF was performed as "soft reuse" (as defined in [ 53]), importing those entities that were deemed as relevant for the domains at hand. Also, considering OAIC’s specific focus on mental health, only a few ICD classes were modelled, while ICF classes included in OAIC serve the purpose of illustrating the ontology’s scalability (i.e., the ontology can possibly encompass also physical and mental conditions). The following subsections delve into OAIC’s structure.
A oaic:HealthCondition is linked to its oaic:Occupant via the oiac:hasHealthCondition object property and is described by relying on an existing ontology design pattern [54] (Figure 2).
This pattern allows linking oaic:HCDescriptor individuals to a health condition; each descriptor is characterized by either the ICD code describing the occupant’s mental disorder or the ICF codes representing the impaired functions (with the datatype property oaic:ICFGenericQualifier for quantifying the magnitude of the impairment). To assess and quantify the most common mental disorders afecting older adults, OAIC models as datatype properties the results of widely adopted tests: • Depression is quantified using the Geriatric Depression Scale (GDS) – Short Version [ 55]; this scale may be used to monitor depression over time in all clinical settings. Any score above 5 on the GDS can be considered as a mild depression, while scores between 5 and 8 can be considered moderate depressive disorder, and finally, scores greater than 8 indicate severe depressive disorder. • Anxiety is quantified relying on the Geriatric Anxiety Inventory (GAI) - Short Form, which, for scores greater than or equal to 3, indicates a possible form of anxiety. • Sleep disorder is quantified with the Geriatric Sleep Questionnaire (GSQ) - 6 item version [ 56], which gives positive results for scores above 15.
OAIC reuses many concepts from SOSA, namely sosa:Sensor, sosa:Observation, which becomes oaic:Measurement, and sosa:Actuator, together with all the object and datatype properties relevant for the correct functioning of the related patterns. Each sensor performs observations, which are characterized by an ID, a datetime stamp, and a sosa:hasSimpleResult property that explicits the measurement value. Each sosa:Observation is expressed in a unit of measure (by reusing the Ontology of units of Measure (OM) [57]) and assesses the value of a sosa:Property, physical characteristics of interest which, in OAIC case, are the set of comfort metrics measures by sensors (i.e., CO, CO, CO, TVOC, PM2.5, flicker, humidity, illuminance, ventilation, temperature). Similarly, sosa:Actuators perform sosa:Actuation over (one or more) sosa:Property. Both sensors and actuators are located in dogont:Rooms, the set of rooms composing a domestic environment - borrowed from a subset of the DogOnt model [58]. Figure 3 depicts the TBox and the relationships occuring among these concepts.
Comfort settings are instantiated as individuals of the oaic:ComfortSetting class, which is further
specialized into the oaic:GeneralComfortSetting and oaic:TailoredComfortSetting
subclasses. This partition answers to experts’ request of distinguishing between a neutral comfort setting
and tailored settings that are explicitly developed to support the healing of specific mental conditions.
The choice of modelling comfort settings as individuals traces back to previous studies [
As an example, the following OWL code (serialized in Turtle) represents the definition of maximum and minimum indoor temperatures (for winter and summer seasons) assigned to the oaic:CSDescr2-1 descriptor for a oaic:GeneralComfortSetting individual: o a i c : CSDescr2 −1 r d f : t y p e owl : N a m e d I n d i v i d u a l , o a i c :
C o m f o r t S e t t i n g D e s c r i p t o r ; o a i c : s e t s O b s e r v a b l e P r o p e r t y o a i c : T e m p e r a t u r e ; o a i c : TempSummerMax " 2 3 " ^ ^ x s d : i n t ; o a i c : TempWinterMax " 2 2 " ^ ^ x s d : i n t .
According to the domain experts, the general comfort setting specifies the following comfort values for seven observable property: • CO maximum concentration: 800 ppm; • standard lighting set at 400 lux; • PM2.5 maximum concentration: 10 µg/m3; • winter indoor temperature between 20 and 22° C., summer indoor temperature between 23 and 26° C.; • winter indoor humidity between 20 and 50%, summer indoor humidity between 40 and 60%; • CO maximum concentration: 300 ppm; • TVOC (Total Volatile Organic Compounds) maximum concentration: 100 ppb. 4.2.4. Rules To illustrate the business logic underlying OAIC [59], rules were developed with domain experts. Considering the expressiveness required by the use cases, SWRL was chosen as rule language. The rules serve three purposes: • Classifying an oaic:Occupant’s oaic:HealthCondition according to the magnitude of his/her scale value(s): for instance, the rule O c c u p a n t ( ? o c c ) , h a s H e a l t h C o n d i t i o n ( ? occ , ? hc ) , h a s D e s c r i p t o r ( ? hc , ? hcd ) , G D S s c a l e ( ? hcd , ? g d s ) , g r e a t e r T h a n O r E q u a l ( ? gds , 9 ) , l e s s T h a n O r E q u a l ( ? gds , 1 1 ) −> 6 A71 . 1 ( ? hc ) checks for the occupant’s Geriatric Depression Scale (attributed by a clinician) and classifies the health condition as belonging to the ICD icd:6A71.1 (labelled "Recurrent depressive disorder, current episode moderate - without psychotic symptoms"). • Checking whether a sosa:Observation individual value falls within the ranges specified by the oaic:GeneralComfortSetting (or the oaic:TailoredComfortSetting if it is specified). • In case a values exceeds the threshold(s) represented in the oaic:GeneralComfortSetting or oaic:TailoredComfortSetting, identify the proper sosa:Actuator and activate it (by setting true the value of the oaic:activate datatype property).
Rules were defined with domain experts during the Analysis and conceptualization phase and represent a subset of those cases experts have experienced during their working activities. Considering the prototypical nature of OAIC, the focus of rules was shifted towards older adults and their mental issues, and indoor comfort metrics management, rather than representing actuation in a complete way. In the following Section 4.3, three use cases depicting the role played by SWRL rules are presented.
To conclude the Development and test phase foreseen in AgiSCOnt, the prototype ontology was tested against some use cases. Their purpose is to show domain experts and ontologists whether the prototype complies with their expectations and to rectify authoring issues - if acknowledged. The reasoner Pellet [60] was selected due to its ability to treat SWRL built-ins.
Mr. Arco (82) lives alone in his apartment and sufers from mild depression ( icd:6A71.0), reporting a GDS score equal to 7. Moreover, he is afected by Chronic Obstructive Pulmonary Disease (COPD) (icd:CA22), which limits his freedom of movement and activities. To avoid COPD exacerbation and to mitigate his depression, the air purifier (a sosa:Actuator) is activated whenever the following rule is true: O c c u p a n t ( ? o c c ) , h a s H e a l t h C o n d i t i o n ( ? occ , ? hc ) , ( 6 A71 . 0 o r 6 A71 . 1 o r 6 A71 . 3 o r 6 B00 o r 6 B01 o r 6 B04 ) ( ? hc ) , l o c a t e d I n ( ? occ , ? room ) , O b s e r v a t i o n ( ? o b s ) , madeBySensor ( ? obs , ? s ) , l o c a t e d I n ( ? s , ? room ) , o b s e r v e d P r o p e r t y ( ? obs , ? p r o ) , h a s S i m p l e R e s u l t ( ? obs , ? v ) , d e f i n e d C o m f o r t S e t t i n g s ( ? occ , ? c s ) , h a s C o m f o r t S e t t i n g D e s c r i p t o r ( ? c s , ? c s d ) , CO2max ( ? csd , ? vmax ) , g r e a t e r T h a n O r E q u a l ( ? v , ? vmax ) , C O 2 A c t u a t o r ( ? a ) , l o c a t e d I n ( ? a , ? room ) , a c t u a t e O n ( ? a , ? p r o ) −> a c t i v a t e ( ? a , t r u e )
This simple rule allows Mr. Arco to avoid his respiratory conditions and the exacerbation of his mental disorder. This rule works together with similar rules setting the minimum lighting foreseen by the oaic:GeneralComfortSetting, as well as other rules monitoring air quality.
Mrs. Rossi (76) lives on her own after her husband’s departure. She sufers from chronic insomnia (icd:7A00) and other diagnosed sleep function disorders (icd:VV01). Her physicians recommended an indoor temperature around 20° C. during sleep time and the adoption of blackout shutters to limit exposure to light during the night. Her daughter sets up a TailoredComfortSetting to implement these changes in Mrs. Rossi’s bedroom. Therefore, the following rules are enforced: The two rules exploit the equivalence sosa:Observation oaic:Measurement, which enables the generation of two subclasses of measurement (oaic:NightObservation and oaci:DayObservation) which depend on the specific sosa:resultTime value attached to each observation. In this way, the caregiver can set the night (and day) duration, and the actuators will activate only if the indoor measurements are performed at a specific time of the day.
The work on OAIC is still in its early stages, and several limitations remain to be addressed. First, considering the prototypical characteristic of the ontology, the focus on older adults’ mental health is mostly on single disorders; however, as highlighted in Section 3, older adults may face more than one mental disorder at the same time. In the next stages, domain experts will be involved in how to tackle multiple mental conditions and comorbidities, identifying priorities to avoid conflictual comfort settings. Among the mental issues not explicitly addressed in this work, solitude also needs further investigation; to this end, two research directions can be followed: 1) recurring to domain experts’ knowledge and model solitude as if it was a mental health concern, and b) including OAIC in a broader AAL framework which can foster digital socialization (e.g., as in [61]). From an ontology perspective, the representation of the actuation is currently simplistic. This is partly due to the necessity of focusing on older adults’ mental health, and partly to the need to identify a) a set of actuators that can be controlled remotely and b) other relevant ontologies that can be used in this field. So far, the OAIC ontology has completed the first two phases of AgiSCOnt’s methodology (i.e., Analysis and conceptualization and Development and testing): with the interaction with other domain experts and the collection of further requirements, the ontology can change considerably in some aspects. It is also relevant to underline that, so far, OAIC deals with single occupants (older adults living alone or living in a nursing home in which they have their own room). This limitation must be assessed to identify the possibility of having OACI manage diferent conditions for multiple occupants (characterized by diferent health conditions). Nevertheless, it is also fundamental to gather consensus on the model and its scope. To this end, OAIC needs to be disseminated to other relevant stakeholders and an evaluation of its inferences and their values for the stakeholder community must be performed (as it happened for other health-related and ontology-based projects [62, 63]). This is part of a comprehensive, evidence-based validation strategy informed by practices in architecture, gerontology, and psychology [64]. Semi-structured interviews conducted with stakeholders have the opportunity to explore the emotional and content relevance of system constructs and collect qualitative suggestions for enhancement.
This work presents OAIC, an ontology-based system aimed at supporting the mental health of older adults by managing indoor comfort conditions. Unlike traditional AAL solutions, OAIC uniquely emphasizes the correlation between environmental metrics and mental well-being, ofering tailored comfort interventions through semantic modelling and automated actuation. Developed collaboratively with clinical and technical experts, OAIC integrates standardized health classifications and sensor data to infer actionable insights, thereby enabling personalized, health-supportive environments. Initial use cases demonstrate the feasibility of addressing specific mental health conditions, such as depression and sleep disorders, through indoor environment control. While still in development, OAIC provides a foundation for future systems that promote psychological well-being and independent living among the elderly. Future work will focus on validation, refinement, and stakeholder evaluation to ensure the clinical relevance, usability, and integration of the ontological model into broader AAL ecosystems.
This paper was developed within the project funded by NextGenerationEU-“Age-It-Ageing well in an ageing society” project (PE0000015), National Recovery and Resilience Plan (NRRP) -PE8-Mission4, C2, Intervention 1.3”.
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