Analyzing the Relationship between Human Behavior and Indoor Air Quality

Application of artificial intelligence in active assisted living for aging population in real-world setting with commercial devices - A scoping review

BACKGROUND

The aging population is steadily increasing, posing new challenges and opportunities for healthcare systems worldwide. Technological advancements, particularly in commercially available Active Assisted Living devices, offer a promising alternative. These readily accessible products, ranging from smartwatches to home automation systems, are often equipped with Artificial Intelligence capabilities that can monitor health metrics, predict adverse events, and facilitate a safer living environment. However, there is no review exploring how Artificial Intelligence has been integrated into commercially available Active Assisted Living technologies, and how these devices monitor health metrics and provide healthcare solutions in a real-world environment for healthy aging. This review is essential because it fills a knowledge gap in understanding AI's integration in Active Assisted Living technologies in promoting healthy aging in real-world settings, identifying key issues that require to be addressed in future studies.

OBJECTIVE

The aim of this overview is to outline current understanding, identify potential research opportunities, and highlight research gaps from published studies regarding the use of Artificial Intelligence in commercially available Active Assisted Living technologies that assists older individuals aging at home.

METHODS

A comprehensive search was conducted in six databases-PubMed, CINAHL, IEEE Xplore, Scopus, ACM Digital Library, and Web of Science-to identify relevant studies published over the past decade from 2013 to 2024. Our methodology adhered to the PRISMA extension for scoping reviews to ensure rigor and transparency throughout the review process. After applying predefined inclusion and exclusion criteria on 825 retrieved articles, a total of 64 papers were included for analysis and synthesis.

RESULTS

Several trends emerged from our analysis of the 64 selected papers. A majority of the work (39/64, 61%) was published after the year 2020. Geographically, most of the studies originated from East Asia and North America (36/64, 56%). The primary application goal of Artificial Intelligence in the reviewed literature was focused on activity recognition (34/64, 53%), followed by daily monitoring (10/64, 16%). Methodologically, tree-based and neural network-based approaches were the most prevalent Artificial Intelligence algorithms used in studies (32/64, 50% and 31/64, 48% respectively). A notable proportion of the studies (32/64, 50%) carried out their research using specially designed smart home testbeds that simulate the conditions in real-world. Moreover, ambient technology was a common thread (49/64, 77%), with occupancy-related data (such as motion and electrical appliance usage logs) and environmental sensors (indicators like temperature and humidity) being the most frequently used.

CONCLUSION

Our results suggest that Artificial Intelligence has been increasingly deployed in the real-world Active Assisted Living context over the past decade, offering a variety of applications aimed at healthy aging and facilitating independent living for the older adults. A wide range of smart home indicators were leveraged for comprehensive data analysis, exploring and enhancing the potentials and effectiveness of solutions. However, our review has identified multiple research gaps that need further investigation. First, most research has been conducted in controlled testbed environments, leaving a lack of real-world applications that could validate the technologies' efficacy and scalability. Second, there is a noticeable absence of research leveraging cloud technology, an essential tool for large-scale deployment and standardized data collection and management. Future work should prioritize these areas to maximize the potential benefits of Artificial Intelligence in Active Assisted Living settings.

Comparison of the rate of certain trace metals accumulation in indoor plants for smoking and non-smoking areas

Tobacco smoke causes to release severe toxic metals into the environment. It is recognized as the most significant issue in indoor air quality. Pollution and toxic substances in smoke quickly spread and penetrate the indoor environment. Environmental tobacco smoke is responsible for lowering indoor air quality. There is much evidence that poor air quality occurs with inadequate ventilation conditions in indoor environments. The plants have been observed to absorb the smoke in the environment into their own body like a sponge. The plant species in this study can be used easily in almost every office, home, or other indoor areas. Using indoor plants is very beneficial in biomonitoring and absorbing these trace metals. Some indoor plants have shown successful performance as biomonitors for health-damaging pollutants. The study aims to determine the concentration of three trace metals (Cu, Co, and Ni) using five indoor ornamentals frequently used in smoking areas, namely D. amoena, D. marginata, F. elastica, S. wallisii, and Y. massengena. The Ni uptake and its accumulation in S. wallisii, and Y. massengena increased in correlation with smoke areas. However, the rate of accumulation of Co and Cu was found to be independent due to consideration of the environmental emissions. Consequently, our results suggest that F. elastica is more resistant to smoking, whereas S. wallisii would be a better choice as a biomonitoring plant of tobacco smoke.

From Raising Awareness to a Behavioural Change: A Case Study of Indoor Air Quality Improvement Using IoT and COM-B Model

The topic of indoor air pollution has yet to receive the same level of attention as ambient pollution. We spend considerable time indoors, and poorer indoor air quality affects most of us, particularly people with respiratory and other health conditions. There is a pressing need for methodological case studies focusing on informing households about the causes and harms of indoor air pollution and supporting changes in behaviour around different indoor activities that cause it. The use of indoor air quality (IAQ) sensor data to support behaviour change is the focus of our research in this paper. We have conducted two studies-first, to evaluate the effectiveness of the IAQ data visualisation as a trigger for the natural reflection capability of human beings to raise awareness. This study was performed without the scaffolding of a formal behaviour change model. In the second study, we showcase how a behaviour psychology model, COM-B (Capability, Opportunity, and Motivation-Behaviour), can be operationalised as a means of digital intervention to support behaviour change. We have developed four digital interventions manifested through a digital platform. We have demonstrated that it is possible to change behaviour concerning indoor activities using the COM-B model. We have also observed a measurable change in indoor air quality. In addition, qualitative analysis has shown that the awareness level among occupants has improved due to our approach of utilising IoT sensor data with COM-B-based digital interventions.

Impact of Climate Change on Indoor Air Quality: A Review

Climate change can affect the indoor environment due to heat and mass transfers between indoor and outdoor environments. To mitigate climate change impacts and adapt buildings to the changing environment, changes in building characteristics and occupants' behavior may occur. To characterize the effects of climate change on indoor air quality (IAQ), the present review focused on four aspects: (1) experimental and modeling studies that relate IAQ to future environmental conditions, (2) evolution of indoor and outdoor air concentrations in the coming years with regard to temperature rise, (3) climate change mitigation and adaptation actions in the building sector, and (4) evolution of human behavior in the context of climate change. In the indoor environment, experimental and modeling studies on indoor air pollutants highlighted a combined effect of temperature and relative humidity on pollutant emissions from indoor sources. Five IAQ models developed for future climate data were identified in the literature. In the outdoor environment, the increasing ambient temperature may lead directly or indirectly to changes in ozone, particle, nitrogen oxides, and volatile organic compound concentrations in some regions of the world depending on the assumptions made about temperature evolution, anthropogenic emissions, and regional regulation. Infiltration into buildings of outdoor air pollutants is governed by many factors, including temperature difference between indoors and outdoors, and might increase in the years to come during summer and decrease during other seasons. On the other hand, building codes in some countries require a higher airtightness for new and retrofitted buildings. The building adaptation actions include the reinforcement of insulation, implementation of new materials and smart building technologies, and a more systematic and possibly longer use of air conditioning systems in summer compared to nowadays. Moreover, warmer winters, springs, and autumns may induce an increasing duration of open windows in these seasons, while the use of air conditioning in summer may reduce the duration of open windows.

Environmental Pollution Analysis and Impact Study-A Case Study for the Salton Sea in California

A natural experiment conducted on the shrinking Salton Sea, a saline lake in California, showed that each one foot drop in lake elevation resulted in a 2.6% average increase in PM2.5 concentrations. The shrinking has caused the asthma rate continues to increase among children, with one in five children being sent to the emergency department, which is related to asthma. In this paper, several data-driven machine learning (ML) models are developed for forecasting air quality and dust emission to study, evaluate and predict the impacts on human health due to the shrinkage of the sea, such as the Salton Sea. The paper presents an improved long short-term memory (LSTM) model to predict the hourly air quality (O3 and CO) based on air pollutants and weather data in the previous 5 h. According to our experiment results, the model generates a very good R2 score of 0.924 and 0.835 for O3 and CO, respectively. In addition, the paper proposes an ensemble model based on random forest (RF) and gradient boosting (GBoost) algorithms for forecasting hourly PM2.5 and PM10 using the air quality and weather data in the previous 5 h. Furthermore, the paper shares our research results for PM2.5 and PM10 prediction based on the proposed ensemble ML models using satellite remote sensing data. Daily PM2.5 and PM10 concentration maps in 2018 are created to display the regional air pollution density and severity. Finally, the paper reports Artificial Intelligence (AI) based research findings of measuring air pollution impact on asthma prevalence rate of local residents in the Salton Sea region. A stacked ensemble model based on support vector regression (SVR), elastic net regression (ENR), RF and GBoost is developed for asthma prediction with a good R2 score of 0.978.

Health risk analysis from volatile organic compounds and fine particulate matter in the printing industry

The association between the printing activity and the pollutant exposure of the workers was investigated in five consecutive working days, during 8 h work shift per day. Exposure concentrations of the total volatile organic compound and fine particulate matter were measured in the four voluntary printing factories in Thailand. Two types of the printing process, offset and digital printing, were compared. The 8 h average of particulate matter 2.5 in the field blank, Offset A, Offset B, Offset C printing and Digital printing D was 7.46, 21.51, 44.26, 77.92, and 42.08 µgm^-3, respectively. The highest particulate matter level in the Offset printing C, 77.92 µgm^-3 was due to the surrounded paper dust in the area. The 8 h average of total volatile organic compounds in field blank, Offset A, Offset B, Offset C printing and Digital printing D was 0.12, 2.68, 5.02, 21.86, and 0.67 ppm, respectively. The highest total volatile organic compound was 21.86 ppm in the Offset printing C because of the high production rate and the application of organic solvents in the cleanup process. Worker's exposure to total volatile organic compound and particulate matter 2.5 in the offset printings was higher than in the digital laser printing. From the health risk evaluation, the workers in offset printings were at risk from total volatile organic compound exposure, Hazard quotient > 1. However, workers exposed to particulate matter exposures were not at risk, Hazard quotient < 1.

Using an Internet of Behaviours to Study How Air Pollution Can Affect People's Activities of Daily Living: A Case Study of Beijing, China

This study aims to quantitatively model rather than to presuppose whether or not air pollution in Beijing (China) affects people's activities of daily living (ADLs) based on an Internet of Behaviours (IoB), in which IoT sensor data can signal environmental events that can change human behaviour on mass. Peoples' density distribution computed by call detail records (CDRs) and air quality data are used to build a fixed effect model (FEM) to analyse the influence of air pollution on four types of ADLs. The following four effects are discovered: Air pollution negatively impacts people going sightseeing in the afternoon; has a positive impact on people staying-in, in the morning and the middle of the day. Air pollution lowers people's desire to go to restaurants for lunch, but far less so in the evening. As air quality worsens, people tend to decrease their walking and cycling and tend to travel more by bus or subway. We also find a monotonically decreasing nonlinear relationship between air quality index and the average CDR-based distance for each person of two citizen groups that go walking or cycling. Our key and novel contributions are that we first define IoB as a ubiquitous concept. Based on this, we propose a methodology to better understand the link between bad air pollution events and citizens' activities of daily life. We applied this methodology in the first comprehensive study that provides quantitative evidence of the actual effect, not the presumed effect, that air pollution can significantly affect a wide range of citizens' activities of daily living.

Data Mining Algorithms for Smart Cities: A Bibliometric Analysis

Smart cities connect people and places using innovative technologies such as Data Mining (DM), Machine Learning (ML), big data, and the Internet of Things (IoT). This paper presents a bibliometric analysis to provide a comprehensive overview of studies associated with DM technologies used in smart cities applications. The study aims to identify the main DM techniques used in the context of smart cities and how the research field of DM for smart cities evolves over time. We adopted both qualitative and quantitative methods to explore the topic. We used the Scopus database to find relative articles published in scientific journals. This study covers 197 articles published over the period from 2013 to 2021. For the bibliometric analysis, we used the Biliometrix library, developed in R. Our findings show that there is a wide range of DM technologies used in every layer of a smart city project. Several ML algorithms, supervised or unsupervised, are adopted for operating the instrumentation, middleware, and application layer. The bibliometric analysis shows that DM for smart cities is a fast-growing scientific field. Scientists from all over the world show a great interest in researching and collaborating on this interdisciplinary scientific field.

Urban Data Dynamics: A Systematic Benchmarking Framework to Integrate Crowdsourcing and Smart Cities’ Standardization

Urbanization and knowledge economy have highly marked the new millennium. Urbanization brings new challenges which can be addressed by the knowledge economy, which opens up scientific and technical innovation opportunities. The enhancement of cities’ intelligence has heavily impacted city transformation and sustainable decision-making based on urban data knowledge extraction. This work is motivated by the strong demand for robust standardization efforts to steer and measure city performance and dynamics, given the growing tendency of conventional cities’ transformation into smart and resilient ones. This paper revises the earlier so-called “cityDNA” framework, which was designed to detect the interrelations between the six smart city dimensions, such that a city’s profile and capacities are recognized in a systematic manner. The updated framework implements the widely accepted smart city (ISO 37120:2018) standard, along with an adaptive Web service, which processes urban data and visualizes the city’s profile to facilitate decision-making. The proposed framework offers a solid benchmarking service, at which the value of crowdsourced data is exploited for the production of urban knowledge and city transformation empowerment. The proposed benchmarking approach is tested and validated through relevant case studies and a proof-of-concept scenario, in which open data and crowdsourced data are exploited. The outcomes revealed that cities should intensify their KPI-driven data production and exploitation along with a set of solid standards for cities to enable cities with customizable scenarios enriched with indicators that reflect each city’s vibrancy.

Simulation and Analysis of Indoor Air Quality in Florida Using Time Series Regression (TSR) and Artificial Neural Networks (ANN) Models

Exposures to air pollutants have been associated with various acute respiratory diseases and detrimental human health. Analysis and further interpretation of air pollutant patterns are correspondingly important as monitoring them. In the present study, the 24-h and four-month indoor and outdoor PM2.5, PM10, NO2, relative humidity, and temperature were measured simultaneously for a laboratory in Gainesville city, Florida. The indoor PM2.5, PM10, and NO2 concentrations were predicted using multiple linear regression (MLR), time series regression (TSR), and artificial neural networks (ANN) models. The modeling conducted in this study aims to perform a cross comparison study between these models in a symmetric environment. The value of root-mean-square error was improved by 18.33% in comparison with the MLR model. In addition, the value of the coefficient of determination was improved by 24.68%. The ANN model had the best performance and could predict the target air pollutants at 10-min intervals of the studied building with 90% accuracy levels. The TSR model showed slightly better performance compared to the MLR model. These results can be accordingly referred for studies analyzing indoor air quality in similar building types and climate zones.

Study of the Characteristics and Comprehensive Fuzzy Assessment of Indoor Air Chemical Contamination in Public Buildings

Quality-of-life is improving daily with continuous improvements in urban modernization, which necessitates more stringent requirements for indoor air quality. Fuzzy assessment enables us to obtain the grade of the evaluation object by compound calculation with the help of membership function and weight coefficient, overcoming the limitations of traditional methods applied to develop environmental quality indices. First, this study continuously measured SO₂, O₃, NO₂, NO, CO, CO₂, PM₁₀, PM_2.5, and other chemical pollutants during the daytime operating hours of a library and a canteen. We analyzed the concentration distributions of the particles in the air were discussed based on 31 different particle diameters. Finally, the experimental data in department store and waiting hall were analyzed by fuzzy evaluation, with the following results. (1) The library and canteen PM₁₀ concentrations peaked at 07:45 in the morning and was elevated during the afternoon (48.9 and 59 μg/m³, respectively). (2) The Pearson correlation coefficient of the PM₁₀ and PM_2.5 concentrations in the library was 0.98. PM₁₀ and SO₂ in the canteen were negatively correlated, with a correlation coefficient of −0.65. PM_2.5 and PM₁ were always highly positively correlated. (3) The high concentration of particles in the library was associated with the small particle size range (0.25~0.45 μm). (4) By applying the fuzzy comprehensive evaluation method, the library grade evaluation was the highest level, and the waiting hall was the lowest. This study enhances our understanding of the indoor chemical contamination relationships for public buildings and highlights the urgent need for improving indoor air quality.

Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era

The unprecedented urban growth of recent years requires improved urban planning and management to make urban spaces more inclusive, safe, resilient and sustainable. Additionally, humanity faces the COVID pandemic, which especially complicates the management of Smart Cities. A possible solution to address these two problems (environmental and health) in Smart Cities may be the use of Machine Learning techniques. One of the objectives of our work is to thoroughly analyze the link between the concepts of Smart Cities, Machine Learning techniques and their applicability. In this work, an exhaustive study of the relationship between Smart Cities and the applicability of Machine Learning (ML) techniques is carried out with the aim of optimizing sustainability. For this, the ML models, analyzed from the point of view of the models, techniques and applications, are studied. The areas and dimensions of sustainability addressed are analyzed, and the Sustainable Development Goals (SDGs) are discussed. The main objective is to propose a model (EARLY) that allows us to tackle these problems in the future. An inclusive perspective on applicability, sustainability scopes and dimensions, SDGs, tools, data types and Machine Learning techniques is provided. Finally, a case study applied to an Andalusian city is presented.

Whole-House Emission Rates and Loss Coefficients of Formaldehyde and Other Volatile Organic Compounds as a Function of the Air Change Rate

Whole-house emission rates and indoor loss coefficients of formaldehyde and other volatile organic compounds (VOCs) were determined from continuous measurements inside a net-zero energy home at two different air change rates (ACHs). By turning the mechanical ventilation on and off, it was demonstrated that formaldehyde concentrations reach a steady state much more quickly than other VOCs, consistent with a significant indoor loss rate attributed to surface uptake. The first order loss coefficient for formaldehyde was 0.47 ± 0.06 h^-1 at 0.08 h^-1 ACH and 0.88 ± 0.22 h^-1 at 0.62 h^-1 ACH. Loss rates for other VOCs measured were not discernible, with the exception of hexanoic acid. A factor of 5.5 increase in the ACH increased the whole-house emission rates of VOCs but by varying degrees (factors of 1.1 to 3.8), with formaldehyde displaying no significant change. The formaldehyde area-specific emission rate (86 ± 8 μg m^-2 h^-1) was insensitive to changes in the ACH because its large indoor loss rate muted the impact of ventilation on indoor air concentrations. These results demonstrate that formaldehyde loss rates must be taken into account to correctly estimate whole-house emission rates and that ventilation will not be as effective at reducing indoor formaldehyde concentrations as it is for other VOCs.

Assessment of occupant-behavior-based indoor air quality and its impacts on human exposure risk: A case study based on the wildfires in Northern California

The recent wildfires in California, U.S., have caused not only significant losses to human life and property, but also serious environmental and health issues. Ambient air pollution from combustion during the fires could increase indoor exposure risks to toxic gases and particles, further exacerbating respiratory conditions. This work aims at addressing existing knowledge gaps in understanding how indoor air quality is affected by outdoor air pollutants during wildfires-by taking into account occupant behaviors (e.g., movement, operation of windows and air-conditioning) which strongly influence building performance and occupant comfort. A novel modeling framework was developed to simulate the indoor exposure risks considering the impact of occupant behaviors by integrating building energy and occupant behaviour modeling with computational fluid dynamics simulation. Occupant behaviors were found to exert significant impacts on indoor air flow patterns and pollutant concentrations, based on which, certain behaviors are recommended during wildfires. Further, the actual respiratory injury level under such outdoor conditions was predicted. The modeling framework and the findings enable a deeper understanding of the actual health impacts of wildfires, as well as informing strategies for mitigating occupant health risk during wildfires.