Project RISE: Recognizing Industrial Smoke Emissions

Industrial smoke emissions pose a significant concern to human health. Prior works have shown that using Computer Vision (CV) techniques to identify smoke as visual evidence can influence the attitude of regulators and empower citizens to pursue environmental justice. However, existing datasets are not of sufficient quality nor quantity to train the robust CV models needed to support air quality advocacy. We introduce RISE, the first large-scale video dataset for Recognizing Industrial Smoke Emissions. We adopted a citizen science approach to collaborate with local community members to annotate whether a video clip has smoke emissions. Our dataset contains 12,567 clips from 19 distinct views from cameras that monitored three industrial facilities. These daytime clips span 30 days over two years, including all four seasons. We ran experiments using deep neural networks to establish a strong performance baseline and reveal smoke recognition challenges. Our survey study discussed community feedback, and our data analysis displayed opportunities for integrating citizen scientists and crowd workers into the application of Artificial Intelligence for social good.Comment: Technical repor

2VT: Visions, Technologies, and Visions of Technologies for Understanding Human Scale Spaces

Spatial experience is an important subject in various fields, and in HCI it has been mostly investigated in the urban scale. Research on human scale spaces has focused mostly on the personal meaning or aesthetic and embodied experiences in the space. Further, spatial experience is increasingly topical in envisioning how to build and interact with technologies in our everyday lived environments, particularly in so-called smart cities. This workshop brings researchers and practitioners from diverse fields to collaboratively discover new ways to understand and capture human scale spatial experience and envision its implications to future technological and creative developments in our habitats. Using a speculative design approach, we sketch concrete solutions that could help to better capture critical features of human scale spaces and allow for unique possibilities for aspects such as urban play. As a result, we hope to contribute a road map for future HCI research on human scale spatial experience and its application

Engaging communities in addressing air quality: a scoping review

Abstract Background Exposure to air pollution has a detrimental effect on health and disproportionately affects people living in socio-economically disadvantaged areas. Engaging with communities to identify concerns and solutions could support organisations responsible for air quality control, improve environmental decision-making, and widen understanding of air quality issues associated with health. This scoping review aimed to provide an overview of approaches used to engage communities in addressing air quality and identify the outcomes that have been achieved. Methods Searches for studies that described community engagement in air quality activities were conducted across five databases (Academic Search Complete, CABI, GreenFILE, MEDLINE, Web of Science). Data on study characteristics, community engagement approach, and relevant outcomes were extracted. The review process was informed by a multi-stakeholder group with an interest in and experience of community engagement in air quality. Thirty-nine papers from thirty studies were included in the final synthesis. Conclusion A range of approaches have been used to engage communities in addressing air quality, most notably air quality monitoring. Positive outcomes included increased awareness, capacity building, and changes to organisational policy and practice. Longer-term projects and further exploration of the impact of community engagement on improving air quality and health are needed as reporting on these outcomes was limited. </jats:sec

Earth as Interface: Exploring chemical senses with Multisensory HCI Design for Environmental Health Communication

As environmental problems intensify, the chemical senses -that is smell and taste, are the most relevantsenses to evidence them.As such, environmental exposure vectors that can reach human beings comprise air,food, soil and water[1].Within this context, understanding the link between environmental exposures andhealth[2]is crucial to make informed choices, protect the environment and adapt to new environmentalconditions[3].Smell and taste lead therefore to multi-sensorial experiences which convey multi-layered information aboutlocal and global events[4]. However, these senses are usually absent when those problems are represented indigital systems. The multisensory HCIdesign framework investigateschemical sense inclusion withdigital systems[5]. Ongoing efforts tackledigitalization of smell and taste for digital delivery, transmission or substitution [6]. Despite experimentsproved technological feasibility, its dissemination depends on relevant applicationdevelopment[7].This thesis aims to fillthose gaps by demonstratinghow chemical senses provide the means to link environment and health based on scientific andgeolocation narratives [8], [9],[10]. We present a Multisensory HCI design process which accomplished symbolicdisplaying smell and taste and led us to a new multi-sensorial interaction system presented herein. We describe the conceptualization, design and evaluation of Earthsensum, an exploratory case study project.Earthsensumoffered to 16 participants in the study, environmental smell and taste experiences about real geolocations to participants of the study. These experiences were represented digitally using mobilevirtual reality (MVR) and mobile augmented reality (MAR). Its technologies bridge the real and digital Worlds through digital representations where we can reproduce the multi-sensorial experiences. Our study findings showed that the purposed interaction system is intuitive and can lead not only to a betterunderstanding of smell and taste perception as also of environmental problems. Participants comprehensionabout the link between environmental exposures and health was successful and they would recommend thissystem as education tools. Our conceptual design approach was validated and further developments wereencouraged.In this thesis,we demonstratehow to applyMultisensory HCI methodology to design with chemical senses. Weconclude that the presented symbolic representation model of smell and taste allows communicatingtheseexperiences on digital platforms. Due to its context-dependency, MVR and MAR platforms are adequatetechnologies to be applied for this purpose.Future developments intend to explore further the conceptual approach. These developments are centredon the use of the system to induce hopefully behaviourchange. Thisthesisopens up new application possibilities of digital chemical sense communication,Multisensory HCI Design and environmental health communication.À medida que os problemas ambientais se intensificam, os sentidos químicos -isto é, o cheiroe sabor, são os sentidos mais relevantes para evidenciá-los. Como tais, os vetores de exposição ambiental que podem atingir os seres humanos compreendem o ar, alimentos, solo e água [1]. Neste contexto, compreender a ligação entre as exposições ambientais e a saúde [2] é crucial para exercerescolhas informadas, proteger o meio ambiente e adaptar a novas condições ambientais [3]. O cheiroe o saborconduzemassima experiências multissensoriais que transmitem informações de múltiplas camadas sobre eventos locais e globais [4]. No entanto, esses sentidos geralmente estão ausentes quando esses problemas são representados em sistemas digitais. A disciplina do design de Interação Humano-Computador(HCI)multissensorial investiga a inclusão dossentidos químicos em sistemas digitais [9]. O seu foco atual residena digitalização de cheirose sabores para o envio, transmissão ou substituiçãode sentidos[10]. Apesar dasexperimentaçõescomprovarem a viabilidade tecnológica, a sua disseminação está dependentedo desenvolvimento de aplicações relevantes [11]. Estatese pretendepreencher estas lacunas ao demonstrar como os sentidos químicos explicitama interconexãoentre o meio ambiente e a saúde, recorrendo a narrativas científicas econtextualizadasgeograficamente[12], [13], [14]. Apresentamos uma metodologiade design HCImultissensorial que concretizouum sistema de representação simbólica de cheiro e sabor e nos conduziu a um novo sistema de interação multissensorial, que aqui apresentamos. Descrevemos o nosso estudo exploratório Earthsensum, que integra aconceptualização, design e avaliação. Earthsensumofereceu a 16participantes do estudo experiências ambientais de cheiro e sabor relacionadas com localizações geográficasreais. Essas experiências foram representadas digitalmente através derealidade virtual(VR)e realidade aumentada(AR).Estas tecnologias conectamo mundo real e digital através de representações digitais onde podemos reproduzir as experiências multissensoriais. Os resultados do nosso estudo provaramque o sistema interativo proposto é intuitivo e pode levar não apenas a uma melhor compreensão da perceção do cheiroe sabor, como também dos problemas ambientais. O entendimentosobre a interdependência entre exposições ambientais e saúde teve êxitoe os participantes recomendariam este sistema como ferramenta para aeducação. A nossa abordagem conceptual foi positivamentevalidadae novos desenvolvimentos foram incentivados. Nesta tese, demonstramos como aplicar metodologiasde design HCImultissensorialpara projetar com ossentidos químicos. Comprovamosque o modelo apresentado de representação simbólica do cheiroe do saborpermite comunicar essas experiênciasem plataformas digitais. Por serem dependentesdocontexto, as plataformas de aplicações emVR e AR são tecnologias adequadaspara este fim.Desenvolvimentos futuros pretendem aprofundar a nossa abordagemconceptual. Em particular, aspiramos desenvolvera aplicaçãodo sistema para promover mudanças de comportamento. Esta tese propõenovas possibilidades de aplicação da comunicação dos sentidos químicos em plataformas digitais, dedesign multissensorial HCI e de comunicação de saúde ambiental

Using interpretable machine learning for indoor CO₂ level prediction and occupancy estimation

Management and monitoring of rooms’ environmental conditions is a good step towards achieving energy efficiency and a healthy indoor environment. However, studies indicate that some of the current methods used in environmental room monitoring are faced with some challenges such as high cost and lack of privacy. As a result, there is need to use a method that is simpler, reliable, affordable and without any privacy issues. Therefore, the aims of this thesis were: (i) to predict future CO₂ levels using environmental sensor data, (ii) to determine room occupancy using environmental sensor data and (iii) to create a prototype dashboard for possible future room management based on the models developed for room occupancy and CO₂ prediction. Machine learning methods were used and these included: Gradient Boosting ensemble model (GB), Long Short-Term Memory recurrent neural network model (LSTM) and Facebook Prophet model for time series (Prophet). The sensor data were recorded from three different office locations (two test sites at a university and a real-world commercial office in Glasgow, Scotland, UK). The results of the analysis show that with LSTM method, a Root Mean Square Error (RMSE) (absolute fit of the model results to the observed data) of 0.0682 could be achieved for two-hour time interval CO₂ prediction and with GB, of 82% accuracy could be achieved for proposed room occupancy estimation. Furthermore, as the model understanding was raised as a key issue, interpretable machine learning methods (SHapley Additive exPlanation. (SHAP) and Local Model-agnostic explanations. (LIME)) were used to interpret room occupancy results obtained by GB model. In addition a dashboard was designed and prototyped to show room environmental data, predicted CO₂ levels and estimated room occupancy based on what the sensor data and models might provide for people managing rooms in different settings. The proposed dashboard that was designed in this research was evaluated by interested participants and their responses show that the proposed dashboard could potentially offer inputs to building management towards the control of heating, ventilation and air-conditioning systems. This in turn could lead to improved energy efficiency, better planning of shared spaces in buildings, potentially reducing energy and operational costs, improved environmental conditions for room occupants; potentially leading to improved health, reduced risks, enhanced comfort and improved productivity. It is advised that further studies should be conducted at multiple locations to demonstrate generalisation of the results of the proposed model. In addition, the end benefits of the model could be assessed through applying its outputs to enhance the control of HVAC systems, room management systems and safety systems. The health and productivity of the occupants could be monitored in detail to identify whether resulting environmental improvements deliver improvements in health and productivity. The findings of this research contribute new knowledge that could be used to achieve reliable results in room occupancy estimation using machine learning approach.Management and monitoring of rooms’ environmental conditions is a good step towards achieving energy efficiency and a healthy indoor environment. However, studies indicate that some of the current methods used in environmental room monitoring are faced with some challenges such as high cost and lack of privacy. As a result, there is need to use a method that is simpler, reliable, affordable and without any privacy issues. Therefore, the aims of this thesis were: (i) to predict future CO₂ levels using environmental sensor data, (ii) to determine room occupancy using environmental sensor data and (iii) to create a prototype dashboard for possible future room management based on the models developed for room occupancy and CO₂ prediction. Machine learning methods were used and these included: Gradient Boosting ensemble model (GB), Long Short-Term Memory recurrent neural network model (LSTM) and Facebook Prophet model for time series (Prophet). The sensor data were recorded from three different office locations (two test sites at a university and a real-world commercial office in Glasgow, Scotland, UK). The results of the analysis show that with LSTM method, a Root Mean Square Error (RMSE) (absolute fit of the model results to the observed data) of 0.0682 could be achieved for two-hour time interval CO₂ prediction and with GB, of 82% accuracy could be achieved for proposed room occupancy estimation. Furthermore, as the model understanding was raised as a key issue, interpretable machine learning methods (SHapley Additive exPlanation. (SHAP) and Local Model-agnostic explanations. (LIME)) were used to interpret room occupancy results obtained by GB model. In addition a dashboard was designed and prototyped to show room environmental data, predicted CO₂ levels and estimated room occupancy based on what the sensor data and models might provide for people managing rooms in different settings. The proposed dashboard that was designed in this research was evaluated by interested participants and their responses show that the proposed dashboard could potentially offer inputs to building management towards the control of heating, ventilation and air-conditioning systems. This in turn could lead to improved energy efficiency, better planning of shared spaces in buildings, potentially reducing energy and operational costs, improved environmental conditions for room occupants; potentially leading to improved health, reduced risks, enhanced comfort and improved productivity. It is advised that further studies should be conducted at multiple locations to demonstrate generalisation of the results of the proposed model. In addition, the end benefits of the model could be assessed through applying its outputs to enhance the control of HVAC systems, room management systems and safety systems. The health and productivity of the occupants could be monitored in detail to identify whether resulting environmental improvements deliver improvements in health and productivity. The findings of this research contribute new knowledge that could be used to achieve reliable results in room occupancy estimation using machine learning approach