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Digital ID framework for human-centric monitoring and control of smart buildings

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  • Architecture and Human Behavior
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Abstract

Smart offices can help employers attract and retain talented people and can positively impact well-being and productivity. Thanks to emerging technologies and increased computational power, smart buildings with a specific focus on personal experience are gaining attraction. Real-time monitoring and estimation of the human states are key to achieving individual satisfaction. Although some studies have incorporated real-time data into the buildings to predict occupants’ indoor experience (e.g., thermal comfort and work engagement), a detailed framework to integrate personal prediction models with building systems has not been well studied. Therefore, this paper proposes a framework to predict and track the real-time states of each individual and assist with decision-making (e.g., room assignment and indoor environment control). The core idea of the framework is to distinguish individuals by a new concept of Digital ID (DID), which is then integrated with recognition, prediction, recommendation, visualization, and feedback systems. The establishment of the DID database is discussed and a systematic prediction methodology to determine occupants’ indoor comfort is developed. Based on the prediction results, the Comfort Score Index (CSI) is proposed to give recommendations regarding the best-fit rooms for each individual. In addition, a visualization platform is developed for real-time monitoring of the indoor environment. To demonstrate the framework, a case study is presented. The thermal sensation is considered the reference for the room allocation, and two groups of people are used to demonstrate the framework in different scenarios. For one group of people, it is assumed that they are existing occupants with personal DID databases. People in another group are considered the new occupants without any personal database, and the public database is used to give initial guesses about their thermal sensations. The results show that the recommended rooms can provide better thermal environments for the occupants compared to the randomly assigned rooms. Furthermore, the recommendations regarding the indoor setpoints (temperature and lighting level) are illustrated using a work engagement prediction model. However, although specific indoor metrics are used in the case study to demonstrate the framework, it is scalable and can be integrated with any other algorithms and techniques, which can serve as a fundamental framework for future smart buildings.

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References

  • Adjabi I, Ouahabi A, Benzaoui A, et al. (2020). Past, present, and future of face recognition: A review. Electronics, 9: 1188.

    Article  Google Scholar 

  • André M, de Vecchi R, Lamberts R (2020). User-centered environmental control: A review of current findings on personal conditioning systems and personal comfort models. Energy and Buildings, 222: 110011.

    Article  Google Scholar 

  • Appel-Meulenbroek R, Groenen P, Janssen I (2011). An end-user’s perspective on activity-based office concepts. Journal of Corporate Real Estate, 13: 122–135.

    Article  Google Scholar 

  • Arakawa Martins L, Soebarto V, Williamson T (2022). A systematic review of personal thermal comfort models. Building and Environment, 207: 108502.

    Article  Google Scholar 

  • Arundell L, Sudholz B, Teychenne M, et al. (2018). The impact of activity based working (ABW) on workplace activity, eating behaviours, productivity, and satisfaction. International Journal of Environmental Research and Public Health, 15: 1005.

    Article  Google Scholar 

  • ASHRAE (2017). ASHRAE Standard 55-2017, Thermal Environmental Conditions for Human Occupancy. Atlanta USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.

    Google Scholar 

  • Braithwaite JJ, Watson DG, Jones R, et al. (2013). A guide for analysing electrodermal activity (EDA) & skin conductance responses (SCRs) for psychological experiments. Technical Report: Selective Attention & Awareness Laboratory (SAAL) Behavioural Brain Sciences Centre, University of Birmingham, UK.

    Google Scholar 

  • Calis G, Kuru M (2019). Statistical significance of gender and age on thermal comfort: a case study in Turkey. Proceedings of the Institution of Civil Engineers — Engineering Sustainability, 172: 40–51.

    Article  Google Scholar 

  • Chang K-M, Dzeng R-J, Wu Y (2018). An automated IoT visualization BIM platform for decision support in facilities management. Applied Sciences, 8: 1086.

    Article  Google Scholar 

  • Cheung T, Schiavon S, Parkinson T, et al. (2019). Analysis of the accuracy on PMV-PPD model using the ASHRAE Global Thermal Comfort Database II. Building and Environment, 153: 205–217.

    Article  Google Scholar 

  • Critchley HD, Melmed RN, Featherstone E, et al. (2001). Brain activity during biofeedback relaxation: A functional neuroimaging investigation. Brain, 124: 1003–1012.

    Article  Google Scholar 

  • Critchley H, Melmed RN, Featherstone E, et al. (2002). Volitional control of autonomic arousal: a functional magnetic resonance study. NeuroImage, 16: 909–919.

    Article  Google Scholar 

  • Deng M, Menassa CC, Fu B (2021a). Room match: Achieving thermal comfort through smart space allocation and environmental control in buildings. In: Proceedings of 2021 Winter Simulation Conference (WSC), Phoenix, AZ, USA.

  • Deng M, Menassa CC, Kamat VR (2021b). From BIM to digital twins: a systematic review of the evolution of intelligent building representations in the AEC-FM industry. Journal of Information Technology in Construction, 26: 58–83.

    Article  Google Scholar 

  • Deng M, Wang X, Menassa CC (2021c). Measurement and prediction of work engagement under different indoor lighting conditions using physiological sensing. Building and Environment, 203: 108098.

    Article  Google Scholar 

  • Dong B, Prakash V, Feng F, et al. (2019). A review of smart building sensing system for better indoor environment control. Energy and Buildings, 199: 29–46.

    Article  Google Scholar 

  • Engelen L, Chau J, Young S, et al. (2019). Is activity-based working impacting health, work performance and perceptions? A systematic review. Building Research & Information, 47: 468–479.

    Article  Google Scholar 

  • Ferreira J, Resende R, Martinho S (2018). Beacons and BIM models for indoor guidance and location. Sensors, 18: 4374.

    Article  Google Scholar 

  • Földváry Ličina V, Cheung T, Zhang H, et al. (2018). Development of the ASHRAE Global Thermal Comfort Database II. Building and Environment, 142: 502–512.

    Article  Google Scholar 

  • Frontczak M, Wargocki P (2011). Literature survey on how different factors influence human comfort in indoor environments. Building and Environment, 46: 922–937.

    Article  Google Scholar 

  • Frontczak M, Schiavon S, Goins J, et al. (2012). Quantitative relationships between occupant satisfaction and satisfaction aspects of indoor environmental quality and building design. Indoor Air, 22: 119–131.

    Article  Google Scholar 

  • Gan VJL, Deng M, Tan Y, et al. (2019). BIM-based framework to analyze the effect of natural ventilation on thermal comfort and energy performance in buildings. Energy Procedia, 158: 3319–3324.

    Article  Google Scholar 

  • Gan VJL, Luo H, Tan Y, et al. (2021). BIM and data-driven predictive analysis of optimum thermal comfort for indoor environment. Sensors, 21: 4401.

    Article  Google Scholar 

  • Gupta SK, Atkinson S, O’Boyle I, et al. (2016). BEES: Real-time occupant feedback and environmental learning framework for collaborative thermal management in multi-zone, multi-occupant buildings. Energy and Buildings, 125: 142–152.

    Article  Google Scholar 

  • Ho KF, Hirai HW, Kuo Y, et al. (2015). Indoor air monitoring platform and personal health reporting system: Big data analytics for public health research. In: Proceedings of 2015 IEEE International Congress on Big Data, New York, USA.

  • Huizenga C, Abbaszadeh S, Zagreus L, et al. (2006). Air quality and thermal comfort in office buildings: Results of a large indoor environmental quality survey. In: Proceedings of Healthy Buildings 2006, Lisbon, Portugal

  • Humanyze (2018). Data to Design”Bringing the Right Intelligence to Redesigning the Workplace. Available at https://cdn.worktechacademy.com/uploads/2018/12/WORKTECH-Academy-Humanyze-Data-to-Design-Report.pdf?_ga=2.40387493.628437762.1577049988-1390632762.1577049988.

  • Indraganti M, Rao KD (2010). Effect of age, gender, economic group and tenure on thermal comfort: A field study in residential buildings in hot and dry climate with seasonal variations. Energy and Buildings, 42: 273–281.

    Article  Google Scholar 

  • Indraganti M, Ooka R, Rijal HB (2015). Thermal comfort in offices in India: Behavioral adaptation and the effect of age and gender. Energy and Buildings, 103: 284–295.

    Article  Google Scholar 

  • Ioannou A, Itard L, Agarwal T (2018). In-situ real time measurements of thermal comfort and comparison with the adaptive comfort theory in Dutch residential dwellings. Energy and Buildings, 170: 229–241.

    Google Scholar 

  • Jahncke H, Hallman DM (2020). Objective measures of cognitive performance in activity based workplaces and traditional office types. Journal of Environmental Psychology, 72: 101503.

    Article  Google Scholar 

  • Kang K, Lin J, Zhang J (2018). BIM- and IoT-based monitoring framework for building performance management. Journal of Structural Integrity and Maintenance, 3: 254–261.

    Article  Google Scholar 

  • Karjalainen S (2007). Gender differences in thermal comfort and use of thermostats in everyday thermal environments. Building and Environment, 42: 1594–1603.

    Article  Google Scholar 

  • Kim JY, Chu CH, Shin SM (2014). ISSAQ: An integrated sensing systems for real-time indoor air quality monitoring. IEEE Sensors Journal, 14: 4230–4244.

    Article  Google Scholar 

  • Kim J, Schiavon S, Brager G (2018). Personal comfort models — A new paradigm in thermal comfort for occupant-centric environmental control. Building and Environment, 132: 114–124.

    Article  Google Scholar 

  • Lan L, Wargocki P, Wyon DP, et al. (2011). Effects of thermal discomfort in an office on perceived air quality, SBS symptoms, physiological responses, and human performance. Indoor Air, 21: 376–390.

    Article  Google Scholar 

  • Lee D, Cha G, Park S (2016). A study on data visualization of embedded sensors for building energy monitoring using BIM. International Journal of Precision Engineering and Manufacturing, 17: 807–814.

    Article  Google Scholar 

  • Li D, Menassa CC, Kamat VR (2017). Personalized human comfort in indoor building environments under diverse conditioning modes. Building and Environment, 126: 304–317.

    Article  Google Scholar 

  • Li D, Menassa CC, Kamat VR (2018). Non-intrusive interpretation of human thermal comfort through analysis of facial infrared thermography. Energy and Buildings, 176: 246–261.

    Article  Google Scholar 

  • Li D, Menassa CC, Kamat VR (2019). Robust non-intrusive interpretation of occupant thermal comfort in built environments with low-cost networked thermal cameras. Applied Energy, 251: 113336.

    Article  Google Scholar 

  • Li D, Menassa CC, Kamat VR, et al. (2020). HEAT—human embodied autonomous thermostat. Building and Environment, 178: 106879.

    Article  Google Scholar 

  • Liu S, Schiavon S, Das HP, et al. (2019). Personal thermal comfort models with wearable sensors. Building and Environment, 162: 106281.

    Article  Google Scholar 

  • Lu Q, Parlikad AK, Woodall P, et al. (2019). Developing a dynamic digital twin at a building level: Using Cambridge campus as case study. In: Proceedings of International Conference on Smart Infrastructure and Construction 2019 (ICSIC), Cambridge, UK.

  • Ma G, Liu Y, Shang S (2019). A building information model (BIM) and artificial neural network (ANN) based system for personal thermal comfort evaluation and energy efficient design of interior space. Sustainability, 11: 4972.

    Article  Google Scholar 

  • Machado FA, Dezotti CG, Ruschel RC (2019). The interface layer of a BIM-IoT prototype for energy consumption monitoring. Advances in Informatics and Computing in Civil and Construction Engineering. Cham, Switzerland: Springer International Publishing.

    Book  Google Scholar 

  • Marzouk M, Abdelaty A (2014). Monitoring thermal comfort in subways using building information modeling. Energy and Buildings, 84: 252–257.

    Article  Google Scholar 

  • Masoso OT, Grobler LJ (2010). The dark side of occupants’ behaviour on building energy use. Energy and Buildings, 42: 173–177.

    Article  Google Scholar 

  • Matthews G, Reinerman-Jones L, Abich JIV, et al. (2017). Metrics for individual differences in EEG response to cognitive workload: Optimizing performance prediction. Personality and Individual Differences, 118: 22–28.

    Article  Google Scholar 

  • Milton DK, Glencross PM, Walters MD (2000). Risk of sick leave associated with outdoor air supply rate, humidification, and occupant complaints. Indoor Air, 10: 212–221.

    Article  Google Scholar 

  • MIRVAC (2019). The Super Experience—Designing for Talent in the Digital Workplace. Available at https://cdn.worktechacademy.com/uploads/2019/09/WORKTECH-Academy-Mirvac-The-Super-Experience-White-Paper-2019.pdf?_ga=2.79728144.628437762.1577049988-1390632762.1577049988.

  • Nagai Y, Critchley HD, Featherstone E, et al. (2004). Activity in ventromedial prefrontal cortex covaries with sympathetic skin conductance level: A physiological account of a “default mode” of brain function. NeuroImage, 22: 243–251.

    Article  Google Scholar 

  • Nakama Y, Onishi Y, Iki K (2015). Development of building information management system with data collecting functions based on IoT technology. In: Proceeding of the 33rd eCAADe Conference Proceedings, Vienna, Austria.

  • Nkurikiyeyezu KN, Suzuki Y, Tobe Y, et al. (2017). Heart rate variability as an indicator of thermal comfort state. In: Proceedings of the 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Kanazawa, Japan.

  • Pasini D, Ventura SM, Rinaldi S, et al. (2016). Exploiting Internet of Things and building information modeling framework for management of cognitive buildings, In: Proceedings of 2016 IEEE International Smart Cities Conference (ISC2), Trento, Italy.

  • Pasini D (2018). Connecting BIM and IoT for addressing user awareness toward energy savings. Journal of Structural Integrity and Maintenance, 3: 243–253.

    Article  Google Scholar 

  • Rafsanjani HN, Ghahramani A (2020). Towards utilizing Internet of Things (IoT) devices for understanding individual occupants’ energy usage of personal and shared appliances in office buildings. Journal of Building Engineering, 27: 100948.

    Article  Google Scholar 

  • Rahaman MS, Pare H, Liono J, et al. (2019). OccuSpace: Towards a robust occupancy prediction system for activity based workplace. In: Proceedings of IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Kyoto, Japan.

  • Rathore MM, Ahmad A, Paul A, et al. (2016). Urban planning and building smart cities based on the Internet of Things using Big Data analytics. Computer Networks, 101: 63–80.

    Article  Google Scholar 

  • Revel GM, Arnesano M, Pietroni F (2014). A low-cost sensor for real-time monitoring of indoor thermal comfort for ambient assisted living. Ambient Assisted Living. Cham, Switzerland: Springer International Publishing, 2014: 3–12.

    Chapter  Google Scholar 

  • Riaz Z, Arslan M, Peña-Mora F (2015). Challenges in data management when monitoring confined spaces using BIM and wireless sensor technology. In: Proceedings of International Workshop on Computing in Civil Engineering, Austin, TX, USA.

  • Schweiker M, André M, Al-Atrash F, et al. (2020). Evaluating assumptions of scales for subjective assessment of thermal environments — Do laypersons perceive them the way, we researchers believe? Energy and Buildings, 211: 109761.

    Article  Google Scholar 

  • Sood T, Janssen P, Miller C (2020). Spacematch: Using environmental preferences to match occupants to suitable activity-based workspaces. Frontiers in Built Environment, 6: 113.

    Article  Google Scholar 

  • Stone PJ, Luchetti R (1985). Your office is where you are. Harvard Business Review, 63: 102–117.

    Google Scholar 

  • Tang S, Shelden DR, Eastman CM, et al. (2019). A review of building information modeling (BIM) and the Internet of Things (IoT) devices integration: Present status and future trends. Automation in Construction, 101: 127–139.

    Article  Google Scholar 

  • Teizer J, Wolf M, Golovina O, et al. (2017). Internet of Things (IoT) for integrating environmental and localization data in building information modeling (BIM). In: Proceedings of International Symposium on Automation and Robotics in Construction (ISARC 2017).

  • Thapa S (2019). Insights into the thermal comfort of different naturally ventilated buildings of Darjeeling, India—Effect of gender, age and BMI. Energy and Buildings, 193: 267–288.

    Article  Google Scholar 

  • Tran TV, Dang NT, Chung WY (2017). Battery-free smart-sensor system for real-time indoor air quality monitoring. Sensors and Actuators B: Chemical, 248: 930–939.

    Article  Google Scholar 

  • Wang X, Li D, Menassa CC, et al. (2019). Investigating the effect of indoor thermal environment on occupants’ mental workload and task performance using electroencephalogram. Building and Environment, 158: 120–132.

    Article  Google Scholar 

  • Wang Z, Wang J, He Y, et al. (2020a). Dimension analysis of subjective thermal comfort metrics based on ASHRAE Global Thermal Comfort Database using machine learning. Journal of Building Engineering, 29: 101120.

    Article  Google Scholar 

  • Wang Z, Zhang H, He Y, et al. (2020b). Revisiting individual and group differences in thermal comfort based on ASHRAE database. Energy and Buildings, 219: 110017.

    Article  Google Scholar 

  • Wargocki P, Wyon DP, Sundell J, et al. (2000). The effects of outdoor air supply rate in an office on perceived air quality, sick building syndrome (SBS) symptoms and productivity. Indoor Air, 10: 222–236.

    Article  Google Scholar 

  • Yang T, Bandyopadhyay A, O’Neill Z, et al. (2022). From occupants to occupants: A review of the occupant information understanding for building HVAC occupant-centric control. Building Simulation, 15: 913–932.

    Article  Google Scholar 

  • Zhan S, Chong A, Lasternas B (2021). Automated recognition and mapping of building management system (BMS) data points for building energy modeling (BEM). Building Simulation, 14: 43–52.

    Article  Google Scholar 

  • Zhang H (2003). Human thermal sensation and comfort in transient and non-uniform thermal environments. PhD Thesis, University of California, Berkeley, USA.

    Google Scholar 

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Acknowledgements

The authors would like to acknowledge the financial support for this research received from the U.S. National Science Foundation (NSF) CBET 1804321. Any opinions and findings in this paper are those of the authors and do not necessarily represent those of the NSF.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, 48109, USA

    Min Deng, Xi Wang & Carol C. Menassa

  2. Glenn Department of Civil Engineering, Clemson University, Clemson, SC, 29634, USA

    Da Li

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  1. Min Deng
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Correspondence to Carol C. Menassa.

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Deng, M., Wang, X., Li, D. et al. Digital ID framework for human-centric monitoring and control of smart buildings. Build. Simul. 15, 1709–1728 (2022). https://doi.org/10.1007/s12273-022-0902-3

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