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A Humanoid Social Robot Based Approach for Indoor Environment Quality Monitoring and Well-Being Improvement

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Abstract

The indoor environmental quality (IEQ) monitoring inside buildings where people spend most of their time is essential for ensuring their well-being. Traditional approaches based on Building Automation and Control Systems consider buildings equipped with many different sensors. Unfortunately, the sensors are not always placed for taking the measurements at the right positions. Besides, users could feel a negative perception due to continuous supervision. The present work proposes an approach based on a social humanoid robot that monitors indoor environmental quality. It friendly interacts with occupants providing appropriate suggestions. Particularly, the social robot has been endowed with cognitive capabilities that ground on (i) a proper ontology that formalizes the IEQ domain, (ii) on formal definitions of normative standards based on deontic logic, and (iii) on algorithms for reasoning about the compliance of the environment with the normative standards. The proposed approach has been experimentally verified in some offices of the National Research Council of Italy located in Palermo, and it has involved ten participants. It is worth noting that at the end of the measurement campaign appears that in some cases, compliance with the standard does not imply the user’s well-being and vice versa.

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Notes

  1. Hygienic Comfort is introduced for completeness of the ontology, although it is not addressed in this paper.

  2. It is worth noting that we did not use the Nao’s internal microphone because it does not give correct measurements, which are influenced by the noise produced by its cooling fan that is positioned near the microphone.

References

  1. Adeleke JA, Moodley D (2015) An ontology for proactive indoor environmental quality monitoring and control. In: Proceedings of the 2015 annual research conference on south African institute of computer scientists and information technologists, p 2. ACM

  2. Almeida RMSF, de Freitas VP, Delgado JMPQ (2015) Indoor environmental quality. In: School Buildings Rehabilitation. Springer, Cham, pp 5–17

  3. Arif M, Katafygiotou M, Mazroei A, Kaushik A, Elsarrag E et al (2016) Impact of indoor environmental quality on occupant well-being and comfort: a review of the literature. Int J Sustain Built Environ 5(1):1–11

    Article  Google Scholar 

  4. Bainbridge WA, Hart J, Kim ES, Scassellati B (2008) The effect of presence on human–robot interaction. In: RO-MAN 2008-the 17th IEEE international symposium on robot and human interactive communication, pp 701–706. IEEE

  5. Bellia L, Fragliasso F, Pedace A (2015) Lighting control systems: factors affecting energy savings evaluation. Energy Procedia 78:2645–2650

    Article  Google Scholar 

  6. Beranek LL, Blazier WE, Figwer JJ (1971) Preferred noise criterion (PNC) curves and their application to rooms. J Acoust Soc Am 50(5A):1223–1228

    Article  Google Scholar 

  7. Bonomolo M, Beccali M, Brano VL, Zizzo G (2017) A set of indices to assess the real performance of daylight-linked control systems. Energy Build 149:235–245

    Article  Google Scholar 

  8. Bordini RH, Hübner JF (2005) BDI agent programming in AgentSpeak using jason. In: International workshop on computational logic in multi-agent systems, pp 143–164. Springer

  9. Castaldo VL, Pigliautile I, Rosso F, Pisello AL, Cotana F (2017) Investigation of the impact of subjective and physical parameters on the indoor comfort of occupants: a case study in central italy. Energy Procedia 126:131–138

    Article  Google Scholar 

  10. Choi JH, Lee K (2018) Investigation of the feasibility of poe methodology for a modern commercial office building. Build Environ 143:591–604

    Article  Google Scholar 

  11. De Giuli V, Da Pos O, De Carli M (2012) Indoor environmental quality and pupil perception in italian primary schools. Build Environ 56:335–345

    Article  Google Scholar 

  12. Fabi V, Spigliantini G, Corgnati SP (2017) Insights on smart home concept and occupants interaction with building controls. Energy Procedia 111:759–769

    Article  Google Scholar 

  13. Frontczak M, Wargocki P (2011) Literature survey on how different factors influence human comfort in indoor environments. Build Environ 46(4):922–937

    Article  Google Scholar 

  14. Heath G, Mendell M (2002) Do indoor environments in schools influence student performance? a review of the literature. In: A compilation of papers for the indoor air 2002 conference in memory of Joan M. Daisey, vol 20

  15. Jafta N, Jeena P, Barregard L, Naidoo R (2015) Childhood tuberculosis and exposure to indoor air pollution: a systematic review and meta-analysis. Int J Tuberc Lung Disease 19(5):596–602

    Article  Google Scholar 

  16. Krogstie J, Sindre G (1996) Utilizing deontic operators in information systems specification. Requir Eng 1(4):210–237

    Article  Google Scholar 

  17. Leyzberg D, Spaulding S, Toneva M, Scassellati B (2012) The physical presence of a robot tutor increases cognitive learning gains. In: Proceedings of the annual meeting of the cognitive science society, vol 34

  18. Mantha BR, Menassa CC, Kamat VR (2018) Robotic data collection and simulation for evaluation of building retrofit performance. Autom Constr 92:88–102

    Article  Google Scholar 

  19. Matarić MJ (2017) Socially assistive robotics: human augmentation versus automation. Sci Robot 2(4):eaam5410

    Article  Google Scholar 

  20. Meerbeek B, te Kulve M, Gritti T, Aarts M, van Loenen E, Aarts E (2014) Building automation and perceived control: a field study on motorized exterior blinds in dutch offices. Build Environ 79:66–77

    Article  Google Scholar 

  21. Patrizia R, Lodato C (2019) A norm compliance approach for open and goal-directed intelligent systems. Complexity 2019:1–20

    Article  Google Scholar 

  22. Powers A, Kiesler S, Fussell S, Fussell S, Torrey C (2007) Comparing a computer agent with a humanoid robot. In: Proceedings of the ACM/IEEE international conference on human–robot interaction, pp 145–152. ACM

  23. Rao AS (1996) Agentspeak (l): BDI agents speak out in a logical computable language. In: European workshop on modelling autonomous agents in a multi-agent world, pp 42–55. Springer

  24. Reiter R (1978) On closed world data bases. Springer, Berlin

    Book  Google Scholar 

  25. Rossi S, Staffa M, Tamburro A (2018) Socially assistive robot for providing recommendations: comparing a humanoid robot with a mobile application. Int J Soc Robot 10(2):265–278

    Article  Google Scholar 

  26. Shiomi M, Shinozawa K, Nakagawa Y, Miyashita T, Sakamoto T, Terakubo T, Ishiguro H, Hagita N (2013) Recommendation effects of a social robot for advertisement-use context in a shopping mall. Int J Soc Robot 5(2):251–262

    Article  Google Scholar 

  27. Siciliano B, Khatib O (2016) Springer handbook of robotics. Springer, Berlin

    Book  Google Scholar 

  28. Standard A (1992) 55-92. thermal environmental comfort conditions for human occupancy. ASHRAE

  29. UNI, E.: 12464–1, (2011) Light and lighting. Lighting of work places. Part 1

  30. UNI, E.: 7730, ergonomia degli ambienti termici. Determinazione analitica e interpretazione del benessere termico mediante il calcolo degli indici PMV e PPD e dei criteri di benessere termico locale

  31. UNI E (2008) 15251: 2008. Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics 14

  32. Wargocki P (2018) Ventilation, thermal comfort, health and productivity. In: A handbook of sustainable building design and engineering. Routledge, pp 209–225

  33. Yu X, Liu L, Wu X, Wu X, Wang Z, Liu Q, Shi G (2017) On a post-occupancy evaluation study of effects of occupant behavior on indoor environment quality in college buildings in chongqing. Procedia Eng 205:623–627

    Article  Google Scholar 

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Funding

This work was conducted in the framework of the project AMICO - Medical Assistance In COntextual awareness - (CUP B46G18000390005). Facilitation marked by the identification code ARS01_0090.

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Correspondence to Patrizia Ribino.

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Ribino, P., Bonomolo, M., Lodato, C. et al. A Humanoid Social Robot Based Approach for Indoor Environment Quality Monitoring and Well-Being Improvement. Int J of Soc Robotics 13, 277–296 (2021). https://doi.org/10.1007/s12369-020-00638-9

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  • DOI: https://doi.org/10.1007/s12369-020-00638-9

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