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Knowledge Engineering Framework for IoT Robotics Applied to Smart Healthcare and Emotional Well-Being

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Abstract

Social companion robots are getting more attention to assist elderly people to stay independent at home and to decrease their social isolation. When developing solutions, one remaining challenge is to design the right applications that are usable by elderly people. For this purpose, co-creation methodologies involving multiple stakeholders and a multidisciplinary researcher team (e.g., elderly people, medical professionals, and computer scientists such as roboticists or IoT engineers) are designed within the ACCRA (Agile Co-Creation of Robots for Ageing) project. This paper will address this research question: How can Internet of Robotic Things (IoRT) technology and co-creation methodologies help to design emotional-based robotic applications? This is supported by the ACCRA project that develops advanced social robots to support active and healthy ageing, co-created by various stakeholders such as ageing people and physicians. We demonstra this with three robots, Buddy, ASTRO, and RoboHon, used for daily life, mobility, and conversation. The three robots understand and convey emotions in real-time using the Internet of Things and Artificial Intelligence technologies (e.g., knowledge-based reasoning).

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Notes

  1. https://ifr.org/news/service-robotics.

  2. https://www.accra-project.org/en/sample-page/.

  3. https://www.who.int/ageing/sdgs/en/.

  4. https://www.nhs.uk/conditions/stress-anxiety-depression/loneliness-in-older-people/.

  5. https://medicalfuturist.com/the-top-12-social-companion-robots/.

  6. http://www.bluefrogrobotics.com/robot/.

  7. https://www.w3.org/WoT/.

  8. https://www.abiresearch.com/market-research/product/1019712-the-internet-of-robotic-things/.

  9. https://www.accra-project.org/en/.

  10. https://www.sharp.co.uk/cps/rde/xchg/gb/hs.xsl/-/html/robohon-an-adventure-in-robotics-what-you-need-to-know.htm.

  11. https://www.accra-project.org/en/robots-for-ageing/.

  12. http://agilemanifesto.org/history.html.

  13. http://iswc2018.semanticweb.org/call-for-resources-track-papers/.

  14. https://www.ai4eu.eu/ke4wot.

  15. http://lov4iot.appspot.com/perfectoOnto/getOntoDomain/?domain=Robotic.

  16. https://www.accra-project.org/en/life/.

  17. https://goo.gl/mFUPVO.

  18. https://ec.europa.eu/eusurvey/runner/OntologyLandscapeTemplate.

  19. https://bit.ly/3fRpQUU.

References

  1. ACCRA D1.3 Methodology Handbook and Instruction Videos

  2. Abaalkhail R, Guthier B, Alharthi R, El Saddik A (2018) Survey on ontologies for affective states and their influences. Semantic web 9(4):441–458

    Article  Google Scholar 

  3. Afzal M, Ali SI, Ali R, Hussain M, Ali T, Khan WA, Amin MB, Kang BH, Lee S (2018) Personalization of wellness recommendations using contextual interpretation. Expert Syst Appl 96:506–521

    Article  Google Scholar 

  4. Ahmed F (2017) An internet of things (IoT) application for predicting the quantity of future heart attack patients. Int J Comput Appl 164(6):36–40

    Google Scholar 

  5. Al-Taee MA, Al-Nuaimy W, Muhsin ZJ, Al-Ataby A (2016) Robot assistant in management of diabetes in children based on the internet of things. IEEE Internet Things J 4(2):437–445

    Article  Google Scholar 

  6. American Diabetes Association (2019) Standards of medical care in diabetes-2019, abridged for primary care providers

  7. Angelidou R (2015) Development of a portable system for collecting and processing bio-signals and sounds to support the diagnosis of sleep Apnea. Master’s thesis

  8. Arguedas M, Xhafa F, Daradoumis T, Caballe S (2015) An ontology about emotion awareness and affective feedback in elearning. In: Proceedings of the 2015 international conference on intelligent networking and collaborative systems, IEEE, pp 156–163

  9. Azkune G, Orduna P, Laiseca X, Castillejo E, López-de Ipiña D, Loitxate M, Azpiazu J (2013) Semantic framework for social robot self-configuration. Sensors 13(6):7004–7020

    Article  Google Scholar 

  10. Balakirsky S, Kootbally Z, Schlenoff C, Kramer T, Gupta S (2012) An industrial robotic knowledge representation for kit building applications. In: Proceedings of the 2012 IEEE/RSJ international conference on intelligent robots and systems, IEEE, pp 1365–1370

  11. Baldoni M, Baroglio C, Patti V, Rena P (2012) From tags to emotions: ontology-driven sentiment analysis in the social semantic web. Intelligenza Artificiale 6(1):41–54

    Article  Google Scholar 

  12. Barrett LF (2017) How emotions are made: the secret life of the brain. Houghton Mifflin Harcourt, Boston

    Google Scholar 

  13. Bauer M, Baqa H, Bilbao S, Corchero A, Daniele L, Esnaola I, Fernandez I, Franberg O, Garcia-Castro R, Girod-Genet M, Guillemin P, Gyrard A, Kaed CE, Kung A, Lee J, Lefrançois M, Li W, Raggett D, Wetterwald M (2019) Semantic IoT solutions: a developer perspective (semantic interoperability white paper part I)

  14. Benta KI, Rarău A, Cremene M (2007) Ontology based affective context representation. In: Proceedings of the 2007 Euro American conference on telematics and information systems, pp 1–9

  15. Bermejo-Alonso J, Sanz R, Rodríguez M, Hernández C (2010) An ontological framework for autonomous systems modelling. Int J Adv Intel Syst 3(3):4

    Google Scholar 

  16. Berthelon F, Sander P (2013) Emotion ontology for context awareness. In: Proceedings of the 2013 IEEE 4th international conference on cognitive infocommunications (CogInfoCom), IEEE, pp 59–64

  17. Breuning LG (2015) Habits of a happy brain: retrain your brain to boost your serotonin, dopamine, oxytocin, and endorphin levels. Simon and Schuster, New York

    Google Scholar 

  18. Budgen D, Brereton P (2006) Performing systematic literature reviews in software engineering. In: Proceedings of the 28th international conference on Software engineering, pp 1051–1052

  19. Budner P, Eirich J, Gloor PA (2017) Making you happy makes me happy-measuring individual mood with smartwatches. arXiv preprint arXiv:1711.06134

  20. Chang KH, Fisher D, Canny J, Hartmann B (2011) Hows my mood and stress? An efficient speech analysis library for unobtrusive monitoring on mobile phones. In: Proceedings of the 6th international conference on body area networks, pp 71–77

  21. Chatterjee R, Matsuno F (2005) Robot description ontology and disaster scene description ontology: analysis of necessity and scope in rescue infrastructure context. Adv Robot 19(8):839–859

    Article  Google Scholar 

  22. Chella A, Cossentino M, Pirrone R, Ruisi A (2002) Modeling ontologies for robotic environments. In: Proceedings of the 14th international conference on Software engineering and knowledge engineering, pp 77–80

  23. Church K, Hoggan E, Oliver N (2010) A study of mobile mood awareness and communication through mobimood. In: Proceedings of the 6th Nordic conference on human-computer interaction: extending boundaries, pp 128–137

  24. Commission E (2020) White paper on artificial intelligence: a European approach to excellence and trust

  25. Consortium A (2020) D5.3 platform environment for marketplace1

  26. Coviello L, Cavallo F, Limosani R, Rovini E, Fiorini L (2019) Machine learning based physical human-robot interaction for walking support of frail people. In: Proceedings of the 2019 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC), IEEE, pp 3404–3407

  27. Dhouib S, Du Lac N, Farges JL, Gerard S, Hemaissia-Jeannin M, Lahera-Perez J, Millet S, Patin B, Stinckwich S (2011) Control architecture concepts and properties of an ontology devoted to exchanges in mobile robotics. In: Proceedings of the 6th national conference on control architectures of robots, p 24

  28. Dhouib S, Kchir S, Stinckwich S, Ziadi T, Ziane M (2012) Robotml, a domain-specific language to design, simulate and deploy robotic applications. International conference on simulation, modeling, and programming for autonomous robots. Springer, New York, pp 149–160

    Chapter  Google Scholar 

  29. Dogmus Z, Papantoniou A, Kilinc M, Yildirim SA, Erdem E, Patoglu V (2013) Rehabilitation robotics ontology on the cloud. In: Proceedings of the 2013 IEEE 13th international conference on rehabilitation robotics (ICORR), IEEE, pp 1–6

  30. Dogmus Z, Erdem E, Patoglu V (2015) Rehabrobo-onto: design, development and maintenance of a rehabilitation robotics ontology on the cloud. Robot Comput Integ Manuf 33:100–109

    Article  Google Scholar 

  31. Donofrio G, Fiorini L, Hoshino H, Matsumori A, Okabe Y, Tsukamoto M, Limosani R, Vitanza A, Greco F, Greco A et al (2019) Assistive robots for socialization in elderly people: results pertaining to the needs of the users. Aging Clin Exp Res 31(9):1313–1329

    Article  Google Scholar 

  32. Eckman P, Davidson RJ (1994) The nature of emotion. Oxford University, New York

    Google Scholar 

  33. Ekman P, Yamey G (2004) Emotions revealed: recognising facial expressions: in the first of two articles on how recognising faces and feelings can help you communicate, paul ekman discusses how recognising emotions can benefit you in your professional life. Stud BMJ 12:140–142

    Google Scholar 

  34. Elizabeth BNS, Stappers PJ (2012) Convivial toolbox: generative research for the front end of design

  35. Eyharabide V, Amandi A, Courgeon M, Clavel C, Zakaria C, Martin JC (2011) An ontology for predicting students’ emotions during a quiz. Comparison with self-reported emotions. In: Proceedings of the 2011 IEEE workshop on affective computational intelligence (WACI), IEEE, pp 1–8

  36. Fiorini L, D’Onofrio G, Rovini E, Sorrentino A, Coviello L, Limosani R, Sancarlo D, Cavallo F (2019) A robot-mediated assessment of tinetti balance scale for sarcopenia evaluation in frail elderly. In: Proceedings of the 2019 28th IEEE international conference on robot and human interactive communication (RO-MAN), IEEE, pp 1–6

  37. Francisco V, Gervás P, Peinado F (2007) Ontological reasoning to configure emotional voice synthesis. International conference on web reasoning and rule systems. Springer, New York, pp 88–102

    Chapter  Google Scholar 

  38. Futami K, Yanagisawa Y, Hoshino H, Matsumori A, Tsukamoto M, Kotani D, Okabe Y (2019) Data distribution infrastructure and applications for robotic therapy for blind elderly. In: Adjunct proceedings of the 2019 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2019 ACM international symposium on wearable computers, pp 61–64

  39. Garcia-Ceja E, Riegler M, Nordgreen T, Jakobsen P, Oedegaard KJ, Tørresen J (2018) Mental health monitoring with multimodal sensing and machine learning: a survey. Pervasive Mob Comput 51:1–26

    Article  Google Scholar 

  40. Garcia-Ceja E, et al (2016) Automatic stress detection in working environments from smartphones accelerometer data: a first step. J Biomed Health Inform (IF: 385 in 2017)

  41. García-Rojas A, et al (2006) Emotional body expression parameters in virtual human ontology

  42. Ghafurian M, Ellard C, Dautenhahn K (2020) Social companion robots to reduce isolation: a perception change due to covid-19. arXiv preprint arXiv:2008.05382

  43. Gil R, Virgili-Gomá J, García R, Mason C (2015) Emotions ontology for collaborative modelling and learning of emotional responses. Comput Hum Behav 51:610–617

    Article  Google Scholar 

  44. Gonçalves PJ (2016) Ontologies applied to surgical robotics. Robot 2015: second Iberian robotics conference. Springer, New York, pp 479–489

    Chapter  Google Scholar 

  45. Grassi M (2009) Developing heo human emotions ontology. European workshop on biometrics and identity management. Springer, New York, pp 244–251

    Google Scholar 

  46. Grea A, Saraydaryan J, Jumel F (XXXX) A robotic and automation services ontology

  47. Group TW (1998) The world health organization quality of life assessment (whoqol): development and general psychometric properties. Soc Sci Med 46(12):1569–1585

    Article  Google Scholar 

  48. Gyrard A, Sheth A (2019) IAMHAPPY: towards an IoT knowledge-based cross-domain well-being recommendation system for everyday happiness

  49. Gyrard A, Bonnet C, Boudaoud K, Serrano M (2016) LOV4IoT: a second life for ontology-based domain knowledge to build semantic web of things applications. In: IEEE international conference on future internet of things and cloud

  50. Gyrard A, Serrano M, Datta S, Jares J, Intizar A (2017) Sensor-based linked open rules (S-LOR): an automated rule discovery approach for IoT applications and its use in smart cities. In: Smart City Workshop (AW4city) in conjunction WWW, ACM

  51. Gyrard A, Gaur M, Thirunarayan K, Sheth A, Shekarpour S (2018) Personalized health knowledge graph. In: Proceedings of the 1st workshop on contextualized knowledge graph (CKG) co-located with international semantic web conference (ISWC), 8–12 October 2018, Monterey, USA

  52. Gyrard A, Atemezing G, Serrano M (2021) PerfectO: an online toolkit for improving quality, accessibility, and classification of domain-based ontologies. Springer, New York

    Google Scholar 

  53. Haidegger T, Barreto M, Gonçalves P, Habib MK, Ragavan SKV, Li H, Vaccarella A, Perrone R, Prestes E (2013) Applied ontologies and standards for service robots. Robot Auton Syst 61(11):1215–1223

    Article  Google Scholar 

  54. Hastings J, Ceusters W, Smith B, Mulligan K (2011) The emotion ontology: enabling interdisciplinary research in the affective sciences. International and interdisciplinary conference on modeling and using context. Springer, New York, pp 119–123

    Chapter  Google Scholar 

  55. Honold F, Schüssel F, Panayotova K, Weber M (2012) The nonverbal toolkit: towards a framework for automatic integration of nonverbal communication into virtual environments. In: Proceedings of the 2012 eighth international conference on intelligent environments, IEEE, pp 243–250

  56. Hotz L, Neumann B, Von Riegen S, Worch N (2012) Using ontology-based experiences for supporting robot tasks-position paper. Machine learning for interactive systems: bridging the gap between language, motor p 17

  57. Hu G, Tay WP, Wen Y (2012) Cloud robotics: architecture, challenges and applications. IEEE Netw 26(3):21–28

    Article  Google Scholar 

  58. Jangid N, Sharma B (2016) Cloud computing and robotics for disaster management. In: Proceedings of the 2016 7th international conference on intelligent systems. Modelling and simulation (ISMS), IEEE, pp 20–24

  59. Kamilaris A, Botteghi N (2020) The penetration of internet of things in robotics: towards a web of robotic things. J Amb Intel Smart Environ (Preprint) 1–22

  60. Kehoe B, Patil S, Abbeel P, Goldberg K (2015) A survey of research on cloud robotics and automation. IEEE Trans Autom Sci Eng 12(2):398–409

    Article  Google Scholar 

  61. Kim JY, Liu N, Tan HX, Chu CH (2017) Unobtrusive monitoring to detect depression for elderly with chronic illnesses. IEEE Sens J 17(17):5694–5704

    Article  Google Scholar 

  62. Kitchenham B, Pretorius R, Budgen D, Brereton OP, Turner M, Niazi M, Linkman S (2010) Systematic literature reviews in software engineering: a tertiary study. Inform Softw Technol

  63. Koelstra S, et al (2012) DEAP: a database for emotion analysis; using physiological signals. IEEE Trans Affect Comput (IF: 7170 in 2020)

  64. Koubaa A (2015) Ros as a service: web services for robot operating system. J Softw Eng Robot 6(1):1–14

    Google Scholar 

  65. Lane ND, Mohammod M, Lin M, Yang X, Lu H, Ali S, Doryab A, Berke E, Choudhury T, Campbell A (2011) Bewell: a smartphone application to monitor, model and promote wellbeing. In: Proceedings of the 5th international ICST conference on pervasive computing technologies for healthcare, pp 23–26

  66. Laxminarayan P (2004) Exploratory analysis of human sleep data. PhD thesis, Worcester Polytechnic Institute

  67. Lemaignan S, Ros R, Mösenlechner L, Alami R, Beetz M (2010) Oro, a knowledge management platform for cognitive architectures in robotics. In: Proceedings of the 2010 IEEE/RSJ international conference on intelligent robots and systems, IEEE, pp 3548–3553

  68. Li X, Bilbao S, Martín-Wanton T, Bastos J, Rodriguez J (2017) Swarms ontology: a common information model for the cooperation of underwater robots. Sensors 17(3):569

    Article  Google Scholar 

  69. LiKamWa R, Liu Y, Lane ND, Zhong L (2013) Moodscope: building a mood sensor from smartphone usage patterns. In: Proceeding of the 11th annual international conference on mobile systems, applications, and services, pp 389–402

  70. Lim GH, Suh IH, Suh H (2011) Ontology-based unified robot knowledge for service robots in indoor environments. IEEE Trans Syst Man Cybern A Syst Hum

  71. Lim TP, Husain W, Zakaria N (2013) Recommender system for personalised wellness therapy. Int J Adv Comput Sci Appl 4

  72. Lin R, Liang C, Duan R, Chen Y, Tao C et al (2018) Visualized emotion ontology: a model for representing visual cues of emotions. BMC Med Inform Decis Mak 18(2):101–113

    Google Scholar 

  73. Lin Y, Jessurun J, De Vries B, Timmermans H (2011) Motivate: towards context-aware recommendation mobile system for healthy living. In: Proceedings of the 2011 5th international conference on pervasive computing technologies for healthcare (PervasiveHealth) and workshops, IEEE, pp 250–253

  74. López JM, Gil R, García R, Cearreta I, Garay N (2008) Towards an ontology for describing emotions. World summit on knowledge society. Springer, New York, pp 96–104

    Google Scholar 

  75. Lortal G, Dhouib S, Gérard S (2010) Integrating ontological domain knowledge into a robotic DSL. International conference on model driven engineering languages and systems. Springer, New York, pp 401–414

    Google Scholar 

  76. Lu H, Frauendorfer D, Rabbi M, Mast MS, Chittaranjan GT, Campbell AT, Gatica-Perez D, Choudhury T (2012) Stresssense: detecting stress in unconstrained acoustic environments using smartphones. In: Proceedings of the 2012 ACM conference on ubiquitous computing, pp 351–360

  77. Martins AI, Rosa AF, Queirós A, Silva A, Rocha NP (2015) European portuguese validation of the system usability scale (sus). Proc Comput Sci 67:293–300

    Article  Google Scholar 

  78. Mouradian C, Yangui S, Glitho RH (2018) Robots as-a-service in cloud computing: Search and rescue in large-scale disasters case study. In: Proceedings of the 2018 15th IEEE Annual consumer communications and networking conference (CCNC), IEEE, pp 1–7

  79. Murdock P, Bassbouss L, Bauer M, Alaya MB, Bhowmik R, Brett P, Chakraborty RN, Dadas M, Davies J, Diab W, et al. (2016) Semantic interoperability for the web of things. PhD thesis, Dépt. Réseaux et Service Multimédia Mobiles (Institut Mines-Télécom-Télécom

  80. Nocentini O, Fiorini L, Acerbi G, Sorrentino A, Mancioppi G, Cavallo F (2019) A survey of behavioral models for social robots. Robotics 8(3):54

    Article  Google Scholar 

  81. Nouh RM, Lee HH, Lee WJ, Lee JD (2019) A smart recommender based on hybrid learning methods for personal well-being services. Sensors 19(2):431

    Article  Google Scholar 

  82. Obrenovic Z, Garay N, López JM, Fajardo I, Cearreta I (2005) An ontology for description of emotional cues. International conference on affective computing and intelligent interaction. Springer, New York, pp 505–512

    Chapter  Google Scholar 

  83. Olivares-Alarcos A, Beßler D, Khamis A, Goncalves P, Habib MK, Bermejo J, Barreto M, Diab M, Rosell J, Quintas J, Olszewska J, Nakawala H, Pignaton E, Gyrard A, Borgo S, Alenya G, Beetz M, Li H (2019) A review and comparison of ontology-based approaches to robot autonomy

  84. Olszewska JI, Barreto M, Bermejo-Alonso J, Carbonera J, Chibani A, Fiorini S, Goncalves P, Habib M, Khamis A, Olivares A, et al (2017) Ontology for autonomous robotics. In: Proceedings of the 2017 26th IEEE international symposium on robot and human interactive communication (RO-MAN), IEEE, pp 189–194

  85. Onyeulo EB, Gandhi V (2020) What makes a social robot good at interacting with humans? Information 11(1):43

    Article  Google Scholar 

  86. Paulius D, Sun Y (2019) A survey of knowledge representation in service robotics. Robot Auton Syst 118:13–30

    Article  Google Scholar 

  87. Paull L, Severac G, Raffo GV, Angel JM, Boley H, Durst PJ, Gray W, Habib M, Nguyen B, Ragavan SV, et al (2012) Towards an ontology for autonomous robots. In: Proceedings of the 2012 IEEE/RSJ international conference on intelligent robots and systems, IEEE, pp 1359–1364

  88. Picard RW (2000) Affective computing. MIT press, Cambridge

    Book  Google Scholar 

  89. Prestes E, Carbonera JL, Fiorini SR, Jorge VA, Abel M, Madhavan R, Locoro A, Goncalves P, Barreto ME, Habib M et al (2013) Towards a core ontology for robotics and automation. Robot Auton Syst 61(11):1193–1204

    Article  Google Scholar 

  90. Prestes E, Fiorini SR, Carbonera J (2014) Core ontology for robotics and automation. In: Proceedings of the 18th workshop on knowledge representation and ontologies for robotics and automation, p 7

  91. Ptaszynski M, Rzepka R, Araki K, Momouchi Y (2012) A robust ontology of emotion objects. In: Proceedings of the eighteenth annual meeting of the association for natural language processing (NLP-2012), pp 719–722

  92. Rabbi M, Ali S, Choudhury T, Berke E (2011) Passive and in-situ assessment of mental and physical well-being using mobile sensors. In: Proceedings of the 13th international conference on Ubiquitous computing, pp 385–394

  93. Radulovic F, Milikic N (2009) Smiley ontology. In: Proceedings of the 1st international workshop on social networks interoperability

  94. Ray PP (2016) Internet of robotic things: concept, technologies, and challenges. IEEE Access 4:9489–9500

    Article  Google Scholar 

  95. Retto J (2017) Sophia, first citizen robot of the world

  96. Rizzo G, Tomassetti F, Vetro A, Ardito L, Torchiano M, Morisio M, Troncy R (2017) Semantic enrichment for recommendation of primary studies in a systematic literature review. Dig Scholar Hum 32(1):195–208

    Google Scholar 

  97. Roy Chowdhury A (2017) Iot and robotics: a synergy. PeerJ Preprints 5:e2760v1

  98. Sabri L, Bouznad S, Rama Fiorini S, Chibani A, Prestes E, Amirat Y (2018) An integrated semantic framework for designing context-aware internet of robotic things systems. Integ Comput Aided Eng 25(2):137–156

    Article  Google Scholar 

  99. Saha O, Dasgupta P (2018) A comprehensive survey of recent trends in cloud robotics architectures and applications. Robotics 7(3):47

    Article  Google Scholar 

  100. Sánchez-Rada JF, Iglesias CA (2016) Onyx: a linked data approach to emotion representation. Inform Process Manag 52(1):99–114

    Article  Google Scholar 

  101. Saraydaryan J, Jumel F, Guenard A (2014) Astro: architecture of services toward robotic objects. Int J Comput Sci Issues (IJCSI) 11(4):1

    Google Scholar 

  102. Saxena A, Jain A, Sener O, Jami A, Misra DK, Koppula HS (2014) Robobrain: large-scale knowledge engine for robots. arXiv preprint arXiv:1412.0691

  103. Schlenoff C, Messina E (2005) A robot ontology for urban search and rescue. In: Proceedings of the 2005 ACM workshop on Research in knowledge representation for autonomous systems, pp 27–34

  104. Seligman M (2012) Flourish: a visionary new understanding of happiness and well-being (book). Simon and Schuster, New York

    Google Scholar 

  105. Sener O (2016) Learning from large-scale visual data for robots. Cornell University, New York

    Google Scholar 

  106. Simoens P, Dragone M, Saffiotti A (2018) The internet of robotic things: a review of concept, added value and applications. Int J Adv Rob Syst 15(1):1729881418759424

    Google Scholar 

  107. Sykora M, Jackson T, O’Brien A, Elayan S (2013) Emotive ontology: extracting fine-grained emotions from terse, informal messages

  108. Tabassum H, Ahmed S (2016) Emotion: an ontology for emotion analysis. In: Proceedings of the 1st national conference on emerging trends and innovations in computing and technology, Karachi, Pakistan

  109. Tapia SAA, Gomez AHF, Corbacho JB, Ratte S, Torres-Diaz J, Torres-Carrion PV, Garcia JM (2014) A contribution to the method of automatic identification of human emotions by using semantic structures. In: Proceedings of the 2014 international conference on interactive collaborative learning (ICL), IEEE, pp 60–70

  110. Tenorth M, Beetz M (2013) KnowRob: A knowledge processing infrastructure for cognition-enabled robots. Int J Robot Res

  111. Tenorth M, Beetz M (2017) Representations for robot knowledge in the knowrob framework. Artif Intell 247:151–169

    Article  MathSciNet  MATH  Google Scholar 

  112. Tiddi I, Bastianelli E, Bardaro G, d’Aquin M, Motta E (2017) An ontology-based approach to improve the accessibility of ros-based robotic systems. In: Proceedings of the knowledge capture conference, pp 1–8

  113. Tiddi I, Bastianelli E, Daga E, Daquin M, Motta E (2020) Robot-city interaction: mapping the research landscape-a survey of the interactions between robots and modern cities. Int J Soc Robot 12(2):299–324

    Article  Google Scholar 

  114. Toselloa E, Fanb Z, Castroc AG, Pagelloa E (2018) RTASK: a cloud-based knowledge engine for robot task and motion planning

  115. Vermesan O, Bröring A, Tragos E, Serrano M, Bacciu D, Chessa S, Gallicchio C, Micheli A, Dragone M, Saffiotti A, et al (2017) Internet of robotic things: converging sensing/actuating, hypoconnectivity, artificial intelligence and iot platforms

  116. Vorobieva H, Soury M, Hède P, Leroux C, Morignot P (2010) Object recognition and ontology for manipulation with an assistant robot. International conference on smart homes and health telematics. Springer, New York, pp 178–185

    Google Scholar 

  117. Waibel M, Beetz M, Civera J, Dandrea R, Elfring J, Galvez-Lopez D, Haussermann K, Janssen R, Montiel J, Perzylo A et al (2011) A world wide web for robots. IEEE Robot Autom Mag 18(2):69–82

    Article  Google Scholar 

  118. Wang E, Kim YS, Kim HS, Son JH, Lee S, Suh IH (2005) Ontology modeling and storage system for robot context understanding. International conference on knowledge-based and intelligent information and engineering systems. Springer, New York, pp 922–929

    Google Scholar 

  119. Yacchirema DC, Sarabia-Jácome D, Palau CE, Esteve M (2018) A smart system for sleep monitoring by integrating iot with big data analytics. IEEE Access 6:35988–36001

    Article  Google Scholar 

  120. Yan J, Bracewell DB, Ren F, Kuroiwa S (2008) The creation of a Chinese emotion ontology based on hownet. Eng Lett 16:1

    Google Scholar 

  121. Yoon S, Sim JK, Cho YH (2016) A flexible and wearable human stress monitoring patch. Sci Rep 6(1):1–11

  122. Zander S, Ahmed N, Frank MT (2016) A survey about the usage of semantic technologies for the description of robotic components and capabilities. In: SAMI@ iKNOW

  123. Zhou D, Luo J, Silenzio VM, Zhou Y, Hu J, Currier G, Kautz H (2015) Tackling mental health by integrating unobtrusive multimodal sensing. In: Twenty-ninth AAAI conference on artificial intelligence

  124. Zweigle O, van de Molengraft R, d’Andrea R, Häussermann K (2009) Roboearth: connecting robots worldwide. In: Proceedings of the 2nd international conference on interaction sciences: information technology, culture and human, pp 184–191

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Acknowledgements

This work has partially received funding from the European Union’s Horizon 2020 research and innovation program (ACCRA) under grant agreement No. 738251, National Institute of Information and Communications Technology (NICT) of Japan, and AI4EU No. 825619. We would like to thanks ACCRA partners for their valuable comments. The opinions expressed are those of the authors and do not reflect those of the sponsors.

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Correspondence to Amelie Gyrard.

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Appendix

Appendix

Table 7 Well-being and IoT-based emotion applications (positive and negative) related work synthesis
Table 8 Related work synthesis: Ontology-based emotional projects and reasoning mechanisms employed

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Gyrard, A., Tabeau, K., Fiorini, L. et al. Knowledge Engineering Framework for IoT Robotics Applied to Smart Healthcare and Emotional Well-Being. Int J of Soc Robotics 15, 445–472 (2023). https://doi.org/10.1007/s12369-021-00821-6

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