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).















Similar content being viewed by others
Notes
References
ACCRA D1.3 Methodology Handbook and Instruction Videos
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
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
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
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
American Diabetes Association (2019) Standards of medical care in diabetes-2019, abridged for primary care providers
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
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
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
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
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
Barrett LF (2017) How emotions are made: the secret life of the brain. Houghton Mifflin Harcourt, Boston
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)
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
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
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
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
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
Budner P, Eirich J, Gloor PA (2017) Making you happy makes me happy-measuring individual mood with smartwatches. arXiv preprint arXiv:1711.06134
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
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
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
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
Commission E (2020) White paper on artificial intelligence: a European approach to excellence and trust
Consortium A (2020) D5.3 platform environment for marketplace1
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
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
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
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
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
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
Eckman P, Davidson RJ (1994) The nature of emotion. Oxford University, New York
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
Elizabeth BNS, Stappers PJ (2012) Convivial toolbox: generative research for the front end of design
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
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
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
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
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
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)
García-Rojas A, et al (2006) Emotional body expression parameters in virtual human ontology
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
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
Gonçalves PJ (2016) Ontologies applied to surgical robotics. Robot 2015: second Iberian robotics conference. Springer, New York, pp 479–489
Grassi M (2009) Developing heo human emotions ontology. European workshop on biometrics and identity management. Springer, New York, pp 244–251
Grea A, Saraydaryan J, Jumel F (XXXX) A robotic and automation services ontology
Group TW (1998) The world health organization quality of life assessment (whoqol): development and general psychometric properties. Soc Sci Med 46(12):1569–1585
Gyrard A, Sheth A (2019) IAMHAPPY: towards an IoT knowledge-based cross-domain well-being recommendation system for everyday happiness
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
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
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
Gyrard A, Atemezing G, Serrano M (2021) PerfectO: an online toolkit for improving quality, accessibility, and classification of domain-based ontologies. Springer, New York
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
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
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
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
Hu G, Tay WP, Wen Y (2012) Cloud robotics: architecture, challenges and applications. IEEE Netw 26(3):21–28
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
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
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
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
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
Koelstra S, et al (2012) DEAP: a database for emotion analysis; using physiological signals. IEEE Trans Affect Comput (IF: 7170 in 2020)
Koubaa A (2015) Ros as a service: web services for robot operating system. J Softw Eng Robot 6(1):1–14
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
Laxminarayan P (2004) Exploratory analysis of human sleep data. PhD thesis, Worcester Polytechnic Institute
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
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
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
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
Lim TP, Husain W, Zakaria N (2013) Recommender system for personalised wellness therapy. Int J Adv Comput Sci Appl 4
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
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
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
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
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
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
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
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
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
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
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
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
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
Onyeulo EB, Gandhi V (2020) What makes a social robot good at interacting with humans? Information 11(1):43
Paulius D, Sun Y (2019) A survey of knowledge representation in service robotics. Robot Auton Syst 118:13–30
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
Picard RW (2000) Affective computing. MIT press, Cambridge
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
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
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
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
Radulovic F, Milikic N (2009) Smiley ontology. In: Proceedings of the 1st international workshop on social networks interoperability
Ray PP (2016) Internet of robotic things: concept, technologies, and challenges. IEEE Access 4:9489–9500
Retto J (2017) Sophia, first citizen robot of the world
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
Roy Chowdhury A (2017) Iot and robotics: a synergy. PeerJ Preprints 5:e2760v1
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
Saha O, Dasgupta P (2018) A comprehensive survey of recent trends in cloud robotics architectures and applications. Robotics 7(3):47
Sánchez-Rada JF, Iglesias CA (2016) Onyx: a linked data approach to emotion representation. Inform Process Manag 52(1):99–114
Saraydaryan J, Jumel F, Guenard A (2014) Astro: architecture of services toward robotic objects. Int J Comput Sci Issues (IJCSI) 11(4):1
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
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
Seligman M (2012) Flourish: a visionary new understanding of happiness and well-being (book). Simon and Schuster, New York
Sener O (2016) Learning from large-scale visual data for robots. Cornell University, New York
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
Sykora M, Jackson T, O’Brien A, Elayan S (2013) Emotive ontology: extracting fine-grained emotions from terse, informal messages
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
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
Tenorth M, Beetz M (2013) KnowRob: A knowledge processing infrastructure for cognition-enabled robots. Int J Robot Res
Tenorth M, Beetz M (2017) Representations for robot knowledge in the knowrob framework. Artif Intell 247:151–169
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
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
Toselloa E, Fanb Z, Castroc AG, Pagelloa E (2018) RTASK: a cloud-based knowledge engine for robot task and motion planning
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
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
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
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
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
Yan J, Bracewell DB, Ren F, Kuroiwa S (2008) The creation of a Chinese emotion ontology based on hownet. Eng Lett 16:1
Yoon S, Sim JK, Cho YH (2016) A flexible and wearable human stress monitoring patch. Sci Rep 6(1):1–11
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
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
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
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.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
Rights and permissions
About this article
Cite this article
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
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12369-021-00821-6