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Assessing users’ emotion at interaction time: a multimodal approach with multiple sensors (2017)

  • Authors:
  • USP affiliated authors: UEYAMA, JO - ICMC
  • Unidades: ICMC
  • DOI: 10.1007/s00500-016-2115-0
  • Keywords: Interaction time; Sensors; Psychologists; Emotional components
  • Language: Inglês
  • Imprenta:
  • Source:
    • Título do periódico: Soft Computing
    • ISSN: 1432-7643
    • Volume/Número/Paginação/Ano: v. 21, n. 18, p. 5309-5323, Set. 2017
  • Online source accessDOI
    Informações sobre o DOI: 10.1007/s00500-016-2115-0 (Fonte: oaDOI API)
    • Este periódico é de assinatura
    • Este artigo NÃO é de acesso aberto
    • Cor do Acesso Aberto: closed

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    • ABNT

      GONÇALVES, Vinícius P; GIANCRISTOFARO, Gabriel T; FILHO, Geraldo P. Rocha; et al. Assessing users’ emotion at interaction time: a multimodal approach with multiple sensors. Soft Computing, Heidelberg, Springer, v. 21, n. 18, p. 5309-5323, 2017. Disponível em: < http://dx.doi.org/10.1007/s00500-016-2115-0 > DOI: 10.1007/s00500-016-2115-0.
    • APA

      Gonçalves, V. P., Giancristofaro, G. T., Filho, G. P. R., Johnson, T., Carvalho, V., Pessin, G., et al. (2017). Assessing users’ emotion at interaction time: a multimodal approach with multiple sensors. Soft Computing, 21( 18), 5309-5323. doi:10.1007/s00500-016-2115-0
    • NLM

      Gonçalves VP, Giancristofaro GT, Filho GPR, Johnson T, Carvalho V, Pessin G, Neris VP de, Ueyama J. Assessing users’ emotion at interaction time: a multimodal approach with multiple sensors [Internet]. Soft Computing. 2017 ; 21( 18): 5309-5323.Available from: http://dx.doi.org/10.1007/s00500-016-2115-0
    • Vancouver

      Gonçalves VP, Giancristofaro GT, Filho GPR, Johnson T, Carvalho V, Pessin G, Neris VP de, Ueyama J. Assessing users’ emotion at interaction time: a multimodal approach with multiple sensors [Internet]. Soft Computing. 2017 ; 21( 18): 5309-5323.Available from: http://dx.doi.org/10.1007/s00500-016-2115-0

    Referências citadas na obra
    Bradley MM, Lang PJ (1994) Measuring emotion: the self-assessment manikin and the semantic differential. J Behav Ther Exp Psychiatry 25(1):49–59
    Brave S, Nass C (2003) Emotion in human-computer interaction. In: Jacko JA, Sears A (eds) The human-computer interaction handbook. L. Erlbaum Associates Inc., Hillsdale, pp 81–96
    Chanel G, Kierkels JJ, Soleymani M, Pun T (2009) Short-term emotion assessment in a recall paradigm. Int J Hum Comput Stud 67(8):607–627
    Davidson RJ, Scherer KR, Goldsmith H (2003) Handbook of affective sciences. Oxford University Press, Oxford
    Desmet P (2004) Funology. In: Blythe MA, Overbeeke K, Monk AF, Wright PC (eds) Measuring emotion: development and application of an instrument to measure emotional responses to products. Kluwer Academic Publishers, Norwell, pp 111–123
    Duncker K, Lees LS (1945) On problem-solving. Psychol Monogr 58(5):i
    Ericsson K, Simon H (1993) Protocol analysis; verbal reports as data (revised edition). bradfordbooks
    Filho GPR, Ueyama J, Villas LA, Pinto AR, Gonalves VP, Pessin G, Pazzi RW, Braun T (2014) Nodepm: a remote monitoring alert system for energy consumption using probabilistic techniques. Sensors 14(1):848–867
    Fontaine JR, Poortinga YH, Setiadi B, Markam SS (2002) Cognitive structure of emotion terms in indonesia and the netherlands. Cogn Emot 16(1):61–86
    Frantzidis CA, Bratsas C, Klados MA, Konstantinidis E, Lithari CD, Vivas AB, Papadelis CL, Kaldoudi E, Pappas C, Bamidis PD (2010) On the classification of emotional biosignals evoked while viewing affective pictures: an integrated data-mining-based approach for healthcare applications. Inform Technol Biomed IEEE Trans 14(2):309–318
    Gonçalves VP, de Almeida Neris VP, Seraphini S, Dias TCM, Pessin G, Johnson T, Ueyama J (2015) Providing adaptive smartphone interfaces targeted at elderly people: an approach that takes into account diversity among the elderly. Univ Access Inf Soc 1–21. doi: 10.1007/s10209-015-0429-9
    Hou X, Liu Y, Sourina O, Tan YRE, Wang L, Mueller-Wittig W (2015) EEG based Stress Monitoring. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2015, pp 3110–3115. doi: 10.1109/SMC.2015.540
    Kahou SE, Bouthillier X, Lamblin P, Gulcehre C, Michalski V, Konda K, Jean S, Froumenty P, Dauphin Y, Boulanger-Lewandowski N, Ferrari RC, Mirza M, Warde-Farley D, Courville A, Vincent P, Memisevic R, Pal C, Bengio Y (2015) EmoNets: Multimodal deep learning approaches for emotion recognition in video. J Multimodal User Interfaces 1–13. doi: 10.1007/s12193-015-0195-2
    Kukolja D, Popović S, Horvat M, Kovač B, Ćosić K (2014) Comparative analysis of emotion estimation methods based on physiological measurements for real-time applications. Int J Hum Comput Stud 72(10):717–727
    Lan Z, Sourina O, Wang LP, Liu Y (2014) Stability of features in real-time EEG-based emotion recognition algorithm. In: International Conference on Cyberworlds (CW), 2014, pp 137–144. doi: 10.1109/CW.2014.27
    Lathia N, Rachuri K, Mascolo C, Roussos G (2013) Open source smartphone libraries for computational social science. In: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication. ACM, pp 911–920
    Li T, An C, Campbell AT, Zhou X (2015) Hilight: Hiding bits in pixel translucency changes. ACM SIGMOBILE Mobile Comput Commun Rev 18(3):62–70
    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. ACM. pp 389–402
    Mahlke S, Minge M (2008) Consideration of multiple components of emotions in human-technology interaction. In: Peter C, Beale R (eds) Affect and Emotion in Human-Computer Interaction, Lecture Notes in Computer Science, vol 4868. Springer, Berline, pp 51–62
    Mano LY, Giancristofaro GT, Faiçal B, Libralon G, Pessin G, Gomes PH, Ueyama J (2015) Exploiting the Use of Ensemble Classifiers to Enhance the Precision of User’s Emotion Classification. In: Proceedings of the 16th International Conference on Engineering Applications of Neural Networks (INNS)
    Mitchell TM (1997) Machine learning. McGraw Hill, Boston, MA
    Monard MC, Baranauskas JA (2003) Conceitos sobre aprendizado de máquina. Sistemas Inteligentes-Fundamentos e Aplicações 1:1
    Nahin ANH, Alam JM, Mahmud H, Hasan K (2014) Identifying emotion by keystroke dynamics and text pattern analysis. Behav Inform Technol 33(9):987–996
    Niedenthal PM, Krauth-Gruber S, Ric F (2006) Psychology of emotion: interpersonal, experiential, and cognitive approaches. Psychology Press, New York
    Pedersen C, Togelius J, Yannakakis GN (2010) Modeling player experience for content creation. IEEE Trans Comput Intell AI Games 2(1):54–67
    Peter C, Urban B (2012) Emotion in human-computer interaction. In: Expanding the frontiers of visual analytics and visualization. Springer, pp 239–262
    Ramakrishnan S, El Emary IM (2013) Speech emotion recognition approaches in human computer interaction. Telecommun Syst 52(3):1467–1478
    Reijneveld K, de Looze M, Krause F, Desmet P (2003) Measuring the emotions elicited by office chairs. In: Proceedings of the 2003 International Conference on Designing Pleasurable Products and Interfaces, ser. DPPI ’03. ACM, New York. p 6–10
    Rosales GCM, Borges de Araujo R, Otsuka JL, da Rocha RV (2011) Using logical sensors network to the accurate monitoring of the learning process in distance education courses. In: Advanced Learning Technologies (ICALT), 2011 11th IEEE International Conference on. IEEE, pp 573–575
    Russell J (1980) A circumplex model of affect. J Personal Soc Psychol 39(6):1161–1178
    Russell JA, Weiss A, Mendelsohn GA (1989) Affect grid: a single-item scale of pleasure and arousal. J Personal Soc Psychol 57(3):493
    Santos V, Coca S, Libralon G, Romero R (2013) Imitation of facial expressions for learning emotions in social robotics. In: Proceedings of the 2013 Simposio Brasileiro de Automacao Inteligente
    Saragih JM, Lucey S, Cohn JF (2011) Deformable model fitting by regularized landmark mean-shift. Int J Comput Vis 91(2):200–215
    Scherer K (2005) What are emotions? And how can they be measured? Soc Sci Inf 44:695–729
    Van Someren MW, Barnard YF, Sandberg JA et al (1994) The think aloud method: a practical guide to modelling cognitive processes, vol 2. Academic Press, London
    Wang R, Chen F, Chen Z, Li T, Harari G, Tignor S, Zhou X, Ben-Zeev D, Campbell AT (2014) Studentlife: assessing mental health, academic performance and behavioral trends of college students using smartphones. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM. pp 3–14
    Wrzus C, Mehl MR (2015) Lab and/or field? Measuring personality processes and their social consequences. Eur J Personal 29(2):250–271
    Xavier RAC, de Almeida Neris VP (2014) Measuring the emotional experience of users through a hybrid semantic approach. In: Proceedings of the 13th Brazilian Symposium on Human Factors in Computing Systems. Sociedade Brasileira de Computação, pp 226–235
    Xavier RAC, Garcia FE, de Almeida Neris VP (2012) Decisoes de design de interfaces ruins e o impacto delas na interacao: Um estudo preliminar considerando o estado emocional de idosos. In: Proceedings of the 11th Brazilian Symposium on Human Factors in Computing Systems, IHC ’12. Brazilian Computer Society, Porto Alegre, Brazil. pp 127–136
    Zhou F, Qu X, Helander MG, Jiao JR (2011) Affect prediction from physiological measures via visual stimuli. Int J Hum Comput Stud 69(12):801–819

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