The Use of Affective Computing in the Conceptual Design Stage of New Products

  • Agustín Job Hernández-MontesEmail author
  • Guillermo Cortés-Robles
  • Giner Alor-Hernández
  • Jorge Luis García-Alcaraz
Part of the Management and Industrial Engineering book series (MINEN)


The innovation process, seen as a set of problem-solving activities needs novel approaches to assist decision-making in the evaluation of different potential solutions. Typical perspective as the Analytic Hierarchy Process, similarity-based techniques, and essential performance indicators find their limits when it is necessary to take into account a crucial factor: emotions. The emotional response to unique product attributes is in fact, a determinant element to succeed in a market. The affective computing paradigm is a recent technology that allows knowing the person’s mood by using different strategies: the use of the camera to recognize an image, the tone voice of a person, and the use of different sensors and wearable devices. Some common features are the use of sensors that measure heart rate, the excitement of an individual, and the detection of the bioelectric activity of the brain at the instant of someone sees something specific. The affective computing has become more relevant in the past years. Emotions are fundamental to human experience, influencing cognition, perception, and everyday tasks such as learning, communication, and even rational decision-making. Industry asks for a different effective mechanism to select the best alternative during design and evaluation of products or services, particularly in the conceptual design stage. Decisions at this stage will determine the primary attributes of a product. The affective computing paradigm can show what characteristics of the product are more attractive to the design stakeholder and allow a review before launching a product.


Conceptual design Affective computing Industrial design Decision-making process 


  1. Bond, R. R. (2017). SenseCare: Using affective computing to manage and care for the emotional wellbeing of older people. eHealth 360, 352–356.Google Scholar
  2. Boucsein, W. et al. (2008). Objective emotional assessment of industrial products. Springer Netherlands.Google Scholar
  3. Chorianopoulou, A. K. (2016). Speech emotion recognition using affective saliency. Interspeech, 500–504.Google Scholar
  4. Christophe Vaudable, L. D. (2009). Study of consumer’s emotions product interviews. In 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops. ACII. Amsterdam, Netherlands.Google Scholar
  5. Chung, L. et al. (2017). The unique chinese innovation pathways: Lessons from chinese small and medium sized manufacturing firms. International Journal of Production Economics 190, 80–87.Google Scholar
  6. Damien Dupré, A. T. (2015). Emotions triggered by innovative products: A multi-componential approach of emotions for user experience tools. In International Conference on Affective Computing and Intelligent Interaction (ACII). Xi’an, China.Google Scholar
  7. Edgett, S. J. (n.d.). Stage-Gate. Retrieved 01 20, 2017, from dea‐to‐Launch (Stage‐Gate®) Model: An Overview.Google Scholar
  8. Efthymios Constantinides, S. J. (2008). Web 2.0: Conceptual foundatiosn and marketing issues. Journal of direct CONSTANTINIDES, Efthymios; Fountain, Stefan J. Web 2.0 Journal of Direct, Data and Digital Marketing Practice, 9(3), 231–244.Google Scholar
  9. Hoonhout, J. (2008). Inquiring about people’S affective product judgements. Probing Experience. Springer.Google Scholar
  10. Hyung-il Ahn, R. W. (2014, April-June). Measuring Affective-Cognitive Experience. IEEE Transactions on Affective Computing, 5(2), 173–186Google Scholar
  11. Leon, N. (2009). The future of computer-aided innovation. Computers in Industry, 60, 539–550Google Scholar
  12. Montes, A. J. (2016). Sistema de detección de emociones para la recomendación de recursos educativos. Tendencias de la Ingeniería de Software.Google Scholar
  13. O’Reilly, T. (2005). What is web 2.0: Design patterns and business models for the next generation of software. Retrieved July 20, 2017, from
  14. O’Relly, T. (2005, September 30). What is web 2.0. Retrieved July 20, 2017, from
  15. Patwardhan, A. S. (2013). Multimodal Affect Analysis for Product Feedback Assessment. In IIE Annual Conference. Proceedings. Institute of Industrial Engineers-Publisher..Google Scholar
  16. Picard, R. (n.d.). Affective Computing. Retrieved October 10, 2017, from
  17. Picard, R. W. (1995). Affective Computing. Affective Computing-MIT Media Laboratory Perceptual Computing Section Technical Report No. 321, 2139.Google Scholar
  18. Réne Lopez Flores, J. P. (2015). Open computer aided innovation to promote innovation in process engineering. Chemical Engineering Research and Design, 103, 90–107.Google Scholar
  19. Rene Lopez Flores, J. P.-M. (2015). Usin the Collective Intelligence for Inventive problem solving: A contribution for Open Compuer Aides Innovation. Expert Systems with Applications, 42, 9340–9352.Google Scholar
  20. Sallis, J. F. (2000). Assessment of physical activity by self-report: status, limitations, and future directions. Research quarterly for exercise and sport, 71(suppl. 2), 1–14.Google Scholar
  21. Samer Schaat, S. W. (2015). Modelling Emotion and Social Norms for Consumer Simulations Exemplified in Social Media. In 2015 International Conference on Affective Computing and Intelligent Interaction (ACII), Xi’an, China.Google Scholar
  22. Schmidt, D. S. (1996). Pattern-Oriented Software Architecture. Wiley.Google Scholar
  23. Stefan Hüsing, S. K. (2009). Computer aided innovation—state of the art from a new product development perspective. Computers in Industry, 60, 551–562.Google Scholar
  24. Tian, X. W. (2008). A Study on the Method of Satisfaction Measurement Based on Emotion Space. In 9th International Conference on Computer-Aided Industrial Design and Conceptual Design, CAID/CD, IEEE. Kunming, China.Google Scholar
  25. Vera-Munoz, C. P.-S. (2008). A wearable EMG monitoring system for emotions assessment. Probing Experience, 139–148.Google Scholar
  26. Westerink, J. O. (2009, September). Emotion measurement platform for daily life situations. In 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops. ACII, (pp. 1–6). Amsterdam, Netherlands.Google Scholar
  27. Zaripova, V. (2014). Computer Aided Innovation model for university-enterprise cooperation. ICTIC—Proceedings in Conference of Informatics and Management Sciences, Vol. 3, pp 377–382.Google Scholar

Copyright information

© Springer International Publishing AG 2019

Authors and Affiliations

  • Agustín Job Hernández-Montes
    • 1
    Email author
  • Guillermo Cortés-Robles
    • 1
  • Giner Alor-Hernández
    • 1
  • Jorge Luis García-Alcaraz
    • 2
  1. 1.Tecnologico Nacional de México-Instituto Tecnológico de OrizabaOrizabaMexico
  2. 2.Department of Industrial Engineering and ManufacturingUniversidad Autónoma de Ciudad JuárezChihuahuaMexico

Personalised recommendations