A Body Emotion-Based Human-Robot Interaction

1. Reddy, K.K., Shah, M.: Recognizing 50 human action categories of web videos. Mach. Vis. Appl. 24(5), 971–981 (2013)

   Article  Google Scholar 

2. Alonso Martín, F., Ramey, A., Salichs, M.A.: Speaker identification using three signal voice domains during human-robot interaction. In: Proceedings of 2014 ACM/IEEE International Conference on Human-Robot Interaction, pp. 114–115. ACM (2014)

   Google Scholar 

3. Chaaraoui, A.A., Padilla-López, J.R., Climent-Pérez, P., Flórez-Revuelta, F.: Evolutionary joint selection to improve human action recognition with RGB-D devices. Expert Syst. Appl. 41(3), 786–794 (2014)

   Article  Google Scholar 

4. Venkataraman, V., Turaga, P., Lehrer, N., Baran, M., Rikakis, T., Wolf, S.L.: Attractor-shape for dynamical analysis of human movement: applications in stroke rehabilitation and action recognition. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 514–520. IEEE Press (2013)

   Google Scholar 

5. Siddiqi, M.H., Ali, R., Khan, A.M., Park, Y.-T., Lee, S.: Human facial expression recognition using stepwise linear discriminant analysis and hidden conditional random fields. IEEE T Image Process 24(4), 1386–1398 (2015)

   Article  MathSciNet  Google Scholar 

6. Yildiz, I.B., von Kriegstein, K., Kiebel, S.J.: From birdsong to human speech recognition: Bayesian inference on a hierarchy of nonlinear dynamical systems. PLoS Comput. Biol. 9(9), 1–16 (2013)

   Article  Google Scholar 

7. Chatterjee, M., Peng, S.-C.: Processing F0 with cochlear implants: modulation frequency discrimination and speech intonation recognition. Hear. Res. 235(1), 143–156 (2008)

   Article  Google Scholar 

8. Lichtenstern, M., Frassl, M., Perun, B., Angermann, M.: A prototyping environment for interaction between a human and a robotic multi-agent system. In: 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 185–186. IEEE Press (2012)

   Google Scholar 

9. Yamada, T., Murata, S., Arie, H., Ogata, T.: Dynamical integration of language and behavior in a recurrent neural network for human-robot interaction. Front. Neurorobot. 10(5), 1–17 (2016)

   Google Scholar 

10. Palm, R., Chadalavada, R., Lilienthal, A.: Fuzzy modeling and control for intention recognition in human-robot systems. In: 8th International Conference on Computational Intelligence (IJCCI), Porto, Portugal, pp. 67–74. SciTePress (2016)

    Google Scholar 

11. Liu, P., Glas, D.F., Kanda, T., Ishiguro, H.: Data-driven HRI: learning social behaviors by example from human-human interaction. IEEE Trans. Robot. 32(4), 988–1008 (2016)

    Article  Google Scholar 

12. Bohus, D., Horvitz, E.: Managing human-robot engagement with forecasts and… um… hesitations. In: Proceedings of 16th International Conference on Multimodal Interaction, pp. 2–9. ACM (2014)

    Google Scholar 

13. Aly, A., Tapus, A.: A model for synthesizing a combined verbal and nonverbal behavior based on personality traits in human-robot interaction. In: Proceedings of 8th ACM/IEEE International Conference on Human-Robot Interaction, pp. 325–332. IEEE Press (2013)

    Google Scholar 

14. Liu, Z., Wu, M., Li, D., Chen, L., Dong, F., Yamazaki, Y., Hirota, K.: Communication atmosphere in humans and robots interaction based on the concept of fuzzy atmosfield generated by emotional states of humans and robots. J. Automat. Mob. Robot. Intell. Syst. 7(2), 52–63 (2013)

    Google Scholar 

15. Dautenhahn, K.: Socially intelligent robots: dimensions of human–robot interaction. Philos. Trans. Roy. Soc. Lond. B 362(1480), 679–704 (2007)

    Article  Google Scholar 

16. Laban, R.: The Language of Movement: A Guidebook to Choreutics. Plays, Boston (1974)

    Google Scholar 

17. Hsieh, C., Wang, Y.: Digitalize emotions to improve the quality life-analyzing movement for emotion application. J. Aesthet. Educ. 168, 64–69 (2009)

    Google Scholar 

18. Ku, M.-S., Chen, Y.: From movement to emotion - a basic research of upper body (analysis foundation of body movement in the digital world 3 of 3). J. Aesthet. Educ. 164, 38–43 (2008)

    Google Scholar 

19. Kinect - Windows App Development. https://developer.microsoft.com/en-us/windows/kinect

20. Xia, G., Tay, J., Dannenberg, R., Veloso, M.: Autonomous robot dancing driven by beats and emotions of music. In: Proceedings of 11th International Conference on Autonomous Agents and Multiagent Systems, vol. 1, pp. 205–212. International Foundation for Autonomous Agents and Multiagent Systems (2012)

    Google Scholar 

21. Chen, C., Jafari, R., Kehtarnavaz, N.: UTD-MHAD: a multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 168–172. IEEE Press (2015)

    Google Scholar 

22. Nao Robot: Characteristics - Aldebaran. https://www.ald.softbankrobotics.com/en/cool-robots/nao/find-out-more-about-nao

Download references