Open Access
Issue
MATEC Web Conf.
Volume 277, 2019
2018 International Joint Conference on Metallurgical and Materials Engineering (JCMME 2018)
Article Number 02037
Number of page(s) 6
Section Data and Signal Processing
DOI https://doi.org/10.1051/matecconf/201927702037
Published online 02 April 2019
  1. Hassani, S., Bafadel, I., Bekhatro, A., Al Blooshi, E., Ahmed, S., & Alahmad, M. (2017, November). Physiological signal-based emotion recognition system. In Engineering Technologies and Applied Sciences (ICETAS), 2017 4th IEEE International Conference on (pp. 1-5). IEEE. [Google Scholar]
  2. Valderas, M. T., Bolea, J., Laguna, P., Vallverdú, M., & Bailón, R. (2015, August). Human emotion recognition using heart rate variability analysis with spectral bands based on respiration. In Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE (pp. 6134-6137). IEEE. [Google Scholar]
  3. Dobbins, C., Fairclough, S., Lisboa, P., & Navarro, F. F. G. (2018). A Lifelogging Platform Towards Detecting Negative Emotions in Everyday Life using Wearable Devices. [Google Scholar]
  4. Emotion API-Emotion Detector | Microsoft Azure. (2019). Azure.microsoft.com. Retrieved 2 February 2019, from https://azure.microsoft.com/en-us/services/cognitiveservices/ emotion. [Google Scholar]
  5. Appel, V. C., Belini, V. L., Jong, D. H., Magalhães, D. V., & Caurin, G. A. (2014, August). Classifying emotions in rehabilitation robotics based on facial skin temperature. In Biomedical Robotics and Biomechatronics (2014 5th IEEE RAS & EMBS International Conference on (pp. 276-280). IEEE. [Google Scholar]
  6. Valderas, M. T., Bolea, J., Laguna, P., Vallverdú, M., & Bailón, R. (2015, August). Human emotion recognition using heart rate variability analysis with spectral bands based on respiration. In Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE (pp. 6134-6137). IEEE. [Google Scholar]
  7. Stoll, A. M. (1964). Techniques and uses of skin temperature measurements. Annals of the New York Academy of Sciences, 121(1), 49-56. [Google Scholar]
  8. Kulandaidaasan, J., Elara, M. R., & García, E. M. (2012). Comparing Thermography, GSR and Hear Rate During Stimulated Therapeutic Pet Robot Interaction Among Elderly. In Proceedings of International Conference on Intelligent Unmanned Systems (Vol. 8). [Google Scholar]
  9. Salazar-López, E., Domínguez, E., Ramos, V. J., de la Fuente, J., Meins, A., Iborra, O.,... & Gómez-Milán, E. (2015). The mental and subjective skin: Emotion, empathy, feelings and thermography. Consciousness and cognition, 34, 149-162. [Google Scholar]
  10. Bauer, R. M. (1998). Physiologic measures of emotion. Journal of clinical neurophysiology, 15(5), 388-396.. [Google Scholar]
  11. López, R., Poy, R., Pastor, M. C., Segarra, P., & Moltó, J. (2009). Cardiac defense response as a predictor of fear learning. International Journal of Psychophysiology, 74(3), 229-235. [Google Scholar]
  12. Khan, M. M., Ward, R. D., & Ingleby, M. (2009). Classifying pretended and evoked facial expressions of positive and negative affective states using infrared measurement of skin temperature. ACM Transactions on Applied Perception (TAP), 6(1), 6. [Google Scholar]

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