This paper is in the following e-collection/theme issue:

Methods and New Tools in Mental Health Research (463) New Methods (963) mHealth for Data Collection and Research (981) Diagnostic Tools in Mental Health (248) Machine Learning (1501) Special Issue on Computing and Mental Health (2018) (33)

Published on in Vol 5, No 3 (2018): Jul-Sept

Recognition of Emotions Conveyed by Touch Through Force-Sensitive Screens: Observational Study of Humans and Machine Learning Techniques

Recognition of Emotions Conveyed by Touch Through Force-Sensitive Screens: Observational Study of Humans and Machine Learning Techniques

Recognition of Emotions Conveyed by Touch Through Force-Sensitive Screens: Observational Study of Humans and Machine Learning Techniques

Authors of this article:

Alicia Heraz1 Author Orcid Image ;   Manfred Clynes2 Author Orcid Image
Article Authors Cited by (16) Tweetations (26) Metrics

Journals

  1. Domínguez-Jiménez J, Campo-Landines K, Martínez-Santos J, Delahoz E, Contreras-Ortiz S. A machine learning model for emotion recognition from physiological signals. Biomedical Signal Processing and Control 2020;55:101646 View
  2. Jia W, Qi Y, Huang A, Zhou F, Gao S. High Security User Authentication Based on Piezoelectric Keystroke Dynamics Applying to Multiple Emotional Responses. IEEE Sensors Journal 2022;22(3):2814 View
  3. Liu Z, Fu Z. Piezoelectric Based Touch Sensing for Interactive Displays—A Short Review. Materials 2021;14(19):5698 View
  4. Guo Y, Liu X, Wang X, Zhu T, Zhan W. Automatic Decision-Making Style Recognition Method Using Kinect Technology. Frontiers in Psychology 2022;13 View
  5. Yang K, Wang C, Gu Y, Sarsenbayeva Z, Tag B, Dingler T, Wadley G, Goncalves J. Behavioral and Physiological Signals-Based Deep Multimodal Approach for Mobile Emotion Recognition. IEEE Transactions on Affective Computing 2023;14(2):1082 View
  6. Olugbade T, He L, Maiolino P, Heylen D, Bianchi-Berthouze N. Touch Technology in Affective Human–, Robot–, and Virtual–Human Interactions: A Survey. Proceedings of the IEEE 2023;111(10):1333 View
  7. Kargarandehkordi A, Kaisti M, Washington P. Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study. Applied Sciences 2024;14(4):1337 View
  8. Padman S, Magare D. Enhanced Emotion Detection Model Using Dusky Canidae Optimization-Based Deep CNN Classifier. International Journal of Pattern Recognition and Artificial Intelligence 2024;38(07) View
  9. Qi Y, Jia W, Feng L, Dai Y, Tang C, Zhou F, Gao S. Piezoelectric Touch Sensing and Random-Forest-Based Technique for Emotion Recognition. IEEE Transactions on Computational Social Systems 2024;11(5):6296 View
  10. Heraz A, Bhyravabhottla K, Sajith N. Predicting user engagement levels through emotion-based gesture analysis of initial impressions. Electronic Commerce Research 2024 View
  11. Heraz A, Sajith N, Siddiqi A, Ashraf I. An Advanced Forecasting Model Leveraging Emotion‐Gesture Correlation to Predict Returning Visitors Surpasses Visit Duration as a Predictive Factor. Applied Computational Intelligence and Soft Computing 2024;2024(1) View

Books/Policy Documents

  1. Kerdvibulvech C, Guan S. Computational Science and Its Applications – ICCSA 2019. View
  2. Gao S, Yan S, Zhao H, Nathan A. Touch-Based Human-Machine Interaction. View