Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Call For Paper : Vol 11, Issue 03, March 2024

Call for Paper

Vol 11, Issue 03, March 2024

Indexing

A Review on Various Mood Detection and Regulation Methods

Author : Himadri Patil 1 Makarand Kulkarni 2

PDF

Date of Publication :25th September 2020

Abstract: Advancements in research and technology have made the human capacity to interact with computers or machines. The most natural way of communication is through emotions. In this era of Artificial Intelligence, Affective computing and virtual reality, to sense and regulate the person’s emotional states without another person’s intervention is possible. Enormous research has taken place in the field of mood detection and regulation. This paper focuses on the major highlights in the recent research of mood detection and regulation with different approaches for providing a technological perspective on society.

Reference :

    1. Hume, D., “Emotions & Moods”, Robbins, S.P., Judge, T.A. (Eds.), Organizational Behavior. Pearson, London, UK, pp. 258–297, 2012.
    2. Carroll E. Izard, “Human Emotions - Emotions, Personality, & Psychotherapy”, Springer Science & Business Media, November, 2013.
    3. R. Smith, A. Alkozei, and W. D. S. Killgore, “How Do Emotions Work?” Front. Young Minds, vol. 5, December, 2017.
    4. R. Brockman Joseph Ciarrochi, Philip Parker & Todd Kashdan, “Emotion regulation strategies in daily life: mindfulness, cognitive reappraisal and emotion suppression”, August, 2016.
    5. J.H.S. Eun-Hye Jang, Byoung-Jun Park, Sang-Hyeob Kim, “Emotion Recognition by Machine Learning Algorithms using Psychophysiological Signals,” Int. J. Eng. Ind., vol. 3, no. 1, pp. 55–66, 2012.
    6. J. Kumari, R. Rajesh, & K. M. Pooja, “Facial Expression Recognition: A Survey,” Second Int. Symp. Comput. Vis. Internet, vol. 58, pp. 486–491, 2015.
    7. M. A. M. & C. C. Leila Kerkeni, Youssef Serrestou, Mohamed Mbarki, Kosai Raoof, “Automatic Speech Emotion Recognition Using Machine Learning,”, Intech, 2019.
    8. D. S. C. Ashish B. Ingale, “Speech Emotion Recognition,” Int. J. soft Comput. Eng., vol. 2, 2012.
    9. P. A. Jennings, J. L. Frank, K. E. Snowberg, M. A. Coccia, and M. T. Greenberg, “Improving Classroom Leaming Environments by Cultivating Awareness and Resilience in Education (CARE): Results of a Randomized Controlled Trial” Sch. Psychol. Q., vol. 28, no. 4, pp. 374–391, 2013.
    10. M. Braun, J. Schubert, B. Pfleging, and F. Alt, “Improving Driver Emotions with Affective Strategies,” Multimodal Technol. Interact., vol. 3, no. 1, p. 21, 2019.
    11. L. S. Sakka and P. N. Juslin, “Emotion regulation with music in depressed and non-depressed individuals: Goals, strategies, and mechanisms,” vol. 1, pp. 1–12, 2018.
    12. T. Thanapattheerakul, J. Amoranto, K. Mao, & J. H. Chan, “Emotion in a century: A review of emotion recognition,” ACM Int. Conf. Proceeding Ser., 2018.
    13. K. L. Phan, T. Wager, S. F. Taylor, and I. Liberzon, “Functional neuroanatomy of emotion: A meta-analysis of emotion activation studies in PET and fMRI,” Neuroimage, vol. 16, no. 2, pp. 331–348, 2002.
    14. K. S. Kassam, A. R. Markey, V. L. Cherkassky, G. Loewenstein, and M. A. Just, “Identifying Emotions on the Basis of Neural Activation,” PLoS One, vol. 8, no. 6, 2013.
    15. U. Habel, M. Klein, T. Kellermann, N. J. Shah, and F. Schneider, “Same or different? Neural correlates of happy and sad mood in healthy males,” Neuroimage, vol. 26, no. 1, pp. 206–214, 2005.
    16. I. B. M. Michael D. Robinson, “Measures of emotion: A review,” vol. 23, no. 2, pp. 1–23, 2009.
    17. M. Bierzynska et al., “Effect of frustration on brain activation pattern in subjects with different temperament,” Front. Psychol., vol. 6, pp. 1–10, 2016.
    18. K. S. Kassam, A. R. Markey, V. L. Cherkassky, G. Loewenstein, and M. A. Just, “Identifying Emotions on the Basis of Neural Activation,” PLoS One, vol. 8, no. 6, 2013.
    19. S. Park et al., “Behavioral and neuroimaging evidence for facial emotion recognition in elderly Korean adults with mild cognitive impairment, Alzheimer’s disease, and front temporal dementia,” Frontiers in Aging Neuroscience, vol. 9, pp. 1–17, 2017.
    20. A. Frick et al., “Increased neurokinin-1 receptor availability in the amygdala in social anxiety disorder: A positron emission tomography study with [11C] GR205171,” Transl. Psychiatry, vol. 5, pp. 1–6, 2015.
    21. L. L. Zeng et al., “Identifying major depression using whole-brain functional connectivity: A multivariate pattern analysis,” Brain, vol. 135, no. 5, pp. 1498–1507, 2012.
    22. A. R. Damasio et al., “Subcortical and cortical brain activity during the feeling of self-generated emotions,” vol. 09, pp. 1049–1056, 2000.
    23. S. D. W. G.N. Peerzade, R.R. Deshmukh, “A Review: Speech Emotion Recognition,” Int. J. Comput. Sci. Eng., vol. 6, no. 3, 2018.
    24. S. Emerich, E. Lupu, A. Apatean, “Emotions Recognitions by Speech and Facial Expressions Analysis”, 17th European Signal Processing Conference, 2009.
    25. S. Motamed, S. Setayeshi, and A. Rabiee, “Speech emotion recognition based on a modified brain emotional learning model,” Biol. Inspired Cogn. Archit., vol. 19, pp. 32–38, 2017.
    26. M. B. Akçay and K. OÄŸuz, “Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers,” Speech Commun., vol. 116, pp. 56–76, 2020.
    27. S. Mirsamadi, E. Barsoum, and C. Zhang, “Automatic Speech Emotion Recognition Using Recurrent Neural Networks with Local Attention Center for Robust Speech Systems, The University of Texas at Dallas, Richardson, TX 75080, USA Microsoft Research, One Microsoft Way, Redmond, WA 98052, USA,” IEEE Int. Conf. Acoust. Speech, Signal Process., pp. 2227–2231, 2017.
    28. W. Dai, D. Han, Y. Dai, and D. Xu, “Emotion recognition and affective computing on vocal social media,” Inf. Manag., vol. 52, no. 7, pp. 777–788, 2015.
    29. J. P. Arias, C. Busso, and N. B. Yoma, “Shape-based modeling of the fundamental frequency contour for emotion detection in speech,” Comput. Speech Lang., vol. 28, no. 1, pp. 278–294, 2014.
    30. K. Y. Huang, C. H. Wu, M. H. Su, and H. C. Fu, “Mood detection from daily conversational speech using denoising autoencoder and LSTM,” ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc., pp. 5125– 5129, 2017.
    31. S. Mirsamadi, E. Barsoum, and C. Zhang, “Automatic Speech Emotion Recognition Using Recurrent Neural Networks with Local Attention Center for Robust Speech Systems, The University of Texas at Dallas, Richardson, TX 75080, USA Microsoft Research, One Microsoft Way, Redmond, WA 98052, USA,” IEEE Int. Conf. Acoust. Speech, Signal Process., pp. 2227–2231, 2017.
    32. Y. Pan, P. Shen, and L. Shen, “Speech emotion recognition using support vector machine,” Int. J. Smart Home, vol. 6, no. 2, pp. 101–108, 2012.
    33. P. Li, Y. Song, I. McLoughlin, W. Guo, and L. Dai, “An attention pooling based representation learning method for speech emotion recognition,” Proc. Annu. Conf. Int. Speech Commun. Assoc. INTERSPEECH, vol. 2018- September, pp. 3087–3091, 2018.
    34. D. Sahni and G. Aggarwal, “Recognizing Emotions and Sentiments in Text: A Survey,” vol. 5, no. 5, pp. 201– 205, 2015.
    35. C. R. Chopade, “Text Based Emotion Recognition: A Survey,” Int. J. Sci. Res., vol. 4, no. 6, pp. 2319–7064, 2013.
    36. E. C. C. Kao, C. C. Liu, T. H. Yang, C. T. Hsieh, and V. W. Soo, “Towards text-based emotion detection: A survey and possible improvements,” Proc. - 2009 Int. Conf. Inf. Manag. Eng. ICIME 2009, pp. 70–74, 2009.
    37. H. Soyel and H. Demirel, “Facial expression recognition using 3D facial feature distances,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 4633 LNCS, pp. 831–838, 2007.
    38. N. Kanger and G. Bathla, “Recognizing Emotion in Text using Neural Network and Fuzzy Logic,” Indian J. Sci. Technol., vol. 10, no. 12, pp. 1–6, 2017.
    39. A. F. M. N. H. Nahin, J. M. Alam, H. Mahmud, and K. Hasan, “Identifying emotion by keystroke dynamics and text pattern analysis,” Behav. Inf. Technol., vol. 33, no. 9, pp. 987–996, 2014
    40. S. Grover and A. Verma, “Design for emotion detection of punjabi text using hybrid approach,” Proc. Int. Conf. Inven. Comput. Technol. ICICT 2016, vol. 2, 2016.
    41. Elgayar, Salma, Abdelaziz A. Abdelhamid, and Zaki T. Fayed. "Unsupervised Emotion Detection from Text Using Word Embedding” The Eighteenth Conference on Language Engineering, ESOLEC, 2018.
    42. Seal Dibyendu & Roy Uttam & Basak, Rohini “Sentence-Level Emotion Detection from Text Based on Semantic Rules”, Information and Communication Technology for Sustainable Development, Proceedings of ICT4SD 2018, pp. 423-430, 2019.
    43. S. Shaheen, W. El-Hajj, H. Hajj, and S. Elbassuoni, “Emotion recognition from text based on automatically generated rules,” IEEE Int. Conf. Data Min. Work. ICDMW, pp. 383–392, 2015.
    44. L. Pivovarova, L. Escoter, A. Klami, and R. Yangarber, “HCS at SemEval-2017 Task 5: Polarity detection in business news using convolutional neural networks,” pp. 842–846, 2018.
    45. Nazia Perveen, Nazir Ahmad, M. Abdul Qadoos Bilal Khan, Rizwan Khalid, Salman Qadri, “Facial Expression Recognition Through Machine Learning”, International Journal of Scientific and Technology Research Volume 5, Issue 03, 2016.
    46. D. Das, “Human’s Facial Parts Extraction to Recognize Facial Expression,” Int. J. Inf. Theory, vol. 3, no. 3, pp. 65–72, 2014.
    47. T. S. Hai, L. H. Thai, and N. T. Thuy, “Facial Expression Classification Using Artificial Neural Network and K-Nearest Neighbor,” Int. J. Inf. Technol. Comput. Sci., vol. 7, no. 3, pp. 27–32, 2015.
    48. Y. L. Wu, H. Y. Tsai, Y. C. Huang, and B. H. Chen, “Accurate Emotion Recognition for Driving Risk Prevention in Driver Monitoring System,” 2018 IEEE 7th Glob. Conf. Consum. Electron. GCCE, pp. 796–797, 2018.
    49. D. Orozco, C. Lee, Y. Arabadzhi, and D. Gupta, “Transfer learning for Facial Expression Recognition.”
    50. P. Burkert, F. Trier, M. Z. Afzal, A. Dengel, and M. Liwicki, “DeXpression: Deep Convolutional Neural Network for Expression Recognition,” pp. 1–8, 2015.
    51. D. Dagar, A. Hudait, H. K. Tripathy and M. N. Das, "Automatic emotion detection model from facial expression," 2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), Ramanathapuram, pp. 77- 85, 2016
    52. A. K. Hassan and S. N. Mohammed, “A novel facial emotion recognition scheme based on graph mining,” Def. Technol., 2020.
    53. A. Alreshidi and M. Ullah, “Facial emotion recognition using hybrid features,” Informatics, vol. 7, no. 1, pp. 1– 13, 2020.
    54. B. Islam, F. Mahmud, and A. Hossain, “Facial Region Segmentation Based Emotion Recognition Using Extreme Learning Machine,” in 2018 International Conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2018, 2019.
    55. L. Santamaria-Granados, M. Munoz-Organero, G. Ramirez-Gonzalez, E. Abdulhay, and N. Arunkumar, “Using Deep Convolutional Neural Network for Emotion Detection on a Physiological Signals Dataset (AMIGOS),” IEEE Access, vol. 7, no. c, pp. 57–67, 2019.
    56. X. Zhang, C. Xu, W. Xue, J. Hu, Y. He, and M. Gao, “Emotion recognition based on multichannel physiological signals with comprehensive nonlinear processing,” Sensors (Switzerland), vol. 18, no. 11, pp. 1–16, 2018.
    57. S. Oh, J. Y. Lee, and D. K. Kim, “The design of CNN architectures for optimal six basic emotion classification using multiple physiological signals,” Sensors (Switzerland), vol. 20, no. 3, pp. 1–17, 2020.
    58. J. Zhang, Y. Zhang, S. Zhan, and C. Cheng, “Ensemble emotion recognizing with multiple modal physiological signals,” no. 1, 2020. [59] Z. Shen, J. Cheng, X. Hu, and Q. Dong, “Emotion Recognition Based on Multi-View Body Gestures,” Proc. - Int. Conf. Image Process. ICIP, pp. 3317–3321, 2019.
    59. T. SapiÅ„ski, D. KamiÅ„ska, A. Pelikant, and G. Anbarjafari, “Emotion recognition from skeletal movements,” Entropy, vol. 21, no. 7, pp. 1–16, 2019.
    60. R. Santhoshkumar and M. Kalaiselvi Geetha, “Deep learning approach: Emotion Recognition from Human Body Movements,” Procedia Comput. Sci., vol. 152, no. 3, pp. 158–165, 2019.
    61. P. Soleimaninejadian, M. Zhang, Y. Liu, and S. Ma, “Mood Detection and Prediction Based on User Daily Activities,” First Asian Conf. Affect. Comput. Intell. Interact. ACII Asia, pp. 1–6, 2018.
    62. D. Colombo, J. Fernández-álvarez, A. G. Palacios, P. Cipresso, C. Botella, and G. Riva, “New technologies for the understanding, assessment, and intervention of emotion regulation,” Front. Psychol., vol.10, 2019.
    63. L. S. Sakka and P. N. Juslin, “Emotion regulation with music in depressed and non-depressed individuals: Goals, strategies, and mechanisms,” vol. 1, pp. 1–12, 2018.
    64. S. C. Dulawa & D. S. Janowsky, “Cholinergic regulation of mood: from basic & clinical studies to emerging therapeutics,” Mol. Psychiatry, 2018.
    65. R. Brockman et al., “Emotion regulation strategies in daily life: mindfulness, cognitive reappraisal and emotion suppression,” no. August, 2016.
    66. S. G. Hofmann, “Interpersonal Emotion Regulation Model of Mood & Anxiety Disorders,” 2014.
    67. I. A. Chicchi Giglioli, F. Pallavicini, E. Pedroli, S. Serino, and G. Riva, “Augmented Reality: A BrandNew Challenge for the Assessment and Treatment of Psychological Disorders,” Comput. Math. Methods Med., vol. 2015.
    68. Hartanto, Dody. “Computer-Based Social Anxiety Regulation in Virtual Reality Exposure Therapy.” 2019.
    69. A. Gaume, A. Vialatte, A. Mora-Sánchez, C. Ramdani, and F. B. Vialatte, “A psychoengineering paradigm for the neurocognitive mechanisms of biofeedback and neurofeedback,” Neurosci. Biobehav. Rev., vol. 68, pp. 891–910, 2016.
    70. A. Dillon, M. Kelly, I. H. Robertson, and D. A. Robertson, “Smartphone applications utilizing biofeedback can aid stress reduction,” Front. Psychol., vol. 7, pp. 1–7, 2016.
    71.  V. C. Goessl, J. E. Curtiss, and S. G. Hofmann, “The effect of heart rate variability biofeedback training on stress and anxiety: A meta-analysis,” Psychol. Med., vol. 47, no. 15, pp. 2578–2586, 2017.
    72. C. Zich et al., “Modulatory effects of dynamic fMRIbased neurofeedback on emotion regulation networks in adolescent females,” bioRxiv, vol. 44, no. Preprint, p. Under review, 2019.
    73. R. M. Al-Eidan, H. Al-Khalifa, and A. M. Al-Salman, “A review of wrist-worn wearable: Sensors, models, and challenges,” J. Sensors, vol. 2018, 2018.
    74. N. R. Leonard et al., “Theoretically-based emotion regulation strategies using a mobile app and wearable sensor among homeless adolescent mothers: Acceptability and feasibility study,” J. Med. Internet Res., vol. 20, no. 3, 2018.
    75.  F. Diano, F. Ferrara, and R. Calabretta, “The development of a mindfulness-based mobile application to learn emotional self-regulation,” CEUR Workshop Proc., vol. 2524, pp. 1–11, 2019.

Recent Article