Depression Detection Using Stacked Autoencoder from Facial Features and NLP
DOI:
https://doi.org/10.24113/ojssports.v7i1.115Abstract
Depression has become one of the most common mental illnesses in the past decade, affecting millions of patients and their families. However, the methods of diagnosing depression almost exclusively rely on questionnaire-based interviews and clinical judgments of symptom severity, which are highly dependent on doctors’ experience and makes it a labor-intensive work. This research work aims to develop an objective and convenient method to assist depression detection using facial features as well as textual features. Most of the people conceal their depression from everyone. So, an automated system is required that will pick out them who are dealing with depression. In this research, different research work focused for detecting depression are discussed and a hybrid approach is developed for detecting depression using facial as well as textual features. The main purpose of this research work is to design and propose a hybrid system of combining the effect of three effective models: Natural Language Processing, Stacked Deep Auto Encoder with Random forest (RF) classifier and fuzzy logic based on multi-feature depression detection system. According to literature several fingerprint as well as fingervein recognition system are designed that uses various techniques in order to reduce false detection rate and to enhance the performance of the system. A comparative study of different recognition technique along with their limitations is also summarized and optimum approach is proposed which may enhance the performance of the system. The result analysis shows that the developed technique significantly advantages over existing methods.
References
[2] Q. Cong, Z. Feng, F. Li, Y. Xiang, G. Rao and C. Tao, "X-A-BiLSTM: A Deep Learning Approach for Depression Detection in Imbalanced Data," IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, 2018, pp. 1624-1627.
[3] Shen, Tiancheng, “Cross Domain Depression Detection via Harvesting Social Media.” Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. 2018.
[4] Singh, Harman, Neeti Dhanak, Haroon Ansari, and Krishan Kumar. ”HDML: Habit Detection with Machine Learning.” In Proceedings of the 7th International Conference on Computer and Communication Technology, pp. 29-33. ACM, 2017.
[5] Sharma, Shikhar, et al., ”D-FES: Deep facial expression recognition system.” Information and Communication Technology (CICT), 2017 Conference on. IEEE, 2017.
[6] S. Shikhar, et al., ”LEXER: LEXicon Based Emotion AnalyzeR.” International Conference on Pattern Recognition and Machine Intelligence. Springer, Cham, 2017.
[7] Pampouchidou, Anastasia, “Depression Assessment by Fusing High and Low Level Features from Audio, Video, and Text.” Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge. ACM, 2016.
[8] T. Baltrusaitis et al., “OpenFace: An Open Source Facial Behavior ? Analysis Toolkit,” in IEEE-WACV, 2016, pp. 1–10.
[9] V. Vonikakis et al., “Group happiness assessment using geometric features and dataset balancing,” in 18th ACM-ICMI. ACM, 2016, pp. 479–486.
[10] M. Valstar et al., “AVEC 2014: 3D Dimensional Affect and Depression Recognition Challenge,” in 4th ACM-AVEC, 2014, pp. 3–10.
[11] Michel F. Valstar, Björn Schuller, Kirsty Smith, Florian Eyben, Timur Almaev, Jarek Krajewski, Roddy Cowie, Jeff Cohn and Maja Pantic, ‘AVEC 2014 – The 3D Dimensional Affect and Depression Severity Estimation Challenge’, proc. Int'l Conf. ACM Multimedia, 2014.
[12] Wang, Xinyu, “A depression detection model based on sentiment analysis in micro-blog social network.” Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Berlin, Heidelberg, 2013.
[13] Wang, Xinyu, Chunhong Zhang, and Li Sun. ”An improved model for depression detection in micro-blog social network.” 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW). IEEE, 2013.
[14] S. Alghowinem, “Eye movement analysis for depression detection,” in 20th IEEE-ICIP, 2013, pp. 4220–4224.
[15] Videbeck, Sheila, and Sheila Videbeck. Psychiatric-mental health nursing. Lippincott Williams & Wilkins, 2013.
[16] Low, Lu-Shih Alex, et al. ”Detection of clinical depression in adolescents speech during family interactions.” IEEE Transactions on Biomedical Engineering 58.3, 2011.
[17] Low, Lu-Shih Alex, “Content based clinical depression detection in adolescents.” Signal Processing Conference, 2009 17th European. IEEE, 2009.
[18] Cohn, Jeffrey F., “Detecting depression from facial actions and vocal prosody.” Affective Computing and Intelligent Interaction and Workshops, 2009. ACII 2009. 3rd International Conference on. IEEE, 2009.
[19] Gilson, Mark, and Arthur Freeman. Overcoming depression: A cognitive therapy approach therapist guide. Oxford University Press, 2009.
[20] Clarke, Greg, “Overcoming Depression on the Internet (ODIN)(2): a randomized trial of a self-help depression skills program with reminders.” Journal of medical Internet research 7.2, 2005.
[21] K. Messer, J. Matas, J. Kittler, K. Jonsson, XM2VTS: the extended M2VTS database. In: Second International Conference on Audio- and Video-Based Biometric Person Authentication, 2000, pp. 72–77.
[22] Persons, Jacqueline B. “Psychotherapy outcome studies do not accurately represent current models of psychotherapy: A proposed remedy.” American psychologist 46.2, 1991.
[23] Plutchik, Robert. The emotions. University Press of America, 1991.
[24] P. H. Waxer, “Therapist training in nonverbal communication. I:Nonverbal cues for depression,” Journal of Clinical Psychology,vol. 30, no. 2, p. 215, 1974.
[25] P. Ekman and W. V. Friesen, The Psychology of Depression:Contemporary Theory and Research. Oxford, England: John Wiley& Sons, 1974, ch. Nonverbal behavior and psychopathology, p. 318.
