Development of Emotion Stress Relief Recommender Tool Using Machine Learning

M Mohammed Mustafa

Abstract


In today's society, individuals face unprecedented levels of stress, with varying indicators and effects. Music has long been recognized for its calming effects on the brain and body, especially slow and calm classical music, which can lower heart rate, blood pressure, and stress hormone levels. However, the effectiveness of music in stress management may be reduced if the music does not match the listener's current emotional state. To overcome this problem, this research aims to develop an emotion-based stress reduction recommendation tool using machine learning. This research employs experimental methods. The research results show this tool will analyze the user's emotions and recommend music most likely to reduce stress. By tailoring music recommendations to an individual's emotional state, we hope to increase the overall effectiveness of music as a stress management tool. Implications of this research include the potential for creating intelligent music players that provide more personalized and effective stress reduction.


Keywords


API; Face Detection; Image Processing; Machine Learning; Stress

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References


K. Chankuptarat, R. Sriwatanaworachai, and S. Chotipant, “Emotion-Based Music Player,” in 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST), Luang Prabang, Laos, Jul. 02-05, 2019.

M. T. Quasim, E. H. Alkhammash, M. A. Khan, and M. Hadjouni, “Emotion-based music recommendation and classification using machine learning with IoT Framework,” Soft Comput., vol. 25, no. 18, pp. 12249–12260, 2021, doi : 10.1007/s00500-022-07786-2.

S. A. Kriakous, K. A. Elliott, C. Lamers, and R. Owen, “The effectiveness of mindfulness-based stress reduction on the psychological functioning of healthcare professionals: A systematic review,” Mindfulness (N. Y)., vol. 12, no, 1, pp. 1–28, 2021.

A. Kumar, K. Sharma, and A. Sharma, “Hierarchical deep neural network for mental stress state detection using IoT based biomarkers,” Pattern Recognit. Lett., vol. 145, no. 01, pp. 81–87, 2021.

A. B. Nassif, I. Shahin, S. Hamsa, N. Nemmour, and K. Hirose, “CASA-based speaker identification using cascaded GMM-CNN classifier in noisy and emotional talking conditions,” Appl. Soft Comput., vol. 103, no. 1, p. 1-24, 2021.

Y. Deldjoo, M. Schedl, P. Cremonesi, and G. Pasi, “Recommender systems leveraging multimedia content,” ACM Comput. Surv., vol. 53, no. 5, pp. 1–38, 2020, doi : 10.1145/3407190.

H. Yoo and K. Chung, “Deep learning-based evolutionary recommendation model for heterogeneous big data integration,” KSII Trans. Internet Inf. Syst., vol. 14, no. 9, pp. 3730–3744, 2020, doi : 10.3837/tiis.2020.09.009.

R. Sridhar, H. Wang, P. McAllister, and H. Zheng, “E-Bot: A Facial Recognition Based Human-Robot Emotion Detection System,” Proc. 32nd Int. BCS Hum. Comput. Interact. Conf., 2018, pp. 1–5, doi: 10.14236/ewic/hci2018.213.

S. K. Sharma et al., “A Diabetes Monitoring System and Health-Medical Service Composition Model in Cloud Environment,” IEEE Access, vol. 11, no. March, pp. 32804–32819, 2023, doi: 10.1109/ACCESS.2023.3258549.

H. Dandan and R. Parthasarathy, “A new algorithm for image edge detection based on phase stretch transform,” Proc. SPIE. 12799, Third Int. Conf. Adv. Algorithms Signal Image Process. (AASIP 2023), San Diego, USA, Aug. 20-24, 2023.

S. Poletti et al., “Mindfulness-based stress reduction in early palliative care for people with metastatic cancer: a mixed-method study,” Complement. Ther. Med., vol. 47, vol. 1, pp. 1-29, 2019.

J.-R. Gao, B. Yu, D. Ding, and D. Z. Pan, “Lithography hotspot detection and mitigation in nanometer VLSI,” in 2013 IEEE 10th International Conference on ASIC, 2013, pp. 1–4. doi: 10.1109/ASICON.2013.6811917.

A. Aljanaki, Y.-H. Yang, and M. Soleymani, “Emotion in music task at mediaeval 2014.,” in MediaEval Workshop, Barcelona, Spain, Oct. 16-17, 2014.

M. T. Quazi, “Human emotion recognition using smart sensors,” M.S. thesis, Dept. Master of Engineering in Electronics and Communication Engineering, School of Engineering and Advanced Technology, Massey University, Palmerston North, New Zealand, 2012. [Online]. Available : https://mro.massey.ac.nz/items/79d4cc8e-3cef-4c78-96c9-35b15b5c245d

W. E. J. Knight and N. S. Rickard, “Relaxing music prevents stress-induced increases in subjective anxiety, systolic blood pressure, and heart rate in healthy males and females,” J. Music Ther., vol. 38, no. 4, pp. 254–272, 2001, doi : 10.1093/jmt/38.4.254.

Y. Song, S. Dixon, and M. Pearce, "Evaluation of musical features for emotion classification," Proc. International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal, Oct. 8-12, 2012.

R. Yonck, Heart of the machine: Our future in a world of artificial emotional intelligence. Amazon : Arcade, 2020.

M. G. Salido Ortega, L.-F. Rodríguez, and J. O. Gutierrez-Garcia, “Towards emotion recognition from contextual information using machine learning,” J. Ambient Intell. Humaniz. Comput., vol. 11, no. 8, pp. 3187–3207, 2020, doi : 10.1007/s12652-019-01485-x.

T. Sharma, M. Diwakar, P. Singh, S. Lamba, P. Kumar, and K. Joshi, “Emotion analysis for predicting the emotion labels using machine learning approaches,” in 2021 IEEE 8th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Uttarakhand, India, Nov. 11-13, 2021.

N. Kholodna, V. Vysotska, and S. Albota, “A machine learning model for automatic emotion detection from speech,” in MoMLeT+ DS, Lviv-Shatsk, Ukrine, 2021.

K. Tarunika, R. B. Pradeeba, and P. Aruna, “Applying machine learning techniques for speech emotion recognition,” in 2018 9th international conference on computing, communication and networking technologies (ICCCNT), IEEE, Bengaluru, India, Jul. 10-12, 2018.

C. Sekhar, M. S. Rao, A. S. K. Nayani, and D. Bhattacharyya, “Emotion recognition through human conversation using machine learning techniques,” in Machine Intelligence and Soft Computing: Proceedings of ICMISC 2020, Springer, Visakhapatnam, India, Sept. 3-4, 2020.

J. Zhang, Z. Yin, P. Chen, and S. Nichele, “Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review,” Inf. Fusion, vol. 59, pp. 103–126, 2020, doi : 10.1016/j.inffus.2020.01.011.

A. F. A. Nasir, E. S. Nee, C. S. Choong, A. S. A. Ghani, A. P. P. A. Majeed, A. Adam, and M. Furhan, “Text-based emotion prediction system using machine learning approach,” in IOP Conference Series: Materials Science and Engineering, IOP Publishing, 2020, pp. 1-12, doi : 10.1088/1757-899X/769/1/012022.

E. Ivanova and G. Borzunov, “Optimization of machine learning algorithm of emotion recognition in terms of human facial expressions,” Procedia Comput. Sci., vol. 169, no. 1, pp. 244–248, 2020, doi : 10.1016/j.procs.2020.02.143.

W. R. Shadish, T. D. Cook, and D. T. Campbell, Experimental and Designs for Generalized Causal Inference. Boston: Houchton Mifflin Company, 2002.

S. Asteriadis, P. Tzouveli, K. Karpouzis, and S. Kollias, “Estimation of behavioral user state based on eye gaze and head pose—application in an e-learning environment,” Multimed. Tools Appl., vol. 41, pp. 469–493, 2009, doi : 10.1007/s11042-008-0240-1.

M. S. D. Perera et al., “Stress monitoring and relieving application for it professionals,” Int. Res. J. Innov. Eng. Technol., vol. 07, no. 10, pp. 609–626, 2023, doi: 10.47001/irjiet/2023.710081.

P. Kumar, S. Grag, and A. Grag, “Assessment of anxiety, depression and stress using machine learning models,” Procedia Comput. Sci., vol. 17, no. 1, pp. 1989–1998, 2020.

F. Al-Shargie, T. B. Tang, N. Badruddin, and M. Kiguchi, “Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach,” Med. Biol. Eng. Comput., vol. 56, no. 1, pp. 125–136, 2018, doi: 10.1007/s11517-017-1733-8.

R. Gupta, M. A. Alam, and P. Agarwal, “Modified support vector machine for detecting stress level using EEG signals,” Comput. Intell. Neurosci., vol. 2020, no. 1, pp. 1–14, 2020, doi: 10.1155/2020/8860841.

G. Jun and K. G. Smitha, “EEG based stress level identification,” in 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016, pp. 3270–3274. doi: 10.1109/SMC.2016.7844738.

F. Al-Saqqar, M. Al-Diabat, M. Aloun, and A. M. Al-Shatnawi, “Handwritten arabic text recognition using principal component analysis and support vector machines,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 12, pp. 195–200, 2019, doi: 10.14569/ijacsa.2019.0101227.

Q. Cheng et al., “A method for identifying geospatial data sharing websites by combining multi-source semantic information and machine learning,” Appl. Sci., vol. 11, no. 18, pp. 1-19, 2021, doi: 10.3390/app11188705.




DOI: http://dx.doi.org/10.24042/ijecs.v4i1.21593

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