REVIEW PAPER
An intelligent support system and emotional state tests for people who are sick or recovering
 
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1
WSEI University
 
2
Netrix S.A.
 
 
Submission date: 2024-05-28
 
 
Acceptance date: 2024-07-12
 
 
Publication date: 2024-08-20
 
 
Corresponding author
Michał Maj   

WSEI University
 
 
JoMS 2024;57(Numer specjalny 3):371-387
 
KEYWORDS
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ABSTRACT
The project aims to develop an intelligent system that has the potential to significantly improve patient care by examining and supporting the emotional state of people with chronic diseases or undergoing rehabilitation. The system will use advanced technologies such as image analysis, natural language processing and understanding (NLP/NLU), and a personal assistant module. Acting as a virtual companion, this module will support the user in everyday tasks such as setting reminders and managing schedules and provide emotional support through interactive conversations. The project also involves the development of an analytical module that automatically generates analyses and reports based on the collected data. A patient-oriented system will be created during the work to collect essential data and support him in rehabilitation. On the other hand, the system will cooperate with a doctor who can make a preliminary diagnosis and develop further treatment based on the patient's data. As feedback, the patient will receive health reports along with their interpretation and treatment recommendations created by the doctor.
REFERENCES (20)
1.
Alrowais, F., Negm, N., Khalid, M., Almalki, N., Marzouk, R., Mohamed, A., Al Duhayyim, M., & Alneil, A. A. (2023). Modified Earthworm Optimization With Deep Learning Assisted Emotion Recognition for Human-Computer Interface. IEEE Access, 11. https://doi.org/10.1109/ACCESS....
 
2.
Alsemawi, M. R. M., Mutar, M. H., Ahmed, E. H., Hanoosh, H. O., & Abbas, A. H. (2023). Emotion recognition from human facial images based on a fast learning network. Indonesian Journal of Electrical Engineering and Computer Science, 30(3). https://doi.org/10.11591/ijeec....
 
3.
Azamy, M., Ariwibowo, A. B., & Mardianto, I. (2023). Face Recognition Implementation with MTCNN on Attendance System Prototype at Trisakti University. Indonesian Journal of Banking and Financial Technology (FINTECH), 1(1).
 
4.
Bharti, S. K., Varadhaganapathy, S., Gupta, R. K., Shukla, P. K., Bouye, M., Hingaa, S. K., & Mahmoud, A. (2022). Text-Based Emotion Recognition Using Deep Learning Approach. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/2....
 
5.
Choi, J. H., & Lee, J. S. (2019). EmbraceNet: A robust deep learning architecture for multimodal classification. Information Fusion, 51. https://doi.org/10.1016/j.inff....
 
6.
Khattak, A., Asghar, M. Z., Ali, M., & Batool, U. (2022). An efficient deep learning technique for facial emotion recognition. Multimedia Tools and Applications, 81(2). https://doi.org/10.1007/s11042....
 
7.
Ko, B. C. (2018). A brief review of facial emotion recognition based on visual information. Sensors (Switzerland), 18(2). https://doi.org/10.3390/s18020....
 
8.
Ma, F., Li, Y., Ni, S., Huang, S., & Zhang, L. (2022). Data Augmentation for Audio–Visual Emotion Recognition with an Efficient Multimodal Conditional GAN. Applied Sciences (Switzerland), 12(1). https://doi.org/10.3390/app120....
 
9.
Ma, H., & Wang, J. (2021). Application of Artificial Intelligence in Intelligent Decision-Making of Human Resource Allocation. Advances in Intelligent Systems and Computing, 1282. https://doi.org/10.1007/978-3-....
 
10.
Maciura, Ł., Cieplak, T., Pliszczuk, D., Maj, M., & Rymarczyk, T. (2023). Autonomous Face Classification Online Self-Training System Using Pretrained ResNet50 and Multinomial Naïve Bayes. Sensors, 23(12), 5554. https://doi.org/10.3390/s23125....
 
11.
Maj, M., Rymarczyk, T., Cieplak, T., & Pliszczuk, D. (2022, October). Deep learning model optimization for faster inference using multi-task learning for embedded systems. Proceedings of the 28th Annual International Conference on Mobile Computing And Networking. https://doi.org/10.1145/349524....
 
12.
Misra, I., Shrivastava, A., Gupta, A., & Hebert, M. (2016). Cross-Stitch Networks for Multi-task Learning. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 3994–4003. https://doi.org/10.1109/CVPR.2....
 
13.
Mumu, J., Tanujaya, B., Charitas, R., & Prahmana, I. (2022). Likert Scale in Social Sciences Research: Problems and Difficulties. FWU Journal of Social Sciences, 16(4). https://doi.org/10.51709/19951....
 
14.
Ohri, K., Nihal Reddy, A., Soumya Pappula, S., Bhargava Datta Varma, P., Likhith Kumar, S., & Goud Yeada, S. (2023). AI Personal Trainer Using Open CV and Media Pipe. International Research Journal of Engineering and Technology.
 
15.
Pena, D., Aguilera, A., Dongo, I., Heredia, J., & Cardinale, Y. (2023). A Framework to Evaluate Fusion Methods for Multimodal Emotion Recognition. IEEE Access, 11. https://doi.org/10.1109/ACCESS....
 
16.
Santoni, M. M., Basaruddin, T., & Junus, K. (2023). Convolutional Neural Network Model based Students’ Engagement Detection in Imbalanced DAiSEE Dataset. International Journal of Advanced Computer Science and Applications, 14(3). https://doi.org/10.14569/IJACS....
 
17.
Sepúlveda, A., Castillo, F., Palma, C., & Rodriguez-Fernandez, M. (2021). Emotion recognition from ECG signals using wavelet scattering and machine learning. Applied Sciences (Switzerland), 11(11). https://doi.org/10.3390/app111....
 
18.
Steppan, M., Zimmermann, R., Fürer, L., Southward, M., Koenig, J., Kaess, M., Kleinbub, J. R., Roth, V., & Schmeck, K. (2023). Machine Learning Facial Emotion Classifiers in Psychotherapy Research: A Proof-of-Concept Study. Psychopathology. https://doi.org/10.1159/000534....
 
19.
Wu, Y., & Ji, Q. (2019). Facial Landmark Detection: A Literature Survey. International Journal of Computer Vision, 127(2). https://doi.org/10.1007/s11263....
 
20.
Xu, X., Du, M., Guo, H., Chang, J., & Zhao, X. (2021). Lightweight FaceNet Based on MobileNet. International Journal of Intelligence Science, 11(01). https://doi.org/10.4236/ijis.2....
 
eISSN:2391-789X
ISSN:1734-2031
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