Intelligent recommendation system for patient rehabilitation

Alla V. Zaboleeva-Zotova; Заболеева-Зотова Алла Викторовна; Yulia A. Orlova; Орлова Юлия Александровна; Aleksandr V. Zubkov; Зубков Александр Владимирович; Donsckaia R. Donsckaia; Донская Анастасия Романовна

doi:10.14357/20718594240103

Intelligent recommendation system for patient rehabilitation

Authors: Zaboleeva-Zotova A.V.¹, Orlova Y.A.¹, Zubkov A.V.¹, Donsckaia D.R.¹
Affiliations:
1. Volgograd State Technical University
Issue: No 1 (2024)
Pages: 26-37
Section: Decision Support Systems
URL: https://journals.rcsi.science/2071-8594/article/view/269773
DOI: https://doi.org/10.14357/20718594240103
EDN: https://elibrary.ru/JRKRVM
ID: 269773

Cite item

Full Text

Abstract

The paper describes an intelligent recommendation system for restoring and training the human respiratory system using individually selected special exercises and increasing motivation when performing them. Personal recommendations for the exercises’ composition are formed on the basis of interactive intellectual analysis of video information about a person’s physical activity, taking into account his/her experience. Machine learning models and methods are used to select exercises and evaluate the effectiveness of their implementation. The results of testing the recommendation system are presented.

Keywords

patient rehabilitation, respiratory system, recommendation system, video information, exercises, motivation

Full Text

About the authors

Alla V. Zaboleeva-Zotova

Volgograd State Technical University

Author for correspondence.
Email: zabzot@gmail.com

Doctor of technical sciences, professor, Adviser, Russian Center for Scientific Information, professor

Russian Federation, Volgograd

Yulia A. Orlova

Volgograd State Technical University

Email: yulia.orlova@gmail.com

Doctor of Technical Sciences, docent, Head of the Department

Russian Federation, Volgograd

Aleksandr V. Zubkov

Volgograd State Technical University

Email: zubkov.alexander.v@gmail.com

Senior lecturer

Russian Federation, Volgograd

Donsckaia R. Donsckaia

Volgograd State Technical University

Email: donsckaia.anastasiya@yandex.ru

Senior lecturer

Russian Federation, Volgograd

References

Blagosklonov N. A., Kobrinskii B. A., Petrovsky A. B. Diagnostika i vybor lecheniya pechenochnoy nedostatochnosti: model′ mul′timnozhestva [Diagnosis and choice of treatment of liver failure: a model of multiset] // Vrach i informatsionnyye tekhnologii [Medical doctor and information technologies]. 2018. No 4. P. 83-88.
Borodulina E.A., Gribova V.V., Vdoushkina E.S., Kiryushina T.M. , Agarkova A.S. Tekhnologii iskusstvennogo intellekta v meditsine. Problemy stanovleniya [Artificial intelligence technologies in medicine. Problems of becoming] // Vrach [Doctor]. 2023. No 3. P. 5-8.
Gribova V. V., Shalfeyeva E. A. Ontologicheskaya obolochka dlya konstruirovaniya servisov prognoza i otsenki sostoyaniy patsiyentov [Ontological shell for designing services for prognosis and assessment of patients' conditions] // Iskusstvenniy intellekt i prinyatie resheniy [Artificial Intelligence and Decision Making]. 2023. No 1. P. 19-31.
Gribova V.V., Borodulina E.A., Okun′ D.B., Yeremenko E.P., Kovalev R.I., Borodulin B.E., Amosova E.A. Baza znaniy dlya razrabotki intellektual′nogo assistenta vrachaftiziatra po upravleniyu protsessom lecheniya bol′nykh tuberkulezom legkikh [Knowledge base for the development of an intelligent assistant to a phthisiologist for managing the treatment of patients with pulmonary tuberculosis] // Vrach i informatsionnyye tekhnologii [Medical doctor and information technologies]. 2023. No 2. P. 58-69.
Zubkov A. V., Stepanov S. V., Noskin V. V., Sibirnyy N. D., Orlova Yu. A. Razrabotka rekomendatel′noy sistemy dlya podbora trenirovok [Development of a recommendation system for the selection of trackers] // Inzhenernyy vestnik Dona [Engineering Bulletin of the Don]. 2023. No 6 (120). P. 284-300.
Kobrinskii B. A., Grigoriev O. G., Smirnov I. V., Molodchenkov A. I., Blagosklonov N. A. Printsipy sozdaniya intellektual′noy sistemy formirovaniya i soprovozhdeniya plana meropriyatiy po zdorov′yesberezheniyu [Principles of creating an intellectual system for the formation and maintenance of an action plan for health care] // Tsifrovoye zdravookhraneniye [Digital healthcare]. 2018. P. 31-33.
Kuznetsova V. P., Brook E. I., Larichev O. I., Narizhny E. Komp′yuternoye obucheniye diagnosticheskim navykam (na primere diagnostiki tromboembolii legochnoy arterii) [Computer training in diagnostic skills (on the example of diagnosis of pulmonary embolism)] // Terapevticheskiy arkhiv [Therapeutic archive]. 2003. Vol. 75. No 10. P. 87-90.
Larichev O. I., Petrovsky A. B. Sistemy podderzhki prinyatiya resheniy: sovremennoye sostoyaniye i perspektivy razvitiya [Decision support systems: current state and prospects of development] // Itogi nauki i tekhniki. Teoriya veroyatnostey. Matematicheskaya statistika. Teoreticheskaya kibernetika. [Results of science and technology. Probability theory. Mathematical statistics. Theoretical cybernetics]. 1987. V. 21. P. 131-164.
Ofitsialniy sayt Algom [Official website of the Algom ] // Electronic resource. URL: https://algom.ru/ (accessed 05.09.2023).
Rybchits G. M., Zubkov A. V., Gomazkova YU. S., Korshunov A. A. Razrabotka modeli dlya raspoznavaniya markerov na baze seti YOLO dlya detektirovaniya tsiklov dykhaniya [Development of a model for marker recognition based on the YOTA network for detecting respiratory cycles] // Innovatsionnyye tekhnologii v obuchenii i proizvodstve [Innovative technologies in training and production]. 2021. P. 139-142.
Khar′kova O. A., Grzhibovskiy A. M. Sravneniye dvukh nesvyazannykh vyborok c ispol′zovaniyem paketa statisticheskikh programm Stata: neparametricheskiye kriterii [Comparison of two unrelated samples using the Stata statistical software package: nonparametric criteria] // Ekologiya cheloveka [Human ecology]. 2014. No 4. P. 60-64.
Bledzhyants G. A., Mishvelov A. E., Nuzhnaya K.V., et al. The effectiveness of the medical decision-making support system" electronic clinical pharmacologist" in the management of patients therapeutic profile // Pharmacophore. 2019. V. 10. No 2. P. 76-81.
Calero Valdez A., Ziefle M., Verbert K., Felfernig A., Holzinger A. Recommender systems for health informatics: state-of-the-art and future perspectives // Machine Learning for Health Informatics: State-of-the-Art and Future Challenges. 2016. Р. 391-414.
Chen R. C., Chiu J. Y., Batj C. T. The recommendation of medicines based on multiple criteria decision making and domain ontology — An example of anti-diabetic medicines // 2011 International Conference on Machine Learning and Cybernetics. 2011. V. 1. P. 27-32.
Davis D. A., Chawla N. V., Christakis N. A., Barabási A. L. Time to CARE: a collaborative engine for practical disease prediction // Data Mining and Knowledge Discovery. 2010. V. 20. P. 388-415.
Faiz I., Mukhtar H., Khan S. An integrated approach of diet and exercise recommendations for diabetes patients //2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom). 2014. P. 537-542.
Fluri B., Wursch M., Pinzger M., Gall H. Change distilling: Tree differencing for fine-grained source code change extraction // IEEE Transactions on software engineering. 2007. V. 33. No 11. P. 725-743.
Furems E., Sokolova L. Experts' knowledge acquisition for differential diagnostics of bronchial asthma in children in STEPCLASS environment // International Journal of Technology, Policy and Management. 2011. V. 11. No 1. P. 68-84.
Ge M., Ricci F., Massimo D. Health-aware food recommender system // Proceedings of the 9th ACM Conference on Recommender Systems. 2015. P. 333-334.
Han Q., Ji M., de Troya I., Gaur M., Zejnilovic L. A hybrid recommender system for patient-doctor matchmaking in primary care // IEEE 5th International Conference on Data Science and Advanced Analytics. 2018. P. 481-490.
Ismail W. The importance of motivation attributes in developing serious games for physical rehabilitation – A case study // MIR Preprints. 2022. No 4. Р. 47.
Kobrinskii B. A., Grigoriev O. G., Molodchenkov A. I., Smirnov I. V., Blagosklonov N. A. Artificial intelligence technologies application for personal health management // 19th IFAC Conference on Technology, Culture and International Stability. 2019. Р. 70-74.
Mardini M.T., Hashky A., Raś Z.W. Personalizing patients to enable shared decision making // Recommender Systems for Medicine and Music. 2021. V. 946. Р. 75–90.
Maslova A.Y., Mishvelov A.E., Dudusheva M.J., et al. Using a decision tree with a feedback function to select therapeutic tactics for viral infection of the respiratory tract in the medical expert system // International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. 2022. V. 13. No 8. P. 1-10.
Orlova Yu., Gorobtsov A., Sychev O., Rozaliev V., Zubkov A., Donsckaia A. Method for determining the dominant type of human breathing using motion capture and machine learning // Algorithms. 2023. V. 16 (5). No 249.
Philip K., Owles H., McVey S., et al. An online breathing and wellbeing programme (ENO Breathe) for people with persistent symptoms following COVID-19: a parallelgroup, single-blind, randomised controlled trial // Lancet Respiratory Medicine. 2022. V. 10. No 9. Р. 851-862.
Zhang Q., Zhang G., Lu J., Wu D. A framework of hybrid recommender system for personalized clinical prescription // The 10th International Conference on Intelligent Systems and Knowledge Engineering. 2015. P. 189-195.