DOI:https://doi.org/10.15407/kvt198.04.062
Cybernetics and Computer Engineering, 2019, 4(198)
VOVK М.І., PhD (Biology), Senior Researcher,
Head of Bioelectrical Control & Medical Cybernetics Department
e-mail: vovk@irtc.org.ua; imvovk3940@gmail.com
KUTSYAK О.А., PhD (Engineering),
Senior Researcher of Bioelectrical Control & Medical Cybernetics Department
e-mail: spirotech85@ukr.net
International Research and Training Center for Information Technologies and Systems of the National Academy of Sciences of Ukraine and of Ministry of Education and Science of Ukraine,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine
SOFTWARE MODULE FOR PERSONAL DIAGNOSTICS OF MOTOR FUNCTIONS AFTER STROKE
Introduction. Diagnostics of motor functions after stroke plays an important role in the formation of a rehabilitation program. The results of the preliminary clinical trials of our proposed technique for quantitative assessment of motor functions deficit during studying the dynamics of movement restoring based on bio-informational technology of motor control TRENAR® confirmed the advisability of using this technique to create a new algorithmic and software tools for personal diagnostics of motor functions.
The purpose of the paper is to develop a specialized module for the personal diagnostics of motor functions in patients after stroke, which software implements the determination of the degree of motor functions disorders and the results of their recovery using the technique for quantitative assessment of motor functions deficit.
Results. The structural and functional model of the software module for personal diagnostics of motor functions and the effectiveness of their recovery as a result of rehabilitation measures in patients after stroke has been developed.
An algorithm for diagnostic the motor functions disorder degree of the affected limbs in patients after stroke and the activity diagram of software module using Unified Modeling Language (UML) are presented. The software module “Movement Test Stroke” has been made in Visual Studio 2013 software environment. The programming language is C#. The module is installed in the PC structure. Diagnostic benefits: the ability to obtain an integrated quantitative assessment of the motor functions deficit of the upper and lower limb at the level of separate joints, hand or walking according to relevant evidential criteria, and assessment of muscle hyper- or hypotone at different stages of rehabilitation. The advantage of diagnostics is that the motor func functions disorder degree is performed relative to the patient’s own healthy limbs, the motor functions of which characterize the individual norm of disorders absence.
Conclusions. The quantitative assessment of motor function deficit by evidential criteria, which is provided by the software module “Movement Test Stroke” is the basis to synthesize the digital health mobile means for information and advisory assistance to the physician in creating and making adjustments to personal plan for recovery the motor functions affected by pathology at different stages of stroke rehabilitation.
Keywords: software module, structural and functional model, diagnostics, algorithm, motor functions, personal quantitative assessment, stroke.
REFERENCES
1 Action Plan for Stroke in Europe 2018-2030 / Bo Norrving et al. European Stroke Journal. 2018. Vol. 3(4). P. 309-336. https://doi.org/10.1177/2396987318808719
2 Gritsenko V.I., Vovk M.I. Trenar – Innovative Technology of Restoration of Movements. In Materials of the International Scientific and Practical Forum “Science and Business – the basis of economic development”. Dnipro, 2012. P. 204-206. (in Russian).
3 Vovk M.I. Information Technology of Movement Control. Evolution of Synthesis and Development Prospects. Kibernetika i vycislitelnaa tehnika. 2018. No 4 (194). P. 79-97. (in Ukrainian). https://doi.org/10.15407/kvt194.04.079
4 Vovk M.I., Kutsiak O.A., Lauta A.D., Ovcharenko M.A. Information Support of Researches on the Dynamics of Movement Restoration After the Stroke. Kibernetika i vycislitelnaa tehnika. 2017. No 3 (189). P. 61-78. (in Ukrainian). https://doi.org/10.15407/kvt189.03.061
5 Belova A., Shchepetova O. Scales, tests and questionnaires in medical rehabilitation. Moscow, 2002. (in Russian).
6 Kadykov A., Chernikova L., Shahparonova N. Rehabilitation of neurological patients. Moscow, 2008. (in Russian).
7 Smychek V., Ponomareva E. Craniocerebral trauma (clinic, treatment, examination, rehabilitation). Minsk, 2010. (in Russian).
8 Buch G., Rambo D., Yakobson I. The language of the UML. User’s Guide. Moscow, 2006. (in Russian).
9 Theory and practice of UML. Activity diagram. URL: http://it-gost.ru/articles/view_articles/96 (Last accessed: 04.06.2019). (in Russian).
Received 07.08.2019