Cybernetics and Computer Engineering, 2020, 3(201)
VOVK М.І., PhD (Biology), Senior Researcher,
Head of Bioelectrical Control & Medical Cybernetics Department
e-mail: email@example.com; firstname.lastname@example.org
KUTSIAK О.А., PhD (Engineering),
Senior Researcher of the Bioelectrical Control & Medical Cybernetics Department
International Research and Training Center for Information Technologies
and Systems of the NAS of Ukraine and of MES of Ukraine,
40, Acad. Hlushkov av. Kyiv, 03187, Ukraine
INFORMATION TECHNOLOGY FOR FORMING A PERSONAL MOVEMENT REHABILITATION PLAN AFTER A STROKE
Introduction. Stroke ranks second in the list of major causes of death and is the leading cause of disability in Ukraine. Synthesis of innovative technologies that help to movement restoration after stroke is an urgent scientific and practical task.
The purpose of the article is to synthesize the information technology for forming a personal movement rehabilitation plan after a stroke on the basis of quantitative assessment of movement deficit according to the evidence criteria.
Results. The technology for information support for forming a movement training personal plan to restore movements after a stroke has been developed. This technology is implemented by the specialized software module “MovementRehabStroke 1.0” for information and consulting support to the physician in determining the plan: movements, programs, movements training duration based on electromyostimulation and / or biofeedback.
The structural and functional model of the operator (physician) and software module “MovementRehabStroke 1.0” interaction has been developed. This module consists of a graphical interface and basic information modules: Database, Module “MovementTestStroke 1.1” for quantitative assessment of movement deficit and Module for forming the personal movement training plan. The interface provides the interactive mode of work with information modules for operator.
An algorithm has been developed for the operation of specialized software module “MovementRehabStroke 1.0” in researching on the formation of a personal training plan for patient movements based on indicators of quantitative assessment of movement deficit, which is provided by the information module “MovementTestStroke 1.1” and information received from a personal electronic medical record (EMR) of the patient: indicators of neurological status, concomitant diseases etc.
Conclusions. The obtained results will contribute to the creation of a new class of mobile means of digital medicine — mobile applications installed in the structure of the smartphones for movements assessment after a stroke, forming the personal rehabilitation plan, and assessment of rehabilitation results.
Keywords: information technology, digital medicine, software modules, stroke, movements, diagnostics, rehabilitation, personal plan, structural and functional model, algorithm, electrical stimulation, biofeedback.
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