Robotics in the medicine of the future: treatment and rehabilitation

Inna Mezentseva; Olha Pavlovych; Petro Patiei; Valentyna Yarlykova; Ihor  Savchuk

doi:10.15649/2346075X.5708

Authors

Inna Mezentseva Department of Physical Therapy, Institution of Higher Education “Academy of Recreation Technologies and Law”, Lutsk, Ukraine https://orcid.org/0000-0002-1455-9708
Olha Pavlovych Department of Physical Therapy, Institution of Higher Education “Academy of Recreation Technologies and Law”, Lutsk, Ukraine. https://orcid.org/0000-0002-3840-6933
Petro Patiei Department of Physical Therapy, Institution of Higher Education “Academy of Recreation Technologies and Law”, Lutsk, Ukraine. https://orcid.org/0000-0001-5622-9590
Valentyna Yarlykova Department of Physical Therapy, Institution of Higher Education “Academy of Recreation Technologies and Law”, Lutsk, Ukraine. https://orcid.org/0000-0002-6993-9433
Ihor Savchuk Department of Physical Therapy, Institution of Higher Education “Academy of Recreation Technologies and Law”, Lutsk, Ukraine. https://orcid.org/0000-0002-5724-0078

DOI:

https://doi.org/10.15649/2346075X.5708

Keywords:

Robotics, Stroke rehabilitation, Exoskeletons, Artificial intelligence, Neurorehabilitation

Abstract

Introduction. The growing demand for effective post-stroke rehabilitation has accelerated the integration of robotic systems incorporating artificial intelligence and sensor technologies into clinical practice. Objective. To evaluate the effectiveness of robotic systems in the rehabilitation of patients with ischemic stroke in Ukrainian clinics. Materials and Methods. A controlled experimental study was conducted with 62 patients randomly assigned to control (conventional physiotherapy) and experimental (physiotherapy plus robotic-assisted therapy) groups. Robotic exoskeletons and sensorbased simulators were used. Outcomes were assessed using validated tools: Fugl-Meyer Assessment (FMA), Timed Up and Go (TUG), and 6-Minute Walk Test (6MWT). Adaptive algorithms adjusted therapy intensity to individual needs. Results. The experimental group showed greater improvement compared with the control group, with a mean increase of 6.63 points on the FMA scale, a reduction of 4.17 seconds in TUG time, and an increase of 44.47 meters in 6MWT distance. Improvements were statistically significant (p < 0.05) and consistent with international evidence supporting early and intensive rehabilitation. However, the relatively small sample size and short follow-up period limit generalizability. Conclusions. Robotic-assisted rehabilitation significantly enhances motor function, endurance, and functional mobility after stroke. These findings support its integration as a complementary strategy within clinical rehabilitation protocols. Further multicenter studies with long-term follow-up are needed to standardize protocols and evaluate sustainability of outcomes.

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