DOI: 10.22270/jmpas.V10I5.1594

VOLUME - 10 ISSUE - 5 SEPTEMBER -OCTOBER 2021

Remote rehabilitation of the post-stroke patients with the hand and finger nerves damaged through wireless devices in individual conditions

Zhuravska Iryna M

Petro Mohyla Black Sea National University, Mykolaiv, Ukraine

ABSTRACT

After the stroke, the hand nerves, the motor and/or sensory functions are impaired, especially fine motor skills of each finger individually or a group of fingers. Previous studies have shown that the combination of various methods, e.g., medication, physical therapy, as well as exercises to strengthen muscles and restore fine motor skills with personal small-sized automatized devices are effective. In the paper is proposed a method of constructing a “green zone” (optimal for patient training mode), and “red zone” (dangerous training mode), which correspond to the model of quantitative dependence the time fulfill several sequential actions patient based on the moving average. A decrease of muscle spasticity of the arm in scores following the Modified Ashworth Scale (MAS) was used as an optimality criterion. For remote rehabilitation of the post-stroke patients in individual conditions the wireless devices of the “Reflex Txx” series were developed, which has in-built Hall sensors or touch sensors. The remote rehabilitation training data are transmitted via a wireless channel (4G or Wi-Fi network), accumulated, and analyzed on the microservices of the developed hardware and software complex. The results of each personal training are displayed on the user's gadgets (smartphone, laptop, etc.) in real-time. Implemented electronic components and architecture of the developed devices are based on a cost-effective Arduino platform hardware. Using of “Reflex Txx” series’ devices, in addition to conventional physiotherapy, gives positive shifts in treatment after only 150 minutes of the entire trial during even short-term (1 week) exercise.

Keywords:

Post-stroke damage, hand nerves injury, remote rehabilitation, optimal training mode, training wireless devices, Arduino platform


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