Rehabilitasyonunda Robot Destekli Yürüme Eğitimi: Lokomat Özelliklerine Multidisipliner Bir Bakış

Öz

İnme, dünya genelinde önemli bir ölüm ve sakatlık kaynağıdır. Artan yaşam süresi ile birlikte son 20 yılda inme riski %50 oranında artmış ve şu anda her dört kişiden biri inme geçirme riski taşımaktadır. İnme rehabilitasyonunun erken aşamalarında, bağımsızlığı artırmak ve hastaların hastaneden kısa sürede taburcu olmasını sağlamak için disiplinlerarası bir yaklaşım gereklidir. Bu yaklaşım, hekimler, fizyoterapistler, hemşireler, psikologlar ve diğer sağlık profesyonellerinden oluşur. Bu derleme, inme rehabilitasyonunda kullanılan Lokomat adlı robot destekli yürüyüş eğitimi cihazının seans odaklı parametrelerinin etkilerini incelemektedir. Yapılan çalışmalar, yüksek kılavuz kuvvet seviyelerinin (%100) sensorimotor korteks etkinliğini azalttığını, daha düşük kılavuz kuvvet seviyelerinin (%30-50) kas aktivasyonunu artırarak motor öğrenmeyi desteklediğini göstermektedir. Ayrıca, vücut ağırlığı desteği %30-50 arasında ayarlandığında en etkili sonuçlar elde edilmiştir. Yürüyüş hızının artırılması ise kas aktivasyonunu güçlendirmiştir.

Anahtar Kelimeler

• Arttırılmış performans geri bildirimi
• vücut ağırlık desteği
• inme

Robot-Assisted Gait Training in Stroke Rehabilitation: A Multidisciplinary Overview on Lokomat Features

Abstract

Stroke is a significant cause of death and disability worldwide. With the increasing lifespan, the risk of stroke has risen by 50% in the last 20 years, and currently, one in four individuals is at risk of experiencing a stroke. In the early stages of stroke rehabilitation, an interdisciplinary approach is essential to increase independence and ensure patients are discharged from the hospital in a shorter time. This approach involves physicians, physiotherapists, nurses, psychologists, and other healthcare professionals. This review examines the effects of session-specific parameters of the robot-assisted gait training device, Lokomat, used in stroke rehabilitation. Studies have shown that high guidance force levels (100%) decrease sensorimotor cortex engagement, while lower guidance force levels (30-50%) enhance muscle activation and support motor learning. Additionally, adjusting body weight support between 30% and 50% has been found to yield the most effective results. Increasing walking speed has also been observed to enhance muscle activation.

Keywords

• Augmented performance feedback
• body weight support
• stroke

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