İnmeli Hastalarda Üst Ekstremite Hareket Başarımının Yapay Öğrenme Teknikleri ile Kestirimi

Year 2021, Volume: 10 Issue: 1, 245 - 253, 25.06.2021

Mücahit Çalışan , Muhammed Fatih Talu

https://doi.org/10.46810/tdfd.881205

Abstract

İnme benzeri nörolojik hastalıklardan sonra kişilerin rehabilite hizmetlerine yönlendirilmesinin ana nedeni, bireylerin günlük yaşamdaki yeteneklerini normal düzeye çıkartmaktır. Kişilerin günlük yaşamlarındaki faaliyetlerini ölçmek bu rehabilitasyon hizmetlerinin daha sağlıklı ilerlemesini sağlamaktadır. Çalışmamızda doktorlar tarafından rehabilitasyon sırasında inme hastalarının hareket işlevini değerlendirmek için yaygın olarak kullanılan Brunnstrom Evrelemesi incelenmiştir. Çalışma özgül olarak Brunnstrom Evrelemesi testinin üst ekstremite bölümü 4a hareketine uyarlanmıştır. Hastaların günlük hareketleri ivmeölçerler sensörleri ile değerlendirilmiştir. Bu çalışmada izlenilen metodoloji ile gönüllü 15 inme hastası ve 80 sağlıklı bireyden sensör datası toplanmıştır. Elde edilen sensör verisi medikal uzman tarafından yorumlanmıştır. Böylece sağlıklı ve hasta bireylerin hareket verileri arasındaki tutarlılıklar analiz edilmiştir. Analiz işlemi sonucunda elde edilen veriler yapay öğrenme yöntemleriyle incelenmiş ve sağlıklı/sağlıksız şeklinde sınıflandırılmıştır. Çalışmanın metodolojisi kişilerin günlük yaşamlarında üst/alt ekstremite performansını arttırmak için tasarlanmış araştırmalar için uygundur.

Keywords

Brunnstrom Evrelemesi, İnme, Makine Öğrenmesi, İvmeölçer

Estimation of Upper Extremity Movement Performance in Stroke Patients with Artificial Learning Techniques

Abstract

The main reason why people are directed to rehabilitation services after stroke-like neurological diseases are to bring individuals' daily abilities to a normal level. Measuring the activities of people in their daily lives ensures that these rehabilitation services progress more healthily. In our study, Brunnstrom Hemiplegia Recovery Staging, which is widely used by doctors to evaluate the movement function of stroke patients during rehabilitation, was examined. The study was specifically adapted to the upper extremity stage 4a movement of the Brunnstrom Staging. Daily movements of patients were evaluated with accelerometer sensors. With this methodology, sensor data was collected from 15 volunteer stroke patients and 80 healthy individuals. These sensor data were interpreted by the medical professional. Thus, consistency between movement data of healthy and sick individuals was analyzed. The data obtained as a result of the analysis process were examined with artificial learning methods and classified as healthy/unhealthy. The methodology of the study is suitable for research designed to increase upper / lower extremity performance in the daily life of individuals.

Keywords

Brunnstrom Staging, Stroke, Machine Learning, Accelerometer

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