Giyilebilir dış iskelet el
Yükleniyor...
Tarih
2020-08-28
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Batman Üniversitesi Fen Bilimleri Enstitüsü
Erişim Hakkı
info:eu-repo/semantics/openAccess
Attribution-ShareAlike 3.0 United States
Attribution-ShareAlike 3.0 United States
Özet
Bu tez çalışmasında, felç, omurilik yaralanmaları ve tendon yaralanmaları gibi birçok nedenden dolayı el fonksiyonlarının bir kısmını veya tamamını kaybetmiş hastaların rehabilitasyonunu sağlamak ve parmak hareketlerini desteklemek amacıyla kullanılabilecek bir el dış iskelet sistemi tasarlanmıştır. Elektrikli aktüatör kullanılarak tasarımı yapılan el dış iskelet sistemi, elin ön (dorsal) kısmına yerleştirilen ve 5 parmak hareketini aktif olarak destekleyen bir yapıya sahiptir. Yapılan tasarımda başparmakta 2, diğer parmaklar için 3 olmak üzere toplam 14 aktif ekstansiyon/fleksiyon serbestlik derecesi bulunmaktadır. Ayrıca her parmak için abdüksiyon/addüksiyon ve bilekte ekstansiyon/fleksiyon hareketlerini oluşturabilmek için toplam 6 pasif serbestlik derecesine sahiptir.
Elin antropometrik ölçüleri ve serbestlik dereceleri referans alınarak 3 boyutlu modelleme uygulamalarıyla iskelet sisteminin tasarımı yapılmıştır. Tasarımı basmak için hafif, dayanıklı, esnek ve tamamen doğada çözünebilen PLA (poliaktik asit) filament kullanılmıştır. 3 boyutlu yazıcıyla üretilen prototipin hafif ve taşınabilir olması hastaya evde rehabilitasyon ve günlük hayat aktivitelerinde yardımcı olma imkanı sağlayabilecek potansiyeldedir.
Tasarlanan sistem için geliştirilen android uygulama ile el dış iskeletinin kontrolü sağlanmaktadır. Fizik tedavi uzmanı önerileriyle tanımlanan rehabilitasyon egzersizlerini gerçekleştiren, el hareketlerini desteklemek amacıyla sEMG, GYRO, ACC ve ORI sinyallerini kullanarak niyet algılayan ve 32 farklı el hareketini gerçek zamanlı olarak yapay zeka algoritmalarıyla sınıflandırabilen el dış iskelet sisteminin tasarımı gerçekleştirilmiştir.
Geliştirilen sistemde ön kola yerleştirilen MYO kol bandı ile EMG, GYRO, ACC ve ORI sensörleriyle alınan veriler kullanılarak niyet algılama sağlanmıştır. EMG sinyalleri MVC tekniğiyle normalize edilmiş ve bu sinyallerden MAV, STD, VAR, RMS, IEMG, ZC ve WL özellik vektörleri çıkarılarak etkin özellikler seçilmiştir. Sınıflandırma aşamasında makine öğrenmesi algoritmalarından doğrusal destek vektör makineleri (SVM) kullanılarak oluşturulan 5 sınıflandırıcı paralel olarak kullanılmıştır.
In this thesis, hand exoskeleton system is designed to provide rehabilitation and support finger movements for patients who have lost some or all of their hand functions due to many reasons such as stroke, stroke, spinal cord injuries, tendon injuries. The hand exoskeletal system, which designed using an electric actuator, has a structure that is placed on the front (dorsal) part of the hand and actively supports 5 finger movements. In the design, there are 14 active extension / flexion degrees of freedom, 2 of which are in the thumb and 3 for the other fingers. It also has a total of 6 passive degrees of freedom to create abduction / adduction and extension / flexion movements in the wrist for each finger. In addition, the design has 6 passive degrees of freedom to create abduction / adduction for each finger and extension / flexion movements for wrist. The skeletal system was designed with 3D modeling applications by taking the anthropometric dimensions and degrees of freedom of the hand as reference. To print the design, PLA (polylactic acid) filament is used as a light, durable, flexible and completely biodegradable. The light and portable prototype produced with a 3D printer provides the opportunity to assist the patient in home rehabilitation and daily life activities. Control of the hand exoskeleton is provided with the android application developed for the designed system. The hand exoskeleton system, which performs rehabilitation exercises defined by the recommendations of the physiotherapist, perceives intent using sEMG, GYRO, ACC and ORI signals to support hand movements and can classify 32 different hand movements with artificial intelligence algorithms in real time, has been designed. In the developed system, intent detection was achieved by using the data received with EMG, GYRO, ACC and ORI sensors with the MYO armband placed on the forearm. EMG signals have been normalized with MVC technique and MAV, STD, VAR, RMS, IEMG, ZC and WL feature vectors have been removed and effective features have been selected. In the classification phase, 5 classifiers created using linear support vector machines (SVM), which is one of the machine learning algorithms, were used in parallel.
In this thesis, hand exoskeleton system is designed to provide rehabilitation and support finger movements for patients who have lost some or all of their hand functions due to many reasons such as stroke, stroke, spinal cord injuries, tendon injuries. The hand exoskeletal system, which designed using an electric actuator, has a structure that is placed on the front (dorsal) part of the hand and actively supports 5 finger movements. In the design, there are 14 active extension / flexion degrees of freedom, 2 of which are in the thumb and 3 for the other fingers. It also has a total of 6 passive degrees of freedom to create abduction / adduction and extension / flexion movements in the wrist for each finger. In addition, the design has 6 passive degrees of freedom to create abduction / adduction for each finger and extension / flexion movements for wrist. The skeletal system was designed with 3D modeling applications by taking the anthropometric dimensions and degrees of freedom of the hand as reference. To print the design, PLA (polylactic acid) filament is used as a light, durable, flexible and completely biodegradable. The light and portable prototype produced with a 3D printer provides the opportunity to assist the patient in home rehabilitation and daily life activities. Control of the hand exoskeleton is provided with the android application developed for the designed system. The hand exoskeleton system, which performs rehabilitation exercises defined by the recommendations of the physiotherapist, perceives intent using sEMG, GYRO, ACC and ORI signals to support hand movements and can classify 32 different hand movements with artificial intelligence algorithms in real time, has been designed. In the developed system, intent detection was achieved by using the data received with EMG, GYRO, ACC and ORI sensors with the MYO armband placed on the forearm. EMG signals have been normalized with MVC technique and MAV, STD, VAR, RMS, IEMG, ZC and WL feature vectors have been removed and effective features have been selected. In the classification phase, 5 classifiers created using linear support vector machines (SVM), which is one of the machine learning algorithms, were used in parallel.
Açıklama
Anahtar Kelimeler
El Dış İskelet Sistemi, El Rehabilitasyonu, EMG Sinyalleri, Özellik Çıkarma, Özellik Seçme, MYO Kol Bandı, Hand Exoskeleton, Hand Rehabilitation, EMG Signals, Feature Extraction, Feature Selection, MYO Armband
Kaynak
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Hazar, Y. (2020). Giyilebilir dış iskelet el. (Yayınlanmamış Yüksek Lisans Tezi). Batman Üniversitesi Fen Bilimleri Enstitüsü, Batman.
