Deniz Hande KISA, Mehmet Akif ÖZDEMİR, Onan GÜREN, Ayşegül ALAYBEYOĞLU SOY

El hareketi tahmini için EMG sinyalleri ve uyarlamalı sinirsel bulanık çıkarım sistemine (ANFIS) dayalı bir karar verme mekanizması

Üst ekstremite hareketi tam olarak sağlanamadığında, yapay zeka (artificial intelligence/AI) sistemleri kullanıcılara amaçlanan hareketin uygulanması konusunda yardımcı olurlar. Kas aktivitesinin temsili olan elektromiyografi (EMG), sanal gerçeklik uygulamaları ve protez kontrolleri gibi AI-tabanlı sistemlerde kullanıldığında çeşitli faydalar sağlar. Bu çalışmada, bahsedilen sistemlere etkin kontrol sunmak ve tahmin performanslarını iyileştirmek amacıyla bulanık mantık (Fuzzy Logic/FL)-tabanlı bir karar verme mekanizması sunulmuştur. Bu bağlamda, 30 katılımcıdan yedi farklı el hareketini taklit etmesi sonucunda oluşan EMG sinyalleri toplandı. Gerekli ön işleme ve bölütleme işlemlerinin ardından elde edilen sinyallere Hilbert-Huang Dönüşümü'nün (HHD) ilk aşaması Görgül Kip Ayrışımı (GKA) metodu uygulandı ve İçsel Mod Fonksiyonları (İMF) elde edildi. İstatistiksel İMF seçim yöntemi ile belirlenen İMF’lere HHD uygulanmasıyla iyi çözünürlüklü zaman-frekans (time-frequency/TF) imgeleri elde edildi. Zaman ve frekans uzayının ortak temsiline dayalı görselleştirilmiş TF imgelerinden çeşitli ayırt edici öznitelikler çıkartıldı. İki farklı kümeleme tekniği uygulanan öznitelik veri seti, Uyarlamalı Sinirsel Bulanık Çıkarım Sistemi'ne (ANFIS) girdi olarak verildi. Yedi el hareketi sınıflandırması için Azaltımlı (Subtractive Clustering/SC) ve Bulanık C-ortalama (Fuzzy C-mean/FCM) kümeleme yöntemleri için ortalama doğruluk değerleri sırasıyla %93,88 ve %92,10 olarak elde edilmiştir. TF temsiline dayalı özniteliklerin FL yaklaşımlarıyla sınıflandırılması sonucu elde edilen bulgular, EMG gibi durağan ve doğrusal olmayan biyolojik sinyallerin sınıflandırılması için umut verici olduğunu göstermiştir.

Anahtar Kelimeler:

Bulanık mantık, görgül kip ayrışımı, EMG, Hilbert-Huang Dönüşümü, zaman-frekans analizi

A decision-making mechanism based on EMG signals and adaptive neural fuzzy inference system (ANFIS) for hand gesture prediction

Artificial intelligence (AI)-based technologies assist users in applying the intended action when upper extremity movement cannot be fully provided. Electromyography (EMG), a depiction of muscle activity, offers various advantages when employed with AI-based systems like virtual reality applications and prosthetics controls. In this paper, a fuzzy logic (FL)-based decision-making mechanism is presented in order to provide effective control and improve the prediction performance of the stated systems. In this regard, EMG signals were collected from 30 participants when imitating different seven hand gestures. After the necessary preprocessing and segmentation processes, the Empirical Mode Decomposition (EMD) method which is the first stage of the Hilbert-Huang Transform (HHT) was applied and Intrinsic Mode Functions (IMF) were obtained. High-resolution time-frequency (TF) images were obtained by applying HHT to the IMFs determined by a statistical selection method. Various distinctive features were extracted from the visualized TF images based on the joint representation of the time and frequency domain. The Adaptive Neuro-Fuzzy Inference System (ANFIS) was then fed these features, which used two alternative clustering approaches. For seven hand gesture classifications, the average accuracy scores for the Subtractive Clustering (SC) and Fuzzy C-mean (FCM) clustering methods were obtained as 93.88% and 92.10%, respectively. The proposed feature extraction method based on TF representation combined with FL techniques yielded encouraging results for the classification of nonstationary and nonlinear biological signals such as EMG.

Keywords:

Fuzzy logic, empirical mode decomposition, EMG, Hilbert-Huang Transform, time-frequency analysis,

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