Machine learning-based finger gesture classification using hybrid data

Abstract

Advances in biomedical engineering and robotic technology are increasing the importance of human-machine interaction through the detection of hand-finger movements used by traditional methods in applications ranging from prosthetics to rehabilitation. In this study, it is aimed to increase the accuracy performance by creating and filtering the necessary attribute dataset for supervised machine learning algorithms to create high-accuracy training and test performance by ensuring that camera images and sEMG sensor data are recorded simultaneously. Literature reviews of similar hybrid studies report accuracy rates ranging from 80.22% to 99.58%. In this study, after data preprocessing and feature extraction, the performance of Support Vector Machines (SVM), k-nearest Neighborhood (KNN) and Artificial Neural Networks (ANN) was evaluated using Matlab’s classification tutorial application, and test validation results of 92.70%, 92.26% and 90.41% were obtained, respectively. When the developed filtering process is applied to the dataset created during data preprocessing, 99.11%, 98.55% and 97.87% verification performance values are obtained, respectively. This article presents an integrated approach that combines sEMG sensors and camera image data, leveraging the strengths of both methods. The proposed hybrid method improves the accuracy of machine learning algorithms to classify hand-finger movements, providing a robust solution for applications that require precise motion detection and control. This research introduces a new approach in human-machine interaction technologies to the literature.

Keywords

• sEMG
• signal processing
• image processing
• machine learning

Hibrit veri kullanarak makine öğrenme temelli parmak hareketi sınıflandırma

Öz

Biyomedikal mühendisliği ve robotik teknolojideki gelişmeler, protezden rehabilitasyona kadar çeşitli uygulamalarda geleneksel yöntemlerle kullanılan el-parmak hareketlerinin algılanması yoluyla insan-makine etkileşiminin önemini artırmaktadır. Bu çalışmada, kamera görüntüleri ve sEMG sensör verilerinin eş zamanlı olarak kaydedilmesini sağlanarak denetimli makine öğrenme algoritmalarının yüksek doğrulukta eğitim ve test performansı oluşturabilmesi için gerekli öznitelik veri setinin oluşturulabilmesi ve filtrelenerek doğruluk performansının arttırılması amaçlanmaktadır. Benzer hibrit çalışmaların literatür taramaları, %80,22 ile %99,58 arasında değişen doğruluk oranları bildirmektedir. Bu çalışmada, veri önişleme ve öznitelik çıkarımından sonra, Destek Vektör Makineleri (SVM), k-en yakın Komşuluk (KNN) ve Yapay Sinir Ağlarının (ANN) performansı Matlab’ın sınıflandırma öğreticisi uygulaması kullanılarak değerlendirilmiş ve sırasıyla %92,70, %92,26 ve %90,41 test doğrulama sonuçları elde edilmiştir. Veri ön işleme sırasında oluşturulan verisetine, geliştirilen filtreleme işlemi uygulandığında sırasıyla %99,11, %98,55 ve %97,87 doğrulama performans değerleri elde edilmektedir. Bu makale, her iki yöntemin de güçlü yönlerinden yararlanarak sEMG sensörlerini ve kamera görüntü verilerini birleştiren bütünleşik bir yaklaşım sunmaktadır. Önerilen hibrit yöntem, el-parmak hareketlerini sınıflandırmak için makine öğrenimi algoritmalarının doğruluğunu artırarak, hassas hareket algılama ve kontrolü gerektiren uygulamalar için sağlam bir çözüm sunmaktadır. Bu araştırma, insan-makine etkileşimi teknolojilerinde yeni bir yaklaşımı literatüre kazandırmaktadır.

Anahtar Kelimeler

• sEMG
• sinyal işleme
• görüntü işleme
• makine öğrenmesi.

Kaynakça

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