Comparison of Artificial Neural Networks with other Machine Learning Methods in Foot Movement Classification

Year 2023, Volume: 13 Issue: 1, 153 - 171, 15.03.2023

Selin Aydın Fandaklı , Halil Okumuş

https://doi.org/10.31466/kfbd.1214950

Cited By: 1

Abstract

Modern prostheses can be controlled by using gait analysis data from Inertial Measurement Units compared to traditional prostheses. This article aims to classify foot movements for the robotic ankle system in lower limb prostheses to recognize motion intent and adapt to abnormal walking conditions. The statistical features are extracted from IMU data from 11 volunteers aged 20-34 and then the features are classified using machine learning. In this study, the classification accuracies of Naïve Bayes Classifier, Linear Discriminant Analysis, K-Nearest Neighbour Classifier and Support Vector Machines and Artificial Neural Networks in classifying foot movements are examined separately for the raw data and the processed data such as Euler angles and quaternions which estimate with Madwick Filter. Gait analysis data were obtained by using the Inemo inertial module LSM9DS1 work on an NRF52 including 9 DOF, triaxial gyroscope, triaxial accelerometer, and triaxial magnetometer in the Biomechanics Laboratory of the Department of Mechanical Engineering, Middle East Technical University from eleven subjects and achieved an highest classification accuracy rate of 90.9% on test data, 97.3% for training data.

Keywords

K-Nearest Neighbor, Support Vector Machines, Accelerometers, Gyroscope

Ayak Hareketleri Sınıflandırmasında Yapay Sinir Ağlarının diğer Makine Öğrenme Yöntemleri ile Karşılaştırılması

Abstract

Modern protezler, geleneksel protezlere kıyasla Atalet Ölçüm Birimlerinden (IMU'lar) alınan yürüyüş analizi verileri kullanılarak kontrol edilebilir. Bu makale, hareket niyetini tanımak ve anormal yürüme koşullarına uyum sağlamak için alt ekstremite protezlerinde robotik ayak bileği sistemi için ayak hareketlerini sınıflandırmayı amaçlamaktadır. 20-34 yaşları arasındaki 11 gönüllüden toplanan IMU verilerinden istatistiksel özellikler çıkarılmış ve daha sonra öznitelikler makine öğrenmesi kullanılarak sınıflandırılmıştır. Bu çalışmada, Naive Bayes Sınıflandırıcısı, Doğrusal Ayırım Analizi, K-En Yakın Komşu Sınıflandırıcısı ve Destek Vektör Makineleri ve Yapay Sinir Ağları ayak hareketlerini sınıflandırmadaki sınıflandırma doğrulukları ham veriler için ve Madwick Filtresi ile tahmin edilen Euler açıları ve kuaterniyonlar gibi işlenmiş veriler için ayrı ayrı incelenmiştir. Yürüyüş analizi verileri, Orta Doğu Teknik Üniversitesi Makine Mühendisliği Bölümü Biyomekanik Laboratuvarı'nda on bir denekten üç eksenli jiroskop, üç eksenli ivmeölçer ve üç eksenli manyetometre içeren 9 serbestlik dereceli bir NRF52 üzerinde Inemo atalet modülü LSM9DS1 çalışması kullanılarak alındı ve test verilerinde % 90.90, eğitim verilerinde %97.3 en yüksek sınıflandırma doğruluk oranı elde edildi.

Keywords

K-En yakın komşu, destek vektör makineleri, ivmeölçer, jiroskop

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