Empirical Mode Decomposition for Power Spectral Density Features in Radar-Based Fall Detection

Yıl 2025, Cilt: 41 Sayı: 1, 318 - 330, 30.04.2025

İbrahim Şeflek

Öz

In recent years, the increase in the number of older people and their tendency to live alone has made them more vulnerable to accidents. The most suffered situation in this regard is their falls. In this study, fall detection is carried out using radar. The proposed method classifies different falls and activities of daily living using radar-based measurements. The signals obtained by means of empirical mode decomposition (EMD) are separated into intrinsic mode functions (IMFs). The power spectral densities (PSD) of IMFs are calculated using the Welch method to provide features for classification. Thus, the effect of IMFs on classification is observed. In the study, conventional machine learning classes are employed, and the Support Vector Machine (SVM) (cubic) classifier detects the fall with 100% accuracy as a result of the PSDs calculated depending on the IMF 2-6 values. Furthermore, the classification results obtained based on other IMFs are almost error-free for some classifiers. Therefore, classification is also performed for seven different movements depending on IMFs. The SVM (cubic) algorithm performs above 90% in this case. The proposed method demonstrates that the effect of classical machine learning remains operative and efficacious.

Anahtar Kelimeler

Radar, Fall detection, Signal Processing, Machine Learning, Classification

Radar-Tabanlı Düşme Tespitinde Güç Spektral Yoğunluk Özellikleri için Ampirik Mod Ayrıştırması

Öz

Son dönemde yaşlı bireylerin artması ve yalnız yaşama eğilimleri onları kazalara açık hale getirmektedir. Bu hususta en fazla muzdarip olunan durum onların düşmesidir. Bu çalışmada radar kullanılarak düşmenin tespiti gerçekleştirilmektedir. Çeşitli düşme ve günlük aktiviteler radar tabanlı ölçümler yapılarak önerilen metot ile sınıflandırılmaktadır. Ampirik mod ayrıştırma (EMD) vasıtasıyla elde edilen sinyaller içsel mod fonksiyonlara (IMFs) ayrılmaktadır. IMF’lerin Welch yöntemiyle güç spektral yoğunlukları (PSD) hesaplanarak sınıflandırma için özellik olması sağlanmaktadır. Böylece IMF’lerin sınıflandırma üzerine etkisi gözlemlenmektedir. Geleneksel makine öğrenme sınıflarının kullanıldığı çalışmada IMF 2-6 değerlerine bağlı olarak hesaplanan PSD’ler sonucu Destek Vektör Makinesi (SVM) (kübik) sınıflandırıcısı düşmeyi %100 doğrulukla tespit etmektedir. Ayrıca diğer IMF’lere bağlı olarak elde edilen sınıflandırma sonuçları da bazı sınıflandırıcılar için neredeyse hatasızdır. Bundan dolayı yedi farklı hareket için de IMF’lere bağlı olarak sınıflandırma gerçekleştirilmektedir. SVM (kübik) algoritması bu durum içinde %90’ının üzerinde bir performans sergilemektedir. Önerilen yöntem ile geleneksel makine öğrenmesinin etkisinin hala aktif ve etkili olduğu sunulmaktadır.

Anahtar Kelimeler

Radar, Düşme Tespiti, Sinyal İşleme, Makine Öğrenmesi, Sınıflandırma

Kaynakça

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