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Journals / Journal of Artificial Intelligence and Data Science / 2026 / Cilt: 6 Sayı: 1

Regression-Based Estimation of Wearable Sensor Signal Quality Using Statistical and Spectral Features

İstatistiksel ve Spektral Özellikler Kullanılarak Giyilebilir Sensör Sinyal Kalitesinin Regresyon Tabanlı Tahmini

Gözde Çay(İZMİR KATİP ÇELEBİ ÜNİVERSİTESİ)0000-0003-1985-1772
Pages
96–102
Publication Date
2026-06-29
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results showed high prediction performance across all physiological modalities.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Physiological signals collected from wearable sensors are usually prone to noise due to motion artifacts, sensor displacement, and other sources.
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Key Findings & Data:The results showed high prediction performance across all physiological modalities.
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Conclusion & Impact:These findings demonstrated that physiological signal degradation exhibited distinct modality-specific characteristics and interpretable machine learning techniques could provide valuable insight into the mechanisms of signal degradation.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:biyomedikal sinyaller · regresyon · sinyal kalitesi

Abstract

Physiological signals collected from wearable sensors are usually prone to noise due to motion artifacts, sensor displacement, and other sources. Since this signal degradation might cause problems in signal reliability, signal quality needs to be checked and determined. Although machine learning techniques have been successfully applied to signal quality assessment, the dominant features and their effects on different physiological modalities still need to be investigated. In this study, I performed an analysis on modality-specific signal degradation mechanisms in electrocardiography, electrodermal activity, and respiration signals using interpretable machine learning techniques including Random Forest, eXteme Gradient Boosting, and Light Gradient Boosting Machine. The models were trained using Leave-One-Subject-Out Cross-Validation to estimate the signal quality levels. In addition, feature importance analysis and SHapley Additive exPlanations were employed to investigate model interpretability. The results showed high prediction performance across all physiological modalities. EDA signals performed the highest coefficient of determination (R2 = 0.996) followed by respiration (R2 = 0.954) and ECG (R2 = 0.908). Feature importance and SHAP analysis were also confirmed these results, in addition they reflected the fact that the most dominant feature differs for EDA and ECG/respiration signals. These findings demonstrated that physiological signal degradation exhibited distinct modality-specific characteristics and interpretable machine learning techniques could provide valuable insight into the mechanisms of signal degradation.

Özet

Giyilebilir sensörlerden toplanan fizyolojik sinyaller genellikle hareket artefaktları, sensör yer değiştirmesi ve diğer kaynaklar nedeniyle gürültüye eğilimlidir. Bu sinyal bozulması sinyal güvenilirliğinde sorunlara yol açabileceğinden, sinyal kalitesinin kontrol edilmesi ve belirlenmesi gerekmektedir. Makine öğrenme teknikleri sinyal kalitesi değerlendirmesinde başarıyla uygulanmış olsa da, baskın özellikler ve bunların farklı fizyolojik modaliteler üzerindeki etkileri hala araştırılmaya ihtiyaç duymaktadır. Bu çalışmada, yorumlanabilir makine öğrenme teknikleri (Random Forest, eXteme Gradient Boosting ve Light Gradient Boosting Machine) kullanarak elektrokardiyografi, elektrodermal aktivite ve solunum sinyallerindeki modaliteye özgü sinyal bozulma mekanizmalarını analiz ettim. Modeller, sinyal kalitesi seviyelerini tahmin etmek için Leave-One-Subject-Out Cross-Validation yöntemi kullanılarak eğitildi. Ek olarak, model yorumlanabilirliğini araştırmak için özellik önem analizi ve SHapley Additive exPlanations kullanıldı. Sonuçlar, tüm fizyolojik modalitelerde yüksek tahmin performansı gösterdi. EDA sinyalleri en yüksek belirleme katsayısını (R2 = 0,996) gösterirken, bunu solunum (R2 = 0,954) ve EKG (R2 = 0,908) izledi. Özellik önemi ve SHAP analizi de bu sonuçları doğruladı ve ayrıca en baskın özelliğin EDA ve EKG/solunum sinyalleri için farklı olduğunu yansıttı. Bu bulgular, fizyolojik sinyal bozulmasının farklı modaliteye özgü özellikler sergilediğini ve yorumlanabilir makine öğrenme tekniklerinin sinyal bozulma mekanizmalarına değerli bilgiler sağlayabileceğini göstermiştir.

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Cite This Article

Çay, G. (2026). Regression-Based Estimation of Wearable Sensor Signal Quality Using Statistical and Spectral Features. Journal of Artificial Intelligence and Data Science, Cilt: 6 Sayı: 1, 96–102. https://ajindex.com/articles/1618289

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