Rastgele Orman Sınıflandırıcı Kullanılarak Kumaş Bileşimi ve Destek Malzemesinin Tekstil Basınç Sensörleri Üzerindeki Etkilerinin Karşılaştırmalı Analizi

Öz

Tekstil tabanlı sensörler, esneklikleri, konforları ve giysilere kolay entegrasyonları nedeniyle giyilebilir sağlık izleme sistemleri için umut vadeden adaylardır. Bununla birlikte, performansları üretim sürecinde kullanılan kumaş ve destek malzemesi türlerinden önemli ölçüde etkilenebilir. Bu çalışma, makine öğrenmesi modelleri kullanarak farklı kumaş bileşimlerinin ve destek malzemelerinin etkilerini araştırmaktadır. Dört farklı kumaş türü (%100 pamuk, %94 pamuk-%6 spandeks, %100 naylon ve %82 naylon-%18 spandeks), her biri dört farklı destek malzemesi türüyle (yırtılabilir, kesilebilir, yıkanabilir ve desteksiz) işlenerek toplam on altı sensör elde edilmiştir. On altı sensörün her biri, 0 oz, 2 oz ve 4 oz yüklerin tekrar tekrar uygulanıp bırakıldığı (~1 saniyelik temas) bir doğrusallık testine tabi tutulmuştur (sensör başına 15 dakika, her yük için 5 dakikalık sürekli bir süre boyunca). Daha sonra, her sensör için ham sinyalden basınç seviyesi ve istatistiksel özellikler çıkarılmış ve bu özelliklerle bir Rastgele Orman sınıflandırıcı eğitilmiştir. Sonuçlar, makine öğrenimi modelinin farklı kumaş türleri ve destek malzemeleri arasında doğru bir şekilde ayrım yapabildiğini göstermiştir (doğruluk oranları sırasıyla %91,8 ve %95,7). Bu bulgular, makine öğrenimi modellerinin sensör karakterizasyonu için bir araç olarak kullanılabileceğini vurgulamaktadır.

Anahtar Kelimeler

• sınıflandırma
• textil basınç sensörleri
• makine öğrenmesi
• akıllı tekstiller

Comparative Analysis of Fabric Composition and Backing Material Effects on Textile Pressure Sensors Using Random Forest Classifier

Öz

Textile-based sensors are promising candidates for wearable health monitoring systems due to their flexibility, comfort, and ease of integration into garments. However, their performance can be significantly influenced by the types of fabric and backing material used during fabrication process. This study investigates the effects of different fabric compositions and backing materials using machine learning models. Four fabric types (100% cotton, 94% cotton–6% spandex, 100% nylon, and 82% nylon–18% spandex) were each embroidered with four types of backing materials (tear-away, cut-away, wash-away, and no backing), resulting in a total of sixteen sensors. Each of the sixteen sensors underwent a linearity test in which 0 oz, 2 oz, and 4 oz loads were repeatedly applied and released (~1-second contact) over a continuous 5-minute period for each load (15 minutes per sensor). Then the pressure level and statistical features were extracted from the raw signal for each sensor, and a Random Forest classifier were trained with these features. The results showed that the Random Forest model successfully differentiated signal windows originating from different textile conditions (accuracy: 91.8% and 95.7% respectively). These findings highlight that machine learning models could be used as a tool for sensor characterization by uncovering the hidden relationships within the sensor signals.

Anahtar Kelimeler

• classification
• embroidered textile sensors
• machine learning
• smart textiles

Teşekkür

The author acknowledges the support of Wearable Biosensing Lab at the University of Rhode Island for textile sensor design and fabrication.

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