Islak Alanlarda Mahremiyet Koruyucu Düşme Tespiti: Düşük Çözünürlüklü Termal Diziler ve Uyarlanabilir Eşikleme Kullanan IoT Entegreli Bir Sistem

Abstract

Banyo ve tuvalet gibi ıslak zeminlerde meydana gelen düşme kazaları, özellikle yaşlı ve engelli bireyler için ciddi yaralanmalara ve hatta can kayıplarına yol açan önemli bir halk sağlığı sorunudur. Bu alanlarda mahremiyet kaygıları nedeniyle geleneksel optik kameraların kullanımı mümkün olmamaktadır. Bu çalışmada, mahremiyeti ihlal etmeden banyo ortamındaki düşme eylemlerini tespit eden, Nesnelerin İnterneti entegreli düşük maliyetli bir sistem geliştirilmiştir. Geliştirilen sistem; Raspberry Pi uç birimi, düşük çözünürlüklü termal kamera modülü ve sesli uyarı bileşenlerinden oluşmaktadır. Yazılım mimarisinde ise Python tabanlı görüntü işleme algoritmaları ve kullanıcı etkileşimi için Flutter tabanlı bir mobil uygulama kullanılmıştır. Önerilen algoritma, bireyin vücut yüzey alanı ve ısı haritası yoğunluğunu analiz eder; zemindeki sıcak piksel yoğunluğunun belirlenen kritik eşik değerini (%40) aşması durumunda eylemi "düşme" olarak sınıflandırır. Tespit edilen acil durumlar, MQTT protokolü aracılığıyla anlık olarak mobil uygulamaya iletilerek bakıcı veya sağlık ekiplerinin en kısa sürede müdahale etmesi sağlanır. Deneysel sonuçlar, sistemin ayakta durma, eğilme ve düşme eylemlerini başarıyla ayırt edebildiğini ve mahremiyet koruyucu bir can yeleği işlevi görebileceğini doğrulamaktadır.

Keywords

• Islak Zemin
• IoT
• Termal Görüntüleme
• Görüntü İşleme

Privacy-Preserving Fall Detection in Wet Areas: An IoT-Integrated System Using Low-Resolution Thermal Arrays and Adaptive Thresholding

Abstract

Falls occurring on wet surfaces such as bathrooms and toilets are a significant public health issue, particularly for elderly and disabled individuals, leading to serious injuries and even fatalities. The use of traditional optical cameras in these areas is not feasible due to privacy concerns. In this study, a low-cost system integrated with the Internet of Things (IoT) has been developed that detects falls in the bathroom environment without violating privacy. The developed system consists of a Raspberry Pi edge device, a low-resolution thermal camera module, and an audio alert component. The software architecture utilizes Python-based image processing algorithms and a Flutter-based mobile application for user interaction. The proposed algorithm analyzes the individual's body surface area and heat map intensity; it classifies the action as a “fall” when the density of hot pixels on the floor exceeds a specified critical threshold value (40%). Detected emergencies are instantly communicated to the mobile application via the MQTT protocol, ensuring that caregivers or medical teams can intervene as quickly as possible. Experimental results confirm that the system can successfully distinguish between standing, bending, and falling actions and can function as a privacy-preserving life jacket.

Keywords

• Wet Floor
• IoT
• Thermal Imaging
• Image Processing

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