Fall Detection and Prevention Systems: Sensor Type Perspective

Abstract

Falls among older adults pose significant health risks, making their prevention and detection critical areas of research. This review examines fall detection and prevention systems, categorizing them based on sensor types and utilization methods: wearable sensors, environmental sensors, radio-frequency-based sensors, and hybrid systems. Additionally, it explores the methods employed within these systems. Given the limitations of traditional linear approaches in accurately detecting falls, recent research emphasizes artificial intelligence (AI) techniques, particularly machine learning (ML) and deep learning (DL), to enhance detection accuracy and system functionality. The review provides an overview of the sensors and algorithms used in fall detection and prevention systems, alongside their outcomes. Key findings and challenges related to specific sensors and systems are discussed in detail. This analysis offers researchers a comprehensive understanding of current technologies, highlights the contributions of AI methods, and outlines potential future directions in the field. By evaluating sensors, methodologies, and system sensitivities, the aim is to contribute to the development of effective solutions tailored to specific sensitivities.

Keywords

• fall sensing
• fall prevention
• machine learning
• deep learning
• fall sensor types

Sensör Tipleri Perspektifinden: Düşme Algılama ve Önleme Sistemleri

Abstract

Yaşlı bireylerde düşmeler, önemli sağlık riskleri oluşturmakta ve bu durum, önleme ve tespit çalışmalarını kritik bir araştırma alanı haline getirmektedir. Bu derleme, düşme tespit ve önleme sistemlerini kullanılan sensör türleri ve yöntemlerine göre sınıflandırarak incelemektedir: giyilebilir sensörler, çevresel sensörler, radyo frekansı tabanlı sensörler ve hibrit sistemler. Ayrıca, bu sistemlerde kullanılan yöntemler ele alınmaktadır. Geleneksel doğrusal yaklaşımların düşme olaylarını doğru bir şekilde tespit etmedeki sınırlamaları göz önüne alındığında, son yıllarda makine öğrenmesi (ML) ve derin öğrenme (DL) gibi yapay zeka (YZ) teknikleri üzerine yapılan araştırmalar ön plana çıkmaktadır. Bu derleme, düşme tespit ve önleme sistemlerinde kullanılan sensörler ve algoritmalar ile bunların sonuçlarına dair kapsamlı bir bilgi sunmaktadır. Belirli sensörler ve sistemlerle ilgili temel bulgular ve zorluklar detaylı bir şekilde tartışılmaktadır. Çalışma, mevcut teknolojiler hakkında araştırmacılara geniş bir bakış açısı kazandırmayı, YZ yöntemlerinin katkılarını vurgulamayı ve alanın gelecekteki yönelimlerini ortaya koymayı hedeflemektedir. Sensörler, metodolojiler ve sistem duyarlılıkları değerlendirilerek, etkili ve hassasiyetlere uygun çözümlerin geliştirilmesine katkı sağlanması amaçlanmaktadır.

Keywords

• düşme algılama
• düşme önleme
• makine öğrenmesi
• derin öğrenme
• düşme sensör tipleri

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