Derinlik Sensörü Verilerine Dayalı Çok Sınıflı Oturma Duruşu Sınıflandırmasına Yönelik Otomatik Etiketleme Yaklaşımı

Year 2024, Volume: 15 Issue: 3, 559 - 568, 30.09.2024

Hüseyin Coşkun

https://doi.org/10.24012/dumf.1351801

Abstract

Bu çalışma, ofis çalışanlarının oturma duruşlarını tanımaya yönelik, sağlıklı oturma takibinde uygulanabilir, temassız bir sistem oluşturmayı amaçlamaktadır. Denekler, beş farklı oturma duruşunu sergilerken bir derinlik sensör tabanlı Kinect cihazı aracılığıyla iskelet noktası verileri elde edilmiştir. Bu postürleri farklılaştabilecek beş açı hesaplanmıştır. Verileri etiketlemek için açı değerlerini kullanan bulanık kural tabanlı otomatik bir yaklaşım önerilmiştir. Bu yöntem ile geleneksel zamana dayalı etiketleme yöntemleri kullanılarak iki farklı veri seti oluşturulmuştur. Derinlik değerlerini sınıflandırmak için açısal ve geometrik özellikler kullanılmış olup, KNN ve Adaboost sınıflandırıcıları ile %99,6 ve %98,9 doğruluk elde edilmiştir. Önerilen etiketleme yöntemi, sınıflandırma sonuçlarına göre geleneksel zamana dayalı etiketleme yönteminden daha iyi performans göstermiştir. Bu sistem, ofis çalışanlarında sağlıklı oturma alışkanlıklarını teşvik etmek için yüksek performanslı bir çözüm sunmaktadır ve sağlık izleme ve robot görüsü alanlarında uygulama alanlarına sahiptir.

Keywords

sınıflandırma, otomatik etiketleme, oturma postür, geometrik öznitelikler, derinlik sensörü

An Automatic Labeling Approach Towards Multi-class Sitting Posture Classification Based on Depth-Sensor Data

Abstract

This study aims to create a non-contact system for recognizing the sitting postures of office workers, applicable to healthy sitting monitoring. Skeletal point data were obtained via a depth sensor-based Kinect device while subjects performed five different sitting postures. Five angles have been calculated that can differentiate these postures. A fuzzy rule-based automated approach using angle values is proposed to label the data. With this method, two different data sets were created using traditional time-based labeling methods. Angular and geometric features were used to classify the depth values, and 99.6% and 98.9% accuracy were obtained with KNN and Adaboost classifiers. The proposed labeling method outperformed the traditional time-based labeling method according to the classification results. This system offers a high-performance solution for promoting healthy sitting habits in office workers and has applications in health monitoring and robot vision.

Keywords

classification, sitting posture, geometric features, automatic labeling, depth sensor

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