AKILLI TELEFON VERİLERİ VE MAKİNE ÖĞRENMESİ YÖNTEMLERİ KULLANILARAK STRES TESPİTİ ÇALIŞMALARI ÜZERİNE BİR LİTERATÜR ARAŞTIRMASI

Year 2021, Volume: 9 Issue: 3, 1030 - 1038, 21.09.2021

Ensar Arif SAĞBAŞ Serdar KORUKOĞLU Serkan BALLI

https://doi.org/10.21923/jesd.790845

Abstract

Stres, genel olarak olumsuz etkilere sahip bir süreçtir. Bu olumsuz etkileri en aza indirmek için erken tespit edilmesi önemlidir. Buna bağlı olarak stresin tespit edilmesi bir sınıflandırma problemi olarak ele alınabilir. Stres, fizyolojik ve davranışsal veriler kullanılarak tespit edilebilmektedir. Bu çalışmada, sadece akıllı telefon verileri kullanılarak gerçekleştirilen stres tespiti çalışmaları ele alınmış ve stres tespitinde kullanılan veri kaynakları, veri türleri ve sınıflandırmada kullanılan makine öğrenmesi yöntemleri incelenmiştir. Bu çalışmalar kendi içerisinde veri kaynaklarına göre beş başlık altında incelenmiştir. Araştırma sonucunda akıllı telefon uygulamaları, hareket algılayıcıları, arama ve mesaj atma sıklığı gibi bilgilerin stresin tespitinde önemli bir yer tuttuğu görülmüştür.

Keywords

Stres Tespiti, Akıllı Telefon, Makine Öğrenmesi, Algılayıcı Verisi, Sağlık Bilişimi

A REVIEW OF LITERATURE ON STRESS DETECTION STUDIES USING SMARTPHONE DATA AND MACHINE LEARNING METHODS

Abstract

Stress is a process that generally has negative effects. It is important to be detected early to minimize these negative effects. Accordingly, the detection of stress can be considered as a classification problem. Stress can be detected using physiological and behavioral data. In this study, stress detection studies using only smartphone data were discussed and data sources, data types, and machine learning methods using classification were examined. These studies were handled under five headings according to their data sources. As a result of the research, it has been seen that information such as smartphone applications, motion sensors, frequency of calling and texting has an important place in the detection of stress.

Keywords

Stress Detection, Smartphone, Machine Learning, Sensor Data, Health Informatics.

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