Deep Learning Based Emotion Analysis Model from Real-Time Body Movements

Mustafa Yağcı; Muhammed Erkam Aygül

Makale detayları

Deep Learning Based Emotion Analysis Model from Real-Time Body Movements

Yazarlar:

Mustafa Yağcı | Muhammed Erkam Aygül

Yayıncı:

Gazi University

Ana Başlıklar:

Bilgi Sistemleri (Diğer)

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.12 Issue 2, pp.664-674

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001321570

pISSN:

2147-9526

Anahtar kelimeler:

Özet:

 Communication is a tool used to transfer information, feelings and thoughts between living things. Research shows that approximately 80 of human communication is done through body language or gestures. Interpreting a persons body language can enable inferences from that persons emotions. The aim of this study is to develop a new machine learning model that predicts an individuals emotional states by analyzing human body movements in real time with deep learning techniques. A model that recognizes peoples bodies and collects data from that pose by fitting a skeleton model to the recognized body pose has been developed, and different models have been trained with the collected data set. In addition, a prediction model has been developed for real-time analysis. In this way, emotions can be inferred from peoples body language and facial expressions. It was observed that the accuracy rate of the results of the different test models was 92.86 at the lowest, 97.80 at the highest, and the accuracy rate of the real-time analysis results was 94.50. The results of this study can be used in the fields of better human-machine interaction, emotional intelligence applications and social interaction.

 Communication is a tool used to transfer information, feelings and thoughts between living things. Research shows that approximately 80 of human communication is done through body language or gestures. Interpreting a persons body language can enable inferences from that persons emotions. The aim of this study is to develop a new machine learning model that predicts an individuals emotional states by analyzing human body movements in real time with deep learning techniques. A model that recognizes peoples bodies and collects data from that pose by fitting a skeleton model to the recognized body pose has been developed, and different models have been trained with the collected data set. In addition, a prediction model has been developed for real-time analysis. In this way, emotions can be inferred from peoples body language and facial expressions. It was observed that the accuracy rate of the results of the different test models was 92.86 at the lowest, 97.80 at the highest, and the accuracy rate of the real-time analysis results was 94.50. The results of this study can be used in the fields of better human-machine interaction, emotional intelligence applications and social interaction.

 Communication is a tool used to transfer information, feelings and thoughts between living things. Research shows that approximately 80 of human communication is done through body language or gestures. Interpreting a persons body language can enable inferences from that persons emotions. The aim of this study is to develop a new machine learning model that predicts an individuals emotional states by analyzing human body movements in real time with deep learning techniques. A model that recognizes peoples bodies and collects data from that pose by fitting a skeleton model to the recognized body pose has been developed, and different models have been trained with the collected data set. In addition, a prediction model has been developed for real-time analysis. In this way, emotions can be inferred from peoples body language and facial expressions. It was observed that the accuracy rate of the results of the different test models was 92.86 at the lowest, 97.80 at the highest, and the accuracy rate of the real-time analysis results was 94.50. The results of this study can be used in the fields of better human-machine interaction, emotional intelligence applications and social interaction.

 İletişim, canlılar arasında bilgi, duygu ve düşüncelerin aktarılması için kullanılan bir araçtır. Araştırmalar, insan iletişiminin yaklaşık 80inin beden dili veya mimiklerle yapıldığını göstermektedir. Bir kişinin beden dilini yorumlamak ise o kişinin duygularından çıkarım yapılmasını sağlayabilir. Bu çalışmanın amacı, gerçek zamanlı olarak insan vücut hareketlerini derin öğrenme teknikleri ile analiz ederek bireyin duygusal durumlarını tahmin eden yeni bir makine öğrenmesi modeli geliştirmektir. İnsanların vücudunu tanıyan ve tanınan vücut pozuna iskelet modelin oturtulması işlemi ile o pozdan veri toplayan model geliştirilmiş, toplanan veri seti ile farklı modeller eğitilmiştir. Bununla birlikte gerçek zamanlı analiz yapılması için bir tahmin modeli geliştirilmiştir. Bu sayede insanların beden dili ve mimiklerinden duygu çıkarımı yapılabilmektedir. Yapılan farklı test modellerinin sonuçlarının doğruluk oranının en düşük 92.86, en yüksek 97.80 olduğu ve gerçek zamanlı analiz sonuçlarının doğruluk oranının 94.50 olduğu görülmüştür. Bu çalışmanın sonuçları daha iyi insan-makine etkileşimi, duygusal zekâ uygulamaları ve sosyal etkileşim alanlarında kullanılabilir.

Makale detayları

Deep Learning Based Emotion Analysis Model from Real-Time Body Movements

Yazarlar:

Mustafa Yağcı | Muhammed Erkam Aygül

Yayıncı:

Gazi University

Ana Başlıklar:

Bilgi Sistemleri (Diğer)

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.12 Issue 2, pp.664-674

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001321570

pISSN:

2147-9526

Image processing

machine learning

artificial intelligence

emotion analysis

feature extraction.Görüntü işleme

makine öğrenmesi

yapay zekâ

duygu analizi

nitelik çıkarımı

Sayı

Makale detayları

Yazarlar: Mustafa Yağcı | Muhammed Erkam Aygül

Yayıncı: Gazi University

Ana Başlıklar: Bilgi Sistemleri (Diğer)

Dergi: Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.12 Issue 2, pp.664-674

Belge Tipi: Makale

Yayın Tarihi: 6/2024

Belge Dili: Türkçe

Document ID (Org No.): 2001321570

pISSN: 2147-9526

Sayı

Yazarlar:

Mustafa Yağcı | Muhammed Erkam Aygül

Yayıncı:

Gazi University

Ana Başlıklar:

Bilgi Sistemleri (Diğer)

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.12 Issue 2, pp.664-674

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001321570

pISSN:

2147-9526