İnsan aktivite tanıması için yeni bir veri kümesi ve derin öğrenme modelleri ile sınıflandırılması

Yıl 2025, , 653 - 672, 16.08.2024

Yasin Vurgun , Mustafa Servet Kıran

https://doi.org/10.17341/gazimmfd.1325926

Öz

Mobil sensörler ile insan aktivite tanıma, giyilebilir ve mobil sensörlerin artması nedeniyle son yıllarda ilgi çekici bir araştırma alanı haline gelmiştir. Müslüman hayatında Namaz, müminlerin günde beş vakit kılmak zorunda oldukları bir aktivitedir. Bu çalışmada insan aktivitesi tanımada kullanılmak üzere namaz kılmayı da içeren yeni bir veri kümesi sunulmaktadır. HAR-P (Human Activity Recognition for Praying) adını verdiğimiz veri setinde yürüme, koşma, yazı yazma, merdiven inme, merdiven çıkma, oturma, ayakta durma ve namaz kılma gibi 8 aktivite için doğrusal hızlanma, ivme, manyetik alan ve jiroskop sensör verileri yer almaktadır. HAR-P veri seti için akıllı saat ile 15-60 yaş arası 50 erkek gönüllüden veri toplanmıştır. HAR-P veri kümesinde LSTM, ConvLSTM ve CNN-LSTM modellerinin sınıflandırma başarısı karşılaştırılmıştır. Ortalama en yüksek başarı oranı olan %91’e doğrusal hızlanma sensörü ile LSTM yöntemi ve ivme sensörü ile ConvLSTM modelinde ulaşılırken, en düşük ortalama başarı oranı olan %83,6’a jiroskop sensörü ve ConvLSTM yöntemi ile ulaşılmıştır.

Anahtar Kelimeler

insan aktivite tanıma, lstm, cnn-lstm, convlstm, akıllı saat sensör verisi

Destekleyen Kurum

Konya Teknik Üniversitesi Bilimsel Araştırma Projeleri Koordinatörlüğü

Proje Numarası

231113013

Teşekkür

Bu çalışma, Konya Teknik Üniversitesi Bilimsel Araştırma Projeleri Koordinatörlüğü tarafından 231113013 proje numarasıyla desteklenmiştir. Çalışmamızda veri toplama aşamasında katkısı olan gönüllülere teşekkür ederiz.

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