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

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Bibliographic Details
Title:	İnsan aktivite tanıması için yeni bir veri kümesi ve derin öğrenme modelleri ile sınıflandırılması.
Alternate Title:	A new dataset for human activity recognition and its classification with deep learning models.
Authors:	Vurgun, Yasin¹ yasin_vurgun@hotmail.com, Kıran, Mustafa Servet² mskiran@ktun.edu.tr
Source:	Journal of the Faculty of Engineering & Architecture of Gazi University / Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi,. 2025, Vol. 40 Issue 1, p653-671. 19p.
Subjects:	Linear acceleration, Acceleration (Mechanics), Wearable technology, Human activity recognition, Magnetic fields, Detectors, Gyroscopes, Smartwatches
Abstract (English):	In recent years, the use of mobile sensors for human activity recognition has become an intriguing research area due to the proliferation of wearable and mobile sensors. In Muslim life, the prayer (Salah) is an activity that believers are obligated to perform five times a day. In this study, a new dataset, including Salah, is presented for use in human activity recognition. Named HAR-P (Human Activity Recognition for Praying), the dataset comprises linear acceleration, acceleration, magnetic field, and gyroscope sensor data for eight activities: walking, running, typing, downstairs, upstairs, sitting, standing, and praying. Data were collected from 50 male volunteers aged 15-60 using a smartwatch for the HAR-P dataset. The classification performance of LSTM, ConvLSTM, and CNN-LSTM models was compared for the HAR-P dataset. The highest average classification accuracy of 91% was achieved with the LSTM method using linear acceleration sensor data and the ConvLSTM model using acceleration sensor data, while the lowest average accuracy of 83.6% was attained with the gyroscope sensor data and the ConvLSTM method. [ABSTRACT FROM AUTHOR]
Abstract (Turkish):	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 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 CNNLSTM 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. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	In recent years, the use of mobile sensors for human activity recognition has become an intriguing research area due to the proliferation of wearable and mobile sensors. In Muslim life, the prayer (Salah) is an activity that believers are obligated to perform five times a day. In this study, a new dataset, including Salah, is presented for use in human activity recognition. Named HAR-P (Human Activity Recognition for Praying), the dataset comprises linear acceleration, acceleration, magnetic field, and gyroscope sensor data for eight activities: walking, running, typing, downstairs, upstairs, sitting, standing, and praying. Data were collected from 50 male volunteers aged 15-60 using a smartwatch for the HAR-P dataset. The classification performance of LSTM, ConvLSTM, and CNN-LSTM models was compared for the HAR-P dataset. The highest average classification accuracy of 91% was achieved with the LSTM method using linear acceleration sensor data and the ConvLSTM model using acceleration sensor data, while the lowest average accuracy of 83.6% was attained with the gyroscope sensor data and the ConvLSTM method. [ABSTRACT FROM AUTHOR]
ISSN:	13001884
DOI:	10.17341/gazimmfd.1325926