İnsan aktivite tanıması için yeni bir veri kümesi ve derin öğrenme modelleri ile sınıflandırılması.
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| Title: | İnsan aktivite tanıması için yeni bir veri kümesi ve derin öğrenme modelleri ile sınıflandırılması. |
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| 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] |
| Copyright of Journal of the Faculty of Engineering & Architecture of Gazi University / Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, is the property of Gazi University, Faculty of Engineering & Architecture and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 179246248 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: İnsan aktivite tanıması için yeni bir veri kümesi ve derin öğrenme modelleri ile sınıflandırılması. – Name: TitleAlt Label: Alternate Title Group: TiAlt Data: A new dataset for human activity recognition and its classification with deep learning models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Vurgun%2C+Yasin%22">Vurgun, Yasin</searchLink><relatesTo>1</relatesTo><i> yasin_vurgun@hotmail.com</i><br /><searchLink fieldCode="AR" term="%22Kıran%2C+Mustafa+Servet%22">Kıran, Mustafa Servet</searchLink><relatesTo>2</relatesTo><i> mskiran@ktun.edu.tr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+the+Faculty+of+Engineering+%26+Architecture+of+Gazi+University+%2F+Gazi+Üniversitesi+Mühendislik+Mimarlık+Fakültesi+Dergisi%2C%22">Journal of the Faculty of Engineering & Architecture of Gazi University / Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi,</searchLink>. 2025, Vol. 40 Issue 1, p653-671. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Linear+acceleration%22">Linear acceleration</searchLink><br /><searchLink fieldCode="DE" term="%22Acceleration+%28Mechanics%29%22">Acceleration (Mechanics)</searchLink><br /><searchLink fieldCode="DE" term="%22Wearable+technology%22">Wearable technology</searchLink><br /><searchLink fieldCode="DE" term="%22Human+activity+recognition%22">Human activity recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+fields%22">Magnetic fields</searchLink><br /><searchLink fieldCode="DE" term="%22Detectors%22">Detectors</searchLink><br /><searchLink fieldCode="DE" term="%22Gyroscopes%22">Gyroscopes</searchLink><br /><searchLink fieldCode="DE" term="%22Smartwatches%22">Smartwatches</searchLink> – Name: Abstract Label: Abstract (English) Group: Ab Data: 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] – Name: Abstract Label: Abstract (Turkish) Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of the Faculty of Engineering & Architecture of Gazi University / Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, is the property of Gazi University, Faculty of Engineering & Architecture and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.17341/gazimmfd.1325926 Languages: – Code: tur Text: Turkish PhysicalDescription: Pagination: PageCount: 19 StartPage: 653 Subjects: – SubjectFull: Linear acceleration Type: general – SubjectFull: Acceleration (Mechanics) Type: general – SubjectFull: Wearable technology Type: general – SubjectFull: Human activity recognition Type: general – SubjectFull: Magnetic fields Type: general – SubjectFull: Detectors Type: general – SubjectFull: Gyroscopes Type: general – SubjectFull: Smartwatches Type: general Titles: – TitleFull: İnsan aktivite tanıması için yeni bir veri kümesi ve derin öğrenme modelleri ile sınıflandırılması. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Vurgun, Yasin – PersonEntity: Name: NameFull: Kıran, Mustafa Servet IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13001884 Numbering: – Type: volume Value: 40 – Type: issue Value: 1 Titles: – TitleFull: Journal of the Faculty of Engineering & Architecture of Gazi University / Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, Type: main |
| ResultId | 1 |
