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

Year 2025, Volume: 40 Issue: 1, 653 - 672, 16.08.2024

Yasin Vurgun , Mustafa Servet Kıran

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

Abstract

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.

Keywords

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

Supporting Institution

Konya Teknik Üniversitesi Bilimsel Araştırma Projeleri Koordinatörlüğü

Project Number

231113013

Thanks

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

References

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  • 2. Pareek, P., Thakkar, A., A survey on video-based human action recognition: recent updates, datasets, challenges, and applications, Artificial Intelligence Review, 54, 2259-2322, 2021.
  • 3. Iskanderov, J., Güvensan, M.A., Akıllı telefon ve giyilebilir cihazlarla aktivite tanıma: Klasik yaklaşımlar, yeni çözümler, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 25 (2), 223-239, 2019.
  • 4. Kwapisz, J.R., Weiss, G.M., Moore, S.A., Activity recognition using cell phone accelerometers, ACM SigKDD Explorations Newsletter, 12 (2), 74-82, 2011.
  • 5. Balli, S., Sağbaş, E.A., Peker, M., Human activity recognition from smart watch sensor data using a hybrid of principal component analysis and random forest algorithm, Measurement Control, 52 (1-2), 37-45, 2019.
  • 6. Siirtola, P., Röning, J., Recognizing human activities user-independently on smartphones based on accelerometer data, IJIMAI, 1 (5), 38-45, 2012.
  • 7. Garain, A., Dawn, R., Singh, S., Chowdhury, C., Differentially private human activity recognition for smartphone users, Multimedia Tools Applications, 81 (28), 40827-40848, 2022.
  • 8. Sousa Lima, W., Souto, E., El-Khatib, K., Jalali, R., Gama, J., Human activity recognition using inertial sensors in a smartphone: An overview, Sensors, 19 (14), 3213, 2019.
  • 9. Mekruksavanich, S., Jitpattanakul, A., Lstm networks using smartphone data for sensor-based human activity recognition in smart homes, Sensors, 21 (5), 1636, 2021.
  • 10. Javed, A.R., Faheem, R., Asim, M., Baker, T., Beg, M.O., A smartphone sensors-based personalized human activity recognition system for sustainable smart cities, Sustainable Cities Society, 71, 102970, 2021.
  • 11. Mekruksavanich, S., Jitpattanakul, A., Smartwatch-based human activity recognition using hybrid lstm network, in 2020 IEEE SENSORS. 2020.
  • 12. Boyer, P., Burns, D., Whyne, C., Out-of-distribution detection of human activity recognition with smartwatch inertial sensors, Sensors, 21 (5), 1669, 2021.
  • 13. Albert, M.V., Toledo, S., Shapiro, M., Kording, K., Using mobile phones for activity recognition in Parkinson’s patients, Frontiers in neurology, 3, 158, 2012.
  • 14. Lee, J., Kim, J., Energy-efficient real-time human activity recognition on smart mobile devices, Mobile Information Systems, 2016.
  • 15. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L., Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine, Ambient Assisted Living and Home Care: 4th International Workshop, IWAAL 2012, Vitoria-Gasteiz, Spain, 216-223, 3-5 Aralık, 2012.
  • 16. Sztyler, T., Stuckenschmidt, H., Petrich, W.J.P., Position-aware activity recognition with wearable devices, Pervasive Mobile Computing, 38, 281-295, 2017.
  • 17. Metin İ.A., Karasulu B., A novel dataset of human daily activities: Its benchmarking results for classification performance via using deep learning techniques, Journal of the Faculty of Engineering and Architecture of Gazi University, 36 (2), 759-777, 2021.
  • 18. Borazio, M.,Van Laerhoven, K., Using time use with mobile sensor data: a road to practical mobile activity recognition?, Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia, 1-10, 2013.
  • 19. Lee, Y.-S., Cho, S.-B., Activity recognition using hierarchical hidden markov models on a smartphone with 3D accelerometer, Hybrid Artificial Intelligent Systems: 6th International Conference, HAIS 2011, Wroclaw, Poland, 23-25 Mayıs, 2011.
  • 20. Riboni, D., Bettini, C., COSAR: hybrid reasoning for context-aware activity recognition, Personal Ubiquitous Computing, 15 (3), 271-289, 2011.
  • 21. Zhao, Z., Chen, Y., Liu, J., Shen, Z., Liu, M., Cross-people mobile-phone based activity recognition, Twenty-second international joint conference on artificial intelligence, 2011.
  • 22. Peng, J.-X., Ferguson, S., Rafferty, K., Kelly, P.D., An efficient feature selection method for mobile devices with application to activity recognition, Neurocomputing, 74 (17), 3543-3552, 2011.
  • 23. Hsu, H.-H., Chu, C.-T., Zhou, Y., Cheng, Z., Two-phase activity recognition with smartphone sensors, 18th International Conference on Network-Based Information Systems, IEEE, 2015.
  • 24. Dernbach, S., Das, B., Krishnan, N.C., Thomas, B.L., Cook, D.J., Simple and complex activity recognition through smart phones, in 2012 eighth international conference on intelligent environments, IEEE, 2012.
  • 25. Ustev, Y.E., Durmaz Incel, O., Ersoy, C., User, device and orientation independent human activity recognition on mobile phones: Challenges and a proposal, Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication, 2013.
  • 26. Vo, Q.V., Hoang, M.T., Choi, D., Personalization in mobile activity recognition system using K-medoids clustering algorithm, International Journal of Distributed Sensor Networks, 9 (7), 315841, 2013.
  • 27. Chung, S., Lim, J., Noh, K.J., Kim, G., Jeong, H., Sensor data acquisition and multimodal sensor fusion for human activity recognition using deep learning, Sensors, 19 (7), 1716, 2019.
  • 28. Abdallah, Z.S., Gaber, M.M., Srinivasan, B., Krishnaswamy, S., Adaptive mobile activity recognition system with evolving data streams, Neurocomputing, 150, 304-317, 2015.
  • 29. Concepción, M.Á.Á., Morillo, L.M.S., García, J.A.Á., González-Abril, L., Mobile activity recognition and fall detection system for elderly people using Ameva algorithm, Pervasive Mobile Computing, 34, 3-13, 2017.
  • 30. Acharjee, D., Mukherjee, A., Mandal, J., Mukherjee, N., Activity recognition system using inbuilt sensors of smart mobile phone and minimizing feature vectors, Microsystem Technologies, 22 (11), 2715-2722, 2016.
  • 31. Henpraserttae, A., Thiemjarus, S., Marukatat, S., Accurate activity recognition using a mobile phone regardless of device orientation and location, International Conference on Body Sensor Networks, IEEE, 2011.
  • 32. Yan, Z., Subbaraju, V., Chakraborty, D., Misra, A., Aberer, K., Energy-efficient continuous activity recognition on mobile phones: An activity-adaptive approach, 16th international symposium on wearable computers, IEEE, 2012.
  • 33. Martín, H., Bernardos, A.M., Iglesias, J., Casar, J.R., Activity logging using lightweight classification techniques in mobile devices, Personal ubiquitous computing, 17 (4), 675-695, 2013.
  • 34. Kuncan F., Kaya Y., Kuncan M., New approaches based on local binary patterns for gender identification from sensor signals, Journal of the Faculty of Engineering and Architecture of Gazi University, 34 (4), 2173-2186, 2019.
  • 35. Yüksel, A.S., Şenel, F.A., Çankaya, İ.A., Yazma davranışlarının mobil cihaz sensörleri kullanılarak sınıflandırılması, Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 9 (1), 133-142, 2018.
  • 36. Huynh, Q.T., Nguyen, U.D., Irazabal, L.B., Ghassemian, N., Tran, B.Q., Optimization of an accelerometer and gyroscope-based fall detection algorithm, Journal of Sensors, 2015.
  • 37. Sağbaş, E.A., Ballı, S., Akıllı telefon algılayıcıları ve makine öğrenmesi kullanılarak ulaşım türü tespiti, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 22 (5), 376-383, 2016.
  • 38. Lockhart, J.W., Weiss, G.M., Xue, J.C., Gallagher, S.T., Grosner, A.B., Pulickal, T.T., Design considerations for the WISDM smart phone-based sensor mining architecture, Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data, 2011.
  • 39. Zappi, P., Lombriser, C., Stiefmeier, T., Farella, E., Roggen, D., Benini, L., Tröster, G., Activity recognition from on-body sensors: accuracy-power trade-off by dynamic sensor selection, Wireless Sensor Networks: 5th European Conference, EWSN 2008, Bologna - Italy, 30 Ocak-1 Şubat, 2008.
  • 40. Chavarriaga, R., Sagha, H., Calatroni, A., Digumarti, S.T., Tröster, G., Millán, J.d.R., Roggen, D., The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition, Pattern Recognition Letters, 34 (15), 2033-2042, 2013.
  • 41. Repcik, T. SensorBox: Android app to measure sensors. https://github.com/Foxpace/SensorBox. Yayın tarihi Eylül 21, 2021. Erişim tarihi Nisan 11, 2021.
  • 42. Google. Sensors Overview Android Developers. https://developer.android.com/guide/topics/sensors/sensors_overview. Erişim tarihi Mayıs 11, 2022.
  • 43. Van Der Maaten, L., Accelerating t-SNE using tree-based algorithms, The journal of machine learning research, 15 (1), 3221-3245, 2014.
  • 44. Community, s. sklearn.model_selection.StratifiedKFold scikit-learn 1.3.0 documentation. https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedKFold.html. Erişim tarihi Temmuz 3, 2023.
  • 45. Hochreiter, S., Schmidhuber, J., Long short-term memory, Neural computation, 9 (8), 1735-1780, 1997.
  • 46. Shi, X., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., Woo, W. C., Convolutional LSTM network: A machine learning approach for precipitation nowcasting, Advances in neural information processing systems, 28, 2015.

Year 2025, Volume: 40 Issue: 1, 653 - 672, 16.08.2024

Yasin Vurgun , Mustafa Servet Kıran

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

Abstract

Project Number

231113013

References

  • 1. Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., Liu, Y., Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities, ACM Computing Surveys, 54 (4), 1-40, 2021.
  • 2. Pareek, P., Thakkar, A., A survey on video-based human action recognition: recent updates, datasets, challenges, and applications, Artificial Intelligence Review, 54, 2259-2322, 2021.
  • 3. Iskanderov, J., Güvensan, M.A., Akıllı telefon ve giyilebilir cihazlarla aktivite tanıma: Klasik yaklaşımlar, yeni çözümler, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 25 (2), 223-239, 2019.
  • 4. Kwapisz, J.R., Weiss, G.M., Moore, S.A., Activity recognition using cell phone accelerometers, ACM SigKDD Explorations Newsletter, 12 (2), 74-82, 2011.
  • 5. Balli, S., Sağbaş, E.A., Peker, M., Human activity recognition from smart watch sensor data using a hybrid of principal component analysis and random forest algorithm, Measurement Control, 52 (1-2), 37-45, 2019.
  • 6. Siirtola, P., Röning, J., Recognizing human activities user-independently on smartphones based on accelerometer data, IJIMAI, 1 (5), 38-45, 2012.
  • 7. Garain, A., Dawn, R., Singh, S., Chowdhury, C., Differentially private human activity recognition for smartphone users, Multimedia Tools Applications, 81 (28), 40827-40848, 2022.
  • 8. Sousa Lima, W., Souto, E., El-Khatib, K., Jalali, R., Gama, J., Human activity recognition using inertial sensors in a smartphone: An overview, Sensors, 19 (14), 3213, 2019.
  • 9. Mekruksavanich, S., Jitpattanakul, A., Lstm networks using smartphone data for sensor-based human activity recognition in smart homes, Sensors, 21 (5), 1636, 2021.
  • 10. Javed, A.R., Faheem, R., Asim, M., Baker, T., Beg, M.O., A smartphone sensors-based personalized human activity recognition system for sustainable smart cities, Sustainable Cities Society, 71, 102970, 2021.
  • 11. Mekruksavanich, S., Jitpattanakul, A., Smartwatch-based human activity recognition using hybrid lstm network, in 2020 IEEE SENSORS. 2020.
  • 12. Boyer, P., Burns, D., Whyne, C., Out-of-distribution detection of human activity recognition with smartwatch inertial sensors, Sensors, 21 (5), 1669, 2021.
  • 13. Albert, M.V., Toledo, S., Shapiro, M., Kording, K., Using mobile phones for activity recognition in Parkinson’s patients, Frontiers in neurology, 3, 158, 2012.
  • 14. Lee, J., Kim, J., Energy-efficient real-time human activity recognition on smart mobile devices, Mobile Information Systems, 2016.
  • 15. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L., Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine, Ambient Assisted Living and Home Care: 4th International Workshop, IWAAL 2012, Vitoria-Gasteiz, Spain, 216-223, 3-5 Aralık, 2012.
  • 16. Sztyler, T., Stuckenschmidt, H., Petrich, W.J.P., Position-aware activity recognition with wearable devices, Pervasive Mobile Computing, 38, 281-295, 2017.
  • 17. Metin İ.A., Karasulu B., A novel dataset of human daily activities: Its benchmarking results for classification performance via using deep learning techniques, Journal of the Faculty of Engineering and Architecture of Gazi University, 36 (2), 759-777, 2021.
  • 18. Borazio, M.,Van Laerhoven, K., Using time use with mobile sensor data: a road to practical mobile activity recognition?, Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia, 1-10, 2013.
  • 19. Lee, Y.-S., Cho, S.-B., Activity recognition using hierarchical hidden markov models on a smartphone with 3D accelerometer, Hybrid Artificial Intelligent Systems: 6th International Conference, HAIS 2011, Wroclaw, Poland, 23-25 Mayıs, 2011.
  • 20. Riboni, D., Bettini, C., COSAR: hybrid reasoning for context-aware activity recognition, Personal Ubiquitous Computing, 15 (3), 271-289, 2011.
  • 21. Zhao, Z., Chen, Y., Liu, J., Shen, Z., Liu, M., Cross-people mobile-phone based activity recognition, Twenty-second international joint conference on artificial intelligence, 2011.
  • 22. Peng, J.-X., Ferguson, S., Rafferty, K., Kelly, P.D., An efficient feature selection method for mobile devices with application to activity recognition, Neurocomputing, 74 (17), 3543-3552, 2011.
  • 23. Hsu, H.-H., Chu, C.-T., Zhou, Y., Cheng, Z., Two-phase activity recognition with smartphone sensors, 18th International Conference on Network-Based Information Systems, IEEE, 2015.
  • 24. Dernbach, S., Das, B., Krishnan, N.C., Thomas, B.L., Cook, D.J., Simple and complex activity recognition through smart phones, in 2012 eighth international conference on intelligent environments, IEEE, 2012.
  • 25. Ustev, Y.E., Durmaz Incel, O., Ersoy, C., User, device and orientation independent human activity recognition on mobile phones: Challenges and a proposal, Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication, 2013.
  • 26. Vo, Q.V., Hoang, M.T., Choi, D., Personalization in mobile activity recognition system using K-medoids clustering algorithm, International Journal of Distributed Sensor Networks, 9 (7), 315841, 2013.
  • 27. Chung, S., Lim, J., Noh, K.J., Kim, G., Jeong, H., Sensor data acquisition and multimodal sensor fusion for human activity recognition using deep learning, Sensors, 19 (7), 1716, 2019.
  • 28. Abdallah, Z.S., Gaber, M.M., Srinivasan, B., Krishnaswamy, S., Adaptive mobile activity recognition system with evolving data streams, Neurocomputing, 150, 304-317, 2015.
  • 29. Concepción, M.Á.Á., Morillo, L.M.S., García, J.A.Á., González-Abril, L., Mobile activity recognition and fall detection system for elderly people using Ameva algorithm, Pervasive Mobile Computing, 34, 3-13, 2017.
  • 30. Acharjee, D., Mukherjee, A., Mandal, J., Mukherjee, N., Activity recognition system using inbuilt sensors of smart mobile phone and minimizing feature vectors, Microsystem Technologies, 22 (11), 2715-2722, 2016.
  • 31. Henpraserttae, A., Thiemjarus, S., Marukatat, S., Accurate activity recognition using a mobile phone regardless of device orientation and location, International Conference on Body Sensor Networks, IEEE, 2011.
  • 32. Yan, Z., Subbaraju, V., Chakraborty, D., Misra, A., Aberer, K., Energy-efficient continuous activity recognition on mobile phones: An activity-adaptive approach, 16th international symposium on wearable computers, IEEE, 2012.
  • 33. Martín, H., Bernardos, A.M., Iglesias, J., Casar, J.R., Activity logging using lightweight classification techniques in mobile devices, Personal ubiquitous computing, 17 (4), 675-695, 2013.
  • 34. Kuncan F., Kaya Y., Kuncan M., New approaches based on local binary patterns for gender identification from sensor signals, Journal of the Faculty of Engineering and Architecture of Gazi University, 34 (4), 2173-2186, 2019.
  • 35. Yüksel, A.S., Şenel, F.A., Çankaya, İ.A., Yazma davranışlarının mobil cihaz sensörleri kullanılarak sınıflandırılması, Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 9 (1), 133-142, 2018.
  • 36. Huynh, Q.T., Nguyen, U.D., Irazabal, L.B., Ghassemian, N., Tran, B.Q., Optimization of an accelerometer and gyroscope-based fall detection algorithm, Journal of Sensors, 2015.
  • 37. Sağbaş, E.A., Ballı, S., Akıllı telefon algılayıcıları ve makine öğrenmesi kullanılarak ulaşım türü tespiti, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 22 (5), 376-383, 2016.
  • 38. Lockhart, J.W., Weiss, G.M., Xue, J.C., Gallagher, S.T., Grosner, A.B., Pulickal, T.T., Design considerations for the WISDM smart phone-based sensor mining architecture, Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data, 2011.
  • 39. Zappi, P., Lombriser, C., Stiefmeier, T., Farella, E., Roggen, D., Benini, L., Tröster, G., Activity recognition from on-body sensors: accuracy-power trade-off by dynamic sensor selection, Wireless Sensor Networks: 5th European Conference, EWSN 2008, Bologna - Italy, 30 Ocak-1 Şubat, 2008.
  • 40. Chavarriaga, R., Sagha, H., Calatroni, A., Digumarti, S.T., Tröster, G., Millán, J.d.R., Roggen, D., The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition, Pattern Recognition Letters, 34 (15), 2033-2042, 2013.
  • 41. Repcik, T. SensorBox: Android app to measure sensors. https://github.com/Foxpace/SensorBox. Yayın tarihi Eylül 21, 2021. Erişim tarihi Nisan 11, 2021.
  • 42. Google. Sensors Overview Android Developers. https://developer.android.com/guide/topics/sensors/sensors_overview. Erişim tarihi Mayıs 11, 2022.
  • 43. Van Der Maaten, L., Accelerating t-SNE using tree-based algorithms, The journal of machine learning research, 15 (1), 3221-3245, 2014.
  • 44. Community, s. sklearn.model_selection.StratifiedKFold scikit-learn 1.3.0 documentation. https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedKFold.html. Erişim tarihi Temmuz 3, 2023.
  • 45. Hochreiter, S., Schmidhuber, J., Long short-term memory, Neural computation, 9 (8), 1735-1780, 1997.
  • 46. Shi, X., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., Woo, W. C., Convolutional LSTM network: A machine learning approach for precipitation nowcasting, Advances in neural information processing systems, 28, 2015.
There are 46 citations in total.

Details

Primary Language Turkish
Subjects Deep Learning
Journal Section Makaleler
Authors

Yasin Vurgun 0000-0003-1578-0604

Mustafa Servet Kıran 0000-0002-5896-7180

Project Number 231113013
Early Pub Date August 1, 2024
Publication Date August 16, 2024
Submission Date July 11, 2023
Acceptance Date May 23, 2024
Published in Issue Year 2025 Volume: 40 Issue: 1

Cite

APA Vurgun, Y., & Kıran, M. S. (2024). İnsan aktivite tanıması için yeni bir veri kümesi ve derin öğrenme modelleri ile sınıflandırılması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 40(1), 653-672. https://doi.org/10.17341/gazimmfd.1325926
AMA Vurgun Y, Kıran MS. İnsan aktivite tanıması için yeni bir veri kümesi ve derin öğrenme modelleri ile sınıflandırılması. GUMMFD. August 2024;40(1):653-672. doi:10.17341/gazimmfd.1325926
Chicago Vurgun, Yasin, and Mustafa Servet Kıran. “İnsan Aktivite tanıması için Yeni Bir Veri kümesi Ve Derin öğrenme Modelleri Ile sınıflandırılması”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 40, no. 1 (August 2024): 653-72. https://doi.org/10.17341/gazimmfd.1325926.
EndNote Vurgun Y, Kıran MS (August 1, 2024) İnsan aktivite tanıması için yeni bir veri kümesi ve derin öğrenme modelleri ile sınıflandırılması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 40 1 653–672.
IEEE Y. Vurgun and M. S. Kıran, “İnsan aktivite tanıması için yeni bir veri kümesi ve derin öğrenme modelleri ile sınıflandırılması”, GUMMFD, vol. 40, no. 1, pp. 653–672, 2024, doi: 10.17341/gazimmfd.1325926.
ISNAD Vurgun, Yasin - Kıran, Mustafa Servet. “İnsan Aktivite tanıması için Yeni Bir Veri kümesi Ve Derin öğrenme Modelleri Ile sınıflandırılması”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 40/1 (August 2024), 653-672. https://doi.org/10.17341/gazimmfd.1325926.
JAMA Vurgun Y, Kıran MS. İnsan aktivite tanıması için yeni bir veri kümesi ve derin öğrenme modelleri ile sınıflandırılması. GUMMFD. 2024;40:653–672.
MLA Vurgun, Yasin and Mustafa Servet Kıran. “İnsan Aktivite tanıması için Yeni Bir Veri kümesi Ve Derin öğrenme Modelleri Ile sınıflandırılması”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 40, no. 1, 2024, pp. 653-72, doi:10.17341/gazimmfd.1325926.
Vancouver Vurgun Y, Kıran MS. İnsan aktivite tanıması için yeni bir veri kümesi ve derin öğrenme modelleri ile sınıflandırılması. GUMMFD. 2024;40(1):653-72.