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Derinlik Sensörü Verilerine Dayalı Çok Sınıflı Oturma Duruşu Sınıflandırmasına Yönelik Otomatik Etiketleme Yaklaşımı

Yıl 2024, Cilt: 15 Sayı: 3, 559 - 568
https://doi.org/10.24012/dumf.1351801

Öz

Bu çalışma, ofis çalışanlarının oturma duruşlarını tanımaya yönelik, sağlıklı oturma takibinde uygulanabilir, temassız bir sistem oluşturmayı amaçlamaktadır. Denekler, beş farklı oturma duruşunu sergilerken bir derinlik sensör tabanlı Kinect cihazı aracılığıyla iskelet noktası verileri elde edilmiştir. Bu postürleri farklılaştabilecek beş açı hesaplanmıştır. Verileri etiketlemek için açı değerlerini kullanan bulanık kural tabanlı otomatik bir yaklaşım önerilmiştir. Bu yöntem ile geleneksel zamana dayalı etiketleme yöntemleri kullanılarak iki farklı veri seti oluşturulmuştur. Derinlik değerlerini sınıflandırmak için açısal ve geometrik özellikler kullanılmış olup, KNN ve Adaboost sınıflandırıcıları ile %99,6 ve %98,9 doğruluk elde edilmiştir. Önerilen etiketleme yöntemi, sınıflandırma sonuçlarına göre geleneksel zamana dayalı etiketleme yönteminden daha iyi performans göstermiştir. Bu sistem, ofis çalışanlarında sağlıklı oturma alışkanlıklarını teşvik etmek için yüksek performanslı bir çözüm sunmaktadır ve sağlık izleme ve robot görüsü alanlarında uygulama alanlarına sahiptir.

Kaynakça

  • [1] "Health and safety statistics." https://www.hse.gov.uk/statistics/ (accessed Mar. 12, 2023).
  • [2] T. Borhany, E. Shahid, W. A. Siddique, and H. Ali, "Musculoskeletal problems in frequent computer and internet users," J Family Med Prim Care, vol. 7, no. 2, p. 337, 2018, doi: 10.4103/JFMPC.JFMPC_326_17.
  • [3] S. J. Ray and J. Teizer, "Real-time construction worker posture analysis for ergonomics training," Advanced Engineering Informatics, vol. 26, no. 2, pp. 439–455, Apr. 2012, doi: 10.1016/J.AEI.2012.02.011.
  • [4] O. Patsadu, C. Nukoolkit, and B. Watanapa, "Human gesture recognition using Kinect camera," JCSSE 2012 - 9th International Joint Conference on Computer Science and Software Engineering, pp. 28–32, 2012, doi: 10.1109/JCSSE.2012.6261920.
  • [5] L. Xia, C. C. Chen, and J. K. Aggarwal, "View invariant human action recognition using histograms of 3D joints," IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 20–27, 2012, doi: 10.1109/CVPRW.2012.6239233.
  • [6] P. Paliyawan, C. Nukoolkit, and P. Mongkolnam, "Prolonged sitting detection for office workers syndrome prevention using Kinect," 2014 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications, and Information Technology, ECTI-CON 2014, 2014, doi: 10.1109/ECTICON.2014.6839785.
  • [7] M. Pal, S. Saha, and A. Konar, "Distance matching based gesture recognition for healthcare using Microsoft's Kinect sensor," International Conference on Microelectronics, Computing and Communication, MicroCom 2016, Jul. 2016, doi: 10.1109/MICROCOM.2016.7522586.
  • [8] L. Yao, W. Min, and H. Cui, "A new Kinect approach to judge unhealthy sitting posture based on neck angle and torso angle," Lecture Notes in Computer Science, vol. 10666 LNCS, pp. 340– 350, 2017, doi: 10.1007/978-3-319-71607- 7_30/TABLES/1.
  • [9] B. Li, B. Bai, C. Han, H. Long, and L. Zhao, "Novel hybrid method for human posture recognition based on Kinect V2," Communications in Computer and Information Science, vol. 771, pp. 331–342, 2017, doi: 10.1007/978-981-10- 7299-4_27/TABLES/5.
  • [10] S. Bei, Z. Xing, L. Taocheng, and L. Qin, "Sitting posture detection using adaptively fused 3D features," Proceedings of the 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2017, vol. 2018-January, pp. 1073–1077, Feb. 2018, doi: 10.1109/ITNEC.2017.8284904.
  • [11] N. J. Delleman and J. Dul, "International standards on working postures and movements ISO 11226 and EN 1005-4," https://doi.org/10.1080/00140130701674430, vol. 50, no. 11, pp. 1809–1819, Nov. 2007, doi: 10.1080/00140130701674430.
  • [12] J. Kelly, "Proper Height For Standing Desks," 2014. https://notsitting.com/proper-height/
  • [13] J. Elliott, "How long should I stand at my standing desk? - HealthPostures," 2020. https://healthpostures.com/how-long-should-istand-at-my-standing-desk/ (accessed Jun. 23, 2022).
  • [14] Fondazione Ergo, "Ergonomic Assessment Worksheet," 2021. [Online]. Available: https://www.eaws.it/
  • [15] Canadian Centre for Occupational Health & Safety, "Working in a Sitting Position -Good Body Position," 2022.
  • [16] ISO, "ISO 7250-1:2017," Basic human body measurements for technological design — Part 1: Body measurement definitions and landmarks, 2017. https://www.iso.org/standard/65246.html (accessed Dec. 24, 2022).
  • [17] CCOHS, "Working in a Sitting Position - Good Body Position," 2022. https://www.ccohs.ca/oshanswers/ergonomics/sitt ing/sitting_position.html (accessed Aug. 06, 2023).
  • [18] Y.-K. Kong, S. Lee, K.-S. Lee, and D.-M. Kim, "Comparisons of ergonomic evaluation tools (ALLA, RULA, REBA and OWAS) for farm work," International journal of occupational safety and ergonomics, vol. 24, no. 2, pp. 218–223, 2018.
  • [19] J. Vince, "Mathematics for Computer Graphics," 2022, doi: 10.1007/978-1-4471-7520-9.
  • [20] J. Liu and Y. Zhang, "An Attribute-Weighted Bayes Classifier Based on Asymmetric Correlation Coefficient," Intern J Pattern Recognit Artif Intell, vol. 34, no. 10, Jan. 2020, doi: 10.1142/S0218001420500251.
  • [21] R. K. Bania, "R-GEFS: Condorcet Rank Aggregation with Graph Theoretic Ensemble DUJE (Dicle University Journal of Engineering) 15:3 (2024) Page 559-568 568 Feature Selection Algorithm for Classification," Intern J Pattern Recognit Artif Intell, vol. 36, no. 9, Jun. 2022, doi: 10.1142/S021800142250032X.
  • [22] T. Hu et al., "Rice Variety Identification Based on the Leaf Hyperspectral Feature via LPP-SVM," Intern J Pattern Recognit Artif Intell, vol. 36, no. 15, p. 2350001, Jan. 2023, doi: 10.1142/S0218001423500015.
  • [23] H. Coskun and T. Yigit, "Artificial Intelligence Applications on Classification of Heart Sounds," in Nature-Inspired Intelligent Techniques for Solving Biomedical Engineering Problems, IGI Global, 2018, pp. 146–183.
  • [24] H. Coskun, O. Deperlioglu, and T. Yigit, “Ekstra Sistol Kalp Seslerinin MFKK Öznitelikleriyle Yapay Sinir Aǧlari Kullanilarak Siniflandirilmasi,” 2017 25th Signal Processing and Communications Applications Conference, SIU 2017, Jun. 2017, doi: 10.1109/SIU.2017.7960252.
  • [25] H. Coskun, T. Yiğit, İ. S. Üncü, M. Ersoy, and A. Topal, "An Industrial Application Towards Classification and Optimization of Multi-Class Tile Surface Defects Based on Geometric and Wavelet Features," Traitement du Signal, vol. 39, no. 6, pp. 2011–2022, Dec. 2022, doi: 10.18280/TS.390613.
  • [26] F. Bozkurt, H. Coskun, and H. Aydogan, "Effectiveness of Classroom Lighting Colors Toward Students' Attention and Meditation Extracted from Brainwaves," Journal of Educational And Instructional Studies, vol. 4, no. 2, pp. 6–12, 2014.
  • [27] H. Aydogan, F. Bozkurt, and H. Coskun, "An assessment of brain electrical activities of students toward teacher's specific emotions," International Journal of Psychological and Behavioral Sciences, vol. 9, no. 6, pp. 2037–2040, 2015

An Automatic Labeling Approach Towards Multi-class Sitting Posture Classification Based on Depth-Sensor Data

Yıl 2024, Cilt: 15 Sayı: 3, 559 - 568
https://doi.org/10.24012/dumf.1351801

Öz

This study aims to create a non-contact system for recognizing the sitting postures of office workers, applicable to healthy sitting monitoring. Skeletal point data were obtained via a depth sensor-based Kinect device while subjects performed five different sitting postures. Five angles have been calculated that can differentiate these postures. A fuzzy rule-based automated approach using angle values is proposed to label the data. With this method, two different data sets were created using traditional time-based labeling methods. Angular and geometric features were used to classify the depth values, and 99.6% and 98.9% accuracy were obtained with KNN and Adaboost classifiers. The proposed labeling method outperformed the traditional time-based labeling method according to the classification results. This system offers a high-performance solution for promoting healthy sitting habits in office workers and has applications in health monitoring and robot vision.

Kaynakça

  • [1] "Health and safety statistics." https://www.hse.gov.uk/statistics/ (accessed Mar. 12, 2023).
  • [2] T. Borhany, E. Shahid, W. A. Siddique, and H. Ali, "Musculoskeletal problems in frequent computer and internet users," J Family Med Prim Care, vol. 7, no. 2, p. 337, 2018, doi: 10.4103/JFMPC.JFMPC_326_17.
  • [3] S. J. Ray and J. Teizer, "Real-time construction worker posture analysis for ergonomics training," Advanced Engineering Informatics, vol. 26, no. 2, pp. 439–455, Apr. 2012, doi: 10.1016/J.AEI.2012.02.011.
  • [4] O. Patsadu, C. Nukoolkit, and B. Watanapa, "Human gesture recognition using Kinect camera," JCSSE 2012 - 9th International Joint Conference on Computer Science and Software Engineering, pp. 28–32, 2012, doi: 10.1109/JCSSE.2012.6261920.
  • [5] L. Xia, C. C. Chen, and J. K. Aggarwal, "View invariant human action recognition using histograms of 3D joints," IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 20–27, 2012, doi: 10.1109/CVPRW.2012.6239233.
  • [6] P. Paliyawan, C. Nukoolkit, and P. Mongkolnam, "Prolonged sitting detection for office workers syndrome prevention using Kinect," 2014 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications, and Information Technology, ECTI-CON 2014, 2014, doi: 10.1109/ECTICON.2014.6839785.
  • [7] M. Pal, S. Saha, and A. Konar, "Distance matching based gesture recognition for healthcare using Microsoft's Kinect sensor," International Conference on Microelectronics, Computing and Communication, MicroCom 2016, Jul. 2016, doi: 10.1109/MICROCOM.2016.7522586.
  • [8] L. Yao, W. Min, and H. Cui, "A new Kinect approach to judge unhealthy sitting posture based on neck angle and torso angle," Lecture Notes in Computer Science, vol. 10666 LNCS, pp. 340– 350, 2017, doi: 10.1007/978-3-319-71607- 7_30/TABLES/1.
  • [9] B. Li, B. Bai, C. Han, H. Long, and L. Zhao, "Novel hybrid method for human posture recognition based on Kinect V2," Communications in Computer and Information Science, vol. 771, pp. 331–342, 2017, doi: 10.1007/978-981-10- 7299-4_27/TABLES/5.
  • [10] S. Bei, Z. Xing, L. Taocheng, and L. Qin, "Sitting posture detection using adaptively fused 3D features," Proceedings of the 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2017, vol. 2018-January, pp. 1073–1077, Feb. 2018, doi: 10.1109/ITNEC.2017.8284904.
  • [11] N. J. Delleman and J. Dul, "International standards on working postures and movements ISO 11226 and EN 1005-4," https://doi.org/10.1080/00140130701674430, vol. 50, no. 11, pp. 1809–1819, Nov. 2007, doi: 10.1080/00140130701674430.
  • [12] J. Kelly, "Proper Height For Standing Desks," 2014. https://notsitting.com/proper-height/
  • [13] J. Elliott, "How long should I stand at my standing desk? - HealthPostures," 2020. https://healthpostures.com/how-long-should-istand-at-my-standing-desk/ (accessed Jun. 23, 2022).
  • [14] Fondazione Ergo, "Ergonomic Assessment Worksheet," 2021. [Online]. Available: https://www.eaws.it/
  • [15] Canadian Centre for Occupational Health & Safety, "Working in a Sitting Position -Good Body Position," 2022.
  • [16] ISO, "ISO 7250-1:2017," Basic human body measurements for technological design — Part 1: Body measurement definitions and landmarks, 2017. https://www.iso.org/standard/65246.html (accessed Dec. 24, 2022).
  • [17] CCOHS, "Working in a Sitting Position - Good Body Position," 2022. https://www.ccohs.ca/oshanswers/ergonomics/sitt ing/sitting_position.html (accessed Aug. 06, 2023).
  • [18] Y.-K. Kong, S. Lee, K.-S. Lee, and D.-M. Kim, "Comparisons of ergonomic evaluation tools (ALLA, RULA, REBA and OWAS) for farm work," International journal of occupational safety and ergonomics, vol. 24, no. 2, pp. 218–223, 2018.
  • [19] J. Vince, "Mathematics for Computer Graphics," 2022, doi: 10.1007/978-1-4471-7520-9.
  • [20] J. Liu and Y. Zhang, "An Attribute-Weighted Bayes Classifier Based on Asymmetric Correlation Coefficient," Intern J Pattern Recognit Artif Intell, vol. 34, no. 10, Jan. 2020, doi: 10.1142/S0218001420500251.
  • [21] R. K. Bania, "R-GEFS: Condorcet Rank Aggregation with Graph Theoretic Ensemble DUJE (Dicle University Journal of Engineering) 15:3 (2024) Page 559-568 568 Feature Selection Algorithm for Classification," Intern J Pattern Recognit Artif Intell, vol. 36, no. 9, Jun. 2022, doi: 10.1142/S021800142250032X.
  • [22] T. Hu et al., "Rice Variety Identification Based on the Leaf Hyperspectral Feature via LPP-SVM," Intern J Pattern Recognit Artif Intell, vol. 36, no. 15, p. 2350001, Jan. 2023, doi: 10.1142/S0218001423500015.
  • [23] H. Coskun and T. Yigit, "Artificial Intelligence Applications on Classification of Heart Sounds," in Nature-Inspired Intelligent Techniques for Solving Biomedical Engineering Problems, IGI Global, 2018, pp. 146–183.
  • [24] H. Coskun, O. Deperlioglu, and T. Yigit, “Ekstra Sistol Kalp Seslerinin MFKK Öznitelikleriyle Yapay Sinir Aǧlari Kullanilarak Siniflandirilmasi,” 2017 25th Signal Processing and Communications Applications Conference, SIU 2017, Jun. 2017, doi: 10.1109/SIU.2017.7960252.
  • [25] H. Coskun, T. Yiğit, İ. S. Üncü, M. Ersoy, and A. Topal, "An Industrial Application Towards Classification and Optimization of Multi-Class Tile Surface Defects Based on Geometric and Wavelet Features," Traitement du Signal, vol. 39, no. 6, pp. 2011–2022, Dec. 2022, doi: 10.18280/TS.390613.
  • [26] F. Bozkurt, H. Coskun, and H. Aydogan, "Effectiveness of Classroom Lighting Colors Toward Students' Attention and Meditation Extracted from Brainwaves," Journal of Educational And Instructional Studies, vol. 4, no. 2, pp. 6–12, 2014.
  • [27] H. Aydogan, F. Bozkurt, and H. Coskun, "An assessment of brain electrical activities of students toward teacher's specific emotions," International Journal of Psychological and Behavioral Sciences, vol. 9, no. 6, pp. 2037–2040, 2015
Toplam 27 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Örüntü Tanıma, İnsan Bilgisayar Etkileşimi, Yapay Zeka (Diğer)
Bölüm Makaleler
Yazarlar

Hüseyin Coşkun 0000-0002-8380-245X

Erken Görünüm Tarihi 30 Eylül 2024
Yayımlanma Tarihi
Gönderilme Tarihi 29 Ağustos 2023
Yayımlandığı Sayı Yıl 2024 Cilt: 15 Sayı: 3

Kaynak Göster

IEEE H. Coşkun, “An Automatic Labeling Approach Towards Multi-class Sitting Posture Classification Based on Depth-Sensor Data”, DÜMF MD, c. 15, sy. 3, ss. 559–568, 2024, doi: 10.24012/dumf.1351801.
DUJE tarafından yayınlanan tüm makaleler, Creative Commons Atıf 4.0 Uluslararası Lisansı ile lisanslanmıştır. Bu, orijinal eser ve kaynağın uygun şekilde belirtilmesi koşuluyla, herkesin eseri kopyalamasına, yeniden dağıtmasına, yeniden düzenlemesine, iletmesine ve uyarlamasına izin verir. 24456