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İskelet Bilgisi Üzerinde Ağırlıklı Dinamik Zaman Bükmesi ve Sembolik Birleştirme Yaklaşımı Metotları Kullanarak Yeni Bir Hareket Tanıma Sistemi

Year 2017, Volume: 17 Issue: 1, 117 - 123, 24.04.2017

Abstract

Sensörler ile donatılmış derinlik kamera cihazlarının maliyetlerinin ekonomik olması nedeniyle,
günümüzde kullanım alanları artmakta ve yaygınlaşmaktadır. Bu çalışmada bu tür cihazların en çok
kullanılanlarından biri olan Kinect cihazından elde edilen veriler üzerinde, Ağırlıklı Dinamik Zaman
Bükmesi ve Sembolik Birleştirme Yaklaşımı yöntemleri birlikte kullanılarak yeni bir hareket tanıma
yöntemi geliştirilmiştir. Geliştirilen yöntem günlük hareketlerin yer aldığı veri setinde test edilmiş ve
%98.15 oranında bir başarı ile günlük hareketler tanınabilmiştir.

References

  • Chang, C.-Y., et al. Towards pervasive physical rehabilitation using Microsoft Kinect. in Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2012 6th International Conference on. 2012. IEEE.
  • [2] Chang, Y.-J., et al, A Kinect-based system for physical rehabilitation: A pilot study for young adults with motor disabilities. Research in developmental disabilities, 2011. 32(6): p. 2566- 2570.
  • [3] Chang, Y.-J., et al. A Kinect-based upper limb rehabilitation system to assist people with cerebral palsy. Research in developmental disabilities, 2013. 34(11): p. 3654-3659.
  • [4] Lange, B., et al. Development and evaluation of low cost game-based balance rehabilitation tool using the Microsoft Kinect sensor. in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE. 2011. IEEE.
  • [5] Mentiplay, B., et al., Evaluation of foot posture using the Microsoft Kinect. Journal of Science and Medicine in Sport, 2013. 16: p. e24-e25.
  • [6] Xbox One için Kinect. 04.04.2016]; Available from: http://www.xbox.com/tr-TR/xboxone/ accessories/kinect-for-xboxone# fbid=8NuX71sh2sB.
  • [7] Xtion PRO Live. 04.04.2016]; Available from: https://www.asus.com/tr/3DSensor/ Xtion_PRO_LIVE/.
  • [8] Harding, et al. Ellis. Recognizing hand gesture using Fourier descriptors. in Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on. 2004. IEEE.
  • [9] Karami, A., et al. Persian sign language (PSL) recognition using wavelet transform and neural networks. Expert Systems with Applications, 2011. 38(3): p. 2661-2667.
  • [10] Brand, M., et al. Coupled hidden Markov models for complex action recognition. in Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on. 1997. IEEE.
  • [11] Maji, S., et al. Classification using intersection kernel support vector machines is efficient. in Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on. 2008. IEEE.
  • [12] Le, T.-L., et al. Human posture recognition using human skeleton provided by Kinect. Computing, Management and Telecommunications (ComManTel), 2013 International Conference on, IEEE. (2013)
  • [13] Patsadu, O. Et al. Human gesture recognition using Kinect camera. Computer Science and Software Engineering (JCSSE), 2012 International Joint Conference on. IEEE, 2012.
  • [14] Berndt, D.J. and J. Clifford. Using Dynamic Time Warping to Find Patterns in Time Series. in KDD workshop. 1994. Seattle, WA.
  • [15] Rekha, J., J. Bhattacharya, and S. Majumder. "Shape, texture and local movement hand gesture features for indian sign language recognition." Trendz in Information Sciences and Computing (TISC), 2011 3rd International Conference on. IEEE, 2011.
  • [16] Celebi, S., et al. Gesture Recognition using Skeleton Data with Weighted Dynamic Time Warping. in VISAPP (1). 2013.
  • [17] Kobayashi, Mizuki, et al. "A probabilistic approach to text generation of human motions extracted from Kinect videos." Proceedings of the World Congress on Engineering and Computer Science 2013. 2013.
  • [18] Lin, J., et al. A symbolic representation of time series, with implications for streaming algorithms. in Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery. 2003. ACM.
  • [19] Lun, R. and W. Zhao, A survey of applications and human motion recognition with Microsoft Kinect. International Journal of Pattern Recognition and Artificial Intelligence, 2015. 29(05): p. 1555008.
  • [20] Lin, J., et al., Experiencing SAX: a novel symbolic representation of time series. Data Mining and knowledge discovery, 2007. 15(2): p. 107-144.
  • [21] Bellman, R., The theory of dynamic programming. 1954, DTIC Document.
Year 2017, Volume: 17 Issue: 1, 117 - 123, 24.04.2017

Abstract

References

  • Chang, C.-Y., et al. Towards pervasive physical rehabilitation using Microsoft Kinect. in Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2012 6th International Conference on. 2012. IEEE.
  • [2] Chang, Y.-J., et al, A Kinect-based system for physical rehabilitation: A pilot study for young adults with motor disabilities. Research in developmental disabilities, 2011. 32(6): p. 2566- 2570.
  • [3] Chang, Y.-J., et al. A Kinect-based upper limb rehabilitation system to assist people with cerebral palsy. Research in developmental disabilities, 2013. 34(11): p. 3654-3659.
  • [4] Lange, B., et al. Development and evaluation of low cost game-based balance rehabilitation tool using the Microsoft Kinect sensor. in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE. 2011. IEEE.
  • [5] Mentiplay, B., et al., Evaluation of foot posture using the Microsoft Kinect. Journal of Science and Medicine in Sport, 2013. 16: p. e24-e25.
  • [6] Xbox One için Kinect. 04.04.2016]; Available from: http://www.xbox.com/tr-TR/xboxone/ accessories/kinect-for-xboxone# fbid=8NuX71sh2sB.
  • [7] Xtion PRO Live. 04.04.2016]; Available from: https://www.asus.com/tr/3DSensor/ Xtion_PRO_LIVE/.
  • [8] Harding, et al. Ellis. Recognizing hand gesture using Fourier descriptors. in Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on. 2004. IEEE.
  • [9] Karami, A., et al. Persian sign language (PSL) recognition using wavelet transform and neural networks. Expert Systems with Applications, 2011. 38(3): p. 2661-2667.
  • [10] Brand, M., et al. Coupled hidden Markov models for complex action recognition. in Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on. 1997. IEEE.
  • [11] Maji, S., et al. Classification using intersection kernel support vector machines is efficient. in Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on. 2008. IEEE.
  • [12] Le, T.-L., et al. Human posture recognition using human skeleton provided by Kinect. Computing, Management and Telecommunications (ComManTel), 2013 International Conference on, IEEE. (2013)
  • [13] Patsadu, O. Et al. Human gesture recognition using Kinect camera. Computer Science and Software Engineering (JCSSE), 2012 International Joint Conference on. IEEE, 2012.
  • [14] Berndt, D.J. and J. Clifford. Using Dynamic Time Warping to Find Patterns in Time Series. in KDD workshop. 1994. Seattle, WA.
  • [15] Rekha, J., J. Bhattacharya, and S. Majumder. "Shape, texture and local movement hand gesture features for indian sign language recognition." Trendz in Information Sciences and Computing (TISC), 2011 3rd International Conference on. IEEE, 2011.
  • [16] Celebi, S., et al. Gesture Recognition using Skeleton Data with Weighted Dynamic Time Warping. in VISAPP (1). 2013.
  • [17] Kobayashi, Mizuki, et al. "A probabilistic approach to text generation of human motions extracted from Kinect videos." Proceedings of the World Congress on Engineering and Computer Science 2013. 2013.
  • [18] Lin, J., et al. A symbolic representation of time series, with implications for streaming algorithms. in Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery. 2003. ACM.
  • [19] Lun, R. and W. Zhao, A survey of applications and human motion recognition with Microsoft Kinect. International Journal of Pattern Recognition and Artificial Intelligence, 2015. 29(05): p. 1555008.
  • [20] Lin, J., et al., Experiencing SAX: a novel symbolic representation of time series. Data Mining and knowledge discovery, 2007. 15(2): p. 107-144.
  • [21] Bellman, R., The theory of dynamic programming. 1954, DTIC Document.
There are 21 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Rafet Durgut

İsmail Kurnaz

Publication Date April 24, 2017
Submission Date April 15, 2016
Published in Issue Year 2017 Volume: 17 Issue: 1

Cite

APA Durgut, R., & Kurnaz, İ. (2017). İskelet Bilgisi Üzerinde Ağırlıklı Dinamik Zaman Bükmesi ve Sembolik Birleştirme Yaklaşımı Metotları Kullanarak Yeni Bir Hareket Tanıma Sistemi. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 17(1), 117-123.
AMA Durgut R, Kurnaz İ. İskelet Bilgisi Üzerinde Ağırlıklı Dinamik Zaman Bükmesi ve Sembolik Birleştirme Yaklaşımı Metotları Kullanarak Yeni Bir Hareket Tanıma Sistemi. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. April 2017;17(1):117-123.
Chicago Durgut, Rafet, and İsmail Kurnaz. “İskelet Bilgisi Üzerinde Ağırlıklı Dinamik Zaman Bükmesi Ve Sembolik Birleştirme Yaklaşımı Metotları Kullanarak Yeni Bir Hareket Tanıma Sistemi”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 17, no. 1 (April 2017): 117-23.
EndNote Durgut R, Kurnaz İ (April 1, 2017) İskelet Bilgisi Üzerinde Ağırlıklı Dinamik Zaman Bükmesi ve Sembolik Birleştirme Yaklaşımı Metotları Kullanarak Yeni Bir Hareket Tanıma Sistemi. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 17 1 117–123.
IEEE R. Durgut and İ. Kurnaz, “İskelet Bilgisi Üzerinde Ağırlıklı Dinamik Zaman Bükmesi ve Sembolik Birleştirme Yaklaşımı Metotları Kullanarak Yeni Bir Hareket Tanıma Sistemi”, Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, vol. 17, no. 1, pp. 117–123, 2017.
ISNAD Durgut, Rafet - Kurnaz, İsmail. “İskelet Bilgisi Üzerinde Ağırlıklı Dinamik Zaman Bükmesi Ve Sembolik Birleştirme Yaklaşımı Metotları Kullanarak Yeni Bir Hareket Tanıma Sistemi”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 17/1 (April 2017), 117-123.
JAMA Durgut R, Kurnaz İ. İskelet Bilgisi Üzerinde Ağırlıklı Dinamik Zaman Bükmesi ve Sembolik Birleştirme Yaklaşımı Metotları Kullanarak Yeni Bir Hareket Tanıma Sistemi. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2017;17:117–123.
MLA Durgut, Rafet and İsmail Kurnaz. “İskelet Bilgisi Üzerinde Ağırlıklı Dinamik Zaman Bükmesi Ve Sembolik Birleştirme Yaklaşımı Metotları Kullanarak Yeni Bir Hareket Tanıma Sistemi”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, vol. 17, no. 1, 2017, pp. 117-23.
Vancouver Durgut R, Kurnaz İ. İskelet Bilgisi Üzerinde Ağırlıklı Dinamik Zaman Bükmesi ve Sembolik Birleştirme Yaklaşımı Metotları Kullanarak Yeni Bir Hareket Tanıma Sistemi. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2017;17(1):117-23.