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

Yıl 2017, Cilt: 17 Sayı: 1, 117 - 123, 24.04.2017

Rafet Durgut İsmail Kurnaz

Öz

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.

Anahtar Kelimeler

Hareket Tanıma; Microsoft Kinect Dinamik Zaman Bükmesi Sembolik Birleştirme Yaklaşımı

Kaynakça

• 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.