SavedAdd your journal

Journals / Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi / 2017 / Cilt: 17 - Sayı: 1

A New Gesture Recognition System Using Weighted Dynamic Time Warping and Symbolic Aggregation Approximation Methods on Skeleton Data

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

Rafet DURGUT
İsmail KURNAZ
Pages
117–123
Publication Date
2022-10-12
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The developed method has been tested in the data set where the daily movements recorded in and they can be recognized with a success rate of 98.15%.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Nowadays, the usage areas of depth cameras which equipped with sensors are increasing and growing up extensively, because of their economic prices.
📊
Key Findings & Data:In this study, a new gesture recognition method is developed by combining Dynamic Time Warping and Symbolic Aggregation Approximation methods on data obtained from a Kinect device which is one of the most widely used among such devices.
💡
Conclusion & Impact:The developed method has been tested in the data set where the daily movements recorded in and they can be recognized with a success rate of 98.15%.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Nowadays, the usage areas of depth cameras which equipped with sensors are increasing and growing up extensively, because of their economic prices. In this study, a new gesture recognition method is developed by combining Dynamic Time Warping and Symbolic Aggregation Approximation methods on data obtained from a Kinect device which is one of the most widely used among such devices. The developed method has been tested in the data set where the daily movements recorded in and they can be recognized with a success rate of 98.15%.

Özet

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

🔖

Cite This Article

DURGUT, R., KURNAZ, İ. (2022). A New Gesture Recognition System Using Weighted Dynamic Time Warping and Symbolic Aggregation Approximation Methods on Skeleton Data. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, Cilt: 17 - Sayı: 1, 117–123. https://ajindex.com/articles/430143

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (14)

  1. 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
  4. Mentiplay, B., et al., Evaluation of foot posture using the Microsoft Kinect. Journal of Science and Medicine in Sport, 2013. 16: p. e24-e25
  5. Xbox One için Kinect. 04.04.2016]; Available from:
  6. Xtion PRO Live. 04.04.2016]; Available from:
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. Patsadu, O. Et al. Human gesture recognition using Kinect camera. Computer Science and Software Engineering (JCSSE), 2012 International Joint Conference on. IEEE, 2012
  13. Berndt, D.J. and J. Clifford. Using Dynamic Time Warping to Find Patterns in Time Series. in KDD workshop. 1994. Seattle, WA
  14. Rekha, J., J. Bhattach