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Sequential Classification of Palm Gestures Based on A* Algorithm and MLP Neural Network for Quadrocopter Control

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents an alternative approach to the sequential data classification, based on traditional machine learning algorithms (neural networks, principal component analysis, multivariate Gaussian anomaly detector) and finding the shortest path in a directed acyclic graph, using A* algorithm with a regression-based heuristic. Palm gestures were used as an example of the sequential data and a quadrocopter was the controlled object. The study includes creation of a conceptual model and practical construction of a system using the GPU to ensure the realtime operation. The results present the classification accuracy of chosen gestures and comparison of the computation time between the CPU- and GPU-based solutions.
Słowa kluczowe
Rocznik
Strony
265--276
Opis fizyczny
Bibliogr. 24 poz., rys., tab., wykr., wzory
Twórcy
  • AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics Computer Science and Biomedical Engineering, A. Mickiewicza 30, Cracow, Poland
  • AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics Computer Science and Biomedical Engineering, A. Mickiewicza 30, Cracow, Poland
Bibliografia
  • [1] Diwakar, S., Bodda, S., Nutakki, C., Nair B.G. (2014). Neural Control using EEG as a BCI Technique for Low Cost Prosthetic Arms. Conference: Proc. of the International Conference on Neural Computation Theory and Applications (NCTA-2014).
  • [2] Bitzer, S., Van der Smagt, P. (2006). Learning EMG control of a robotic hand: towards active prostheses. Proceedings 2006 IEEE International Conference on Robotics and Automation.
  • [3] Kim, M.R., Yoon, G. (2013). Control Signal from EOG Analysis and Its Application. International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering., 7(10), 1352-1355.
  • [4] Hackenberg, G., Mccall, R., Broll, W. (2011). Lightweight palm and finger tracking for real-time 3D gesture control. Conference: IEEE Virtual Reality Conference, VR 2011, Singapore.
  • [5] Kim, B.H., Kim, M., Joevaluated, S. (2014). Quadcopter flight control using a low-cost hybrid interface with EEG-based classification and eye tracking. Computers in Biology and Medicine., (51), 82-92.
  • [6] LaFleur, K., Cassady, K., Doud, A., Shades, K., Rogin, E., He, B. (2013). Quadcopter control in threedimensional space using a noninvasive motor imagery-based brain-computer interface. Journal of Neural Engineering, 10(4).
  • [7] Bishop, C. (2006). Pattern Recognition and Machine Learning. New York: Springer
  • [8] Asano, S., Maruyama, T., Yamaguchi, Y. (2009). Performance comparison of FPGA, GPU and CPU in image processing. 2009 International Conference on Field Programmable Logic and Applications.
  • [9] Papadonikolakis. M., Bouganis. C.S., Constantinides. G. (2010). Performance comparison of GPU and FPGA architectures for the SVM training problem. International Conference on Field-Programmable Technology, 2009.
  • [10] Rabieah. M.B., Bouganis. C.S. (2015). FPGA based nonlinear Support Vector Machine training using an ensemble learning. 2015 25th International Conference on Field Programmable Logic and Applications.
  • [11] Panuccio, A., Bicego, M., Murino, V. (2002). A Hidden Markov Model-Based Approach to Sequential Data Clustering. Conference: Structural, Syntactic, and Statistical Pattern Recognition.
  • [12] Li, J., Chen, S., Li, Y. (2009). The fast evaluation of hidden Markov models on GPU. IEEE International Conference on Intelligent Computing and Intelligent Systems. ICIS 2009., (4), 426-430.
  • [13] Leap Motion API Reference: Classes. https://developer.leapmotion.com/documentation/python/api/Leap_Classes.html
  • [14] Weichert, F., Bachmann, D., Rudak, B., Fisseler, D. (2013). Analysis of the Accuracy and Robustness of the Leap Motion Controller. Sensors., 13(5), 6380-6393.
  • [15] Leap Motion API Reference: Gestures. https://developer.leapmotion.com/documentation/python/devguide/Leap_Gestures.html
  • [16] Parrot (2012). AR Drone Developer Guide SDK. http://www.msh-tools.com/ardrone/ARDrone_Developer_Guide.pdf
  • [17] Xu, Y., Wang, Q., Bai, X., Chen, Y.L., Wu, X. (2014). A Novel Feature Extracting Method for Dynamic Gesture Recognition Based on Support Vector Machine. Proc. of the IEEE International Conference on Information and Automation.
  • [18] She, Y., Wang, Q., Jia, Y., Gu, T., He, Q., Yang, B. (2014). A Real-time Hand Gesture Recognition Approach Based on Motion Features of Feature Points. IEEE 17th International Conference on Computational Science and Engineering.
  • [19] Vamsikrishna, K.M., Dogra, D.P. (2016). Computer-Vision-Assisted Palm Rehabilitation With Supervised Learning. IEEE Transactions On Biomedical Engineering., 63(5), 991-1001.
  • [20] Sreejith, M., Siddharth, R., Samik, G., Samprit, B., Partha, P.D. (2015). Real-time hands-free immersive image navigation system using Microsoft Kinect 2.0 and Leap Motion Controller. 2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG).
  • [21] Avidan, S., Shamir, A. (2007). Seam Carving for Content-Aware Image Resizing. ACM Transactions on Graphics (TOG), 26(3), 10.
  • [22] Kohonen, T. (1989). Self-organization and associative memory. 3rd ed. New York: Springer.
  • [23] Kapre, N. (2009). Performance comparison of single-precision SPICE Model-Evaluation on FPGA, GPU, Cell, and multi-core processors. 2009 International Conference on Field Programmable Logic and Applications.
  • [24] Stratix® 10 DSP Specification Table: https://www.altera.com/products/fpga/stratix-series/stratix-10/features.html#dsp
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1195ddc9-a987-4465-8da5-7ae78db20c35
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