Learning human motion feedback with neural self-organization

Proceedings of the International Joint Conference on Neural Networks

Volumn 2015-September, Issue , 2015, Pages

  Parisi, German I ^a   Von Stosch, Florian ^a   Magg, Sven ^a   Wermter, Stefan ^a  

^a UNIVERSITY OF HAMBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

HUMAN ACTIONS; HUMAN MOTIONS; LEARNING ARCHITECTURES; LEARNING-BASED APPROACH; MECHANICAL EFFICIENCY; REAL-TIME FEEDBACK; RECURSIVE NEURAL NETWORKS; SELF ORGANIZATIONS;

NEURAL NETWORKS;

EID: 84951038544     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2015.7280701     Document Type: Conference Paper

References (20)

1. B. P. Hamill. Relative safety of weight lifting and weight training. Journal of Strength & Conditioning Research, 8 (1):53-57, 1994.
2. S. Hancock, F. Wyatt, and J. Lon Kilgore. Variation in barbell position relative to shoulder and foot anatomical landmarks alters movement efficiency. International Journal of Exercise Science, 5 (3):183-195, 2012.
3. M. Rippetoe. Starting strength: Basic barbell training, 3rd ed. The Aasgaard Company, Wichita Falls, TX, 2011.
4. D. S. Alexiadis, P. Kelly, P. Daras, N. E. O'Connor, T. Boubekeur, and M. Ben Moussa. Evaluating a dancer's performance using Kinectbased skeleton tracking. In Proceedings of the 19th ACM international conference on Multimedia, pp. 659-662. ACM, 2011.
5. G. I. Parisi and S. Wermter. Hierarchical SOM-based detection of novel behavior for 3D human tracking. In IEEE International Joint Conference on Neural Networks (/JCNN), pp. 1380-1387, Dallas, TX, USA, 2013.
6. G. l. Parisi, C. Weber and S. Wermter. Human action recognition with hierarchical growing neural gas Learning. In International Conference on Artificial Neural Networks (ICANN), pp. 89-96, Hamburg, Germany, 2014.
7. F. Anderson, T. Grossman, J. Matejka, and G. Fitzmaurice. YouMove: Enhancing movement training with an augmented reality mirror. In Proceedings of the 26th annual ACM symposium on User interface software and technology, pp. 311-320. ACM, 2013
8. K. Rector, C. L Bennett, and J. A Kientz. Eyes-free yoga: An exergame using depth cameras for blind & low vision exercise. In Proceedings of the 15th International ACM SIGACCESS Conference on Computers and Accessibility, pp. 12. ACM, 2013.
9. E. Velloso, A. Bulling, H. Gellersen, W. Ugulino, and H. Fuks. Qualitative activity recognition of weight lifting exercises. In Proceedings of the 4th Augmented Human International Conference, pp. 116-123. ACM, 2013.
10. c. 1. SUo Personal rehabilitation exercise assistant with kinect and dynamic time warping. International Journal of Information and Education Technology, 3 (4):448-454, 2013.
11. E. Velloso, A. Bulling, and H. Gellersen. MotionMA: Motion modelling and analysis by demonstration. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1309-1318. ACM, 2013.
12. T. Voegtlin. Recursive self-organizing maps. Neural Networks, 15 (8):979-991, 2005.
13. M. Hagenbuchner, A. Sperduti, and A. Chung Tsoi. A self-organizing map for adaptive processing of structured data. IEEE Transactions on Neural Networks, 14 (3):491-505, 2003.
14. M. Strickert and B. Hammer. Merge SOM for temporal data. Neurocomputing, 64:39-71, 2005.
15. B. Hammer, A. Micheli, A. Sperduti, and M. Strickert. Recursive selforganizing network models. Neural Networks, 17 (8):1061-1085, 2004.
16. T. Kohonen. The self-organizing map. Proceedings of the IEEE, 78 (9):1464-1480, 1990.
17. J. Shotton, T. Sharp, A. Kipman, A. Fitzgibbon, M. Finocchio, A. Blake, M. Cook, Richard Moore. Real-time human pose recognition in parts from single depth images. Communications of the ACM, 56 (1):116-124, 2013.
18. A. Andreakis, N. V. Hoyningen-Huene, and M. Beetz. Incremental unsupervised time series analysis using merge growing neural gas. In: Advances in Self-Organizing Maps, pp. 10-18. Springer, 2009.
19. P. A. Estevez and R. Hernandez. Gamma SOM for temporal sequence processing. In: Advances in Self-Organizing Maps, pp. 63-71. Springer Berlin Heidelberg, 2009.
20. P. A. Estevez and J. R. Vergara. Nonlinear time series analysis by using gamma growing neural gas. In: Advances in Self-Organizing Maps, pp. 205-214. Springer, 2013.