Human motion assessment in real time using recurrent self-organization

25th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2016

Volumn , Issue , 2016, Pages 71-76

  Parisi, German I ^a   Magg, Sven ^a   Wermter, Stefan ^a  

^a UNIVERSITY OF HAMBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

GESTURE RECOGNITION; MOTION ESTIMATION; NEURAL NETWORKS;

EFFICIENT LEARNING; HUMAN-ACTION RECOGNITION; LEARNING-BASED METHODS; NOVEL ARCHITECTURE; PHYSICAL REHABILITATION; REAL-TIME FEEDBACK; RECURSIVE NEURAL NETWORKS; SELF ORGANIZATIONS;

FEEDBACK;

EID: 85002605885     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ROMAN.2016.7745093     Document Type: Conference Paper

References (20)

1. R. Kachouie, S. Sedighadeli, R. Khosla, and M-T. Chu, M-T. Socially Assistive Robots in Elderly Care: A Mixed-Method Systematic Literature Review. Int. J. Hum. Comput. Interaction 30(5):369-393, 2014
2. E. Velloso, A. Bulling, G. Gellersen, W. Ugulino, and G. Fuks. Qualitative activity recognition of weight lifting exercises. Augmented Human International Conference, 116-123 (ACM), 2013
3. G.I. Parisi, C. Weber, and S. Wermter. Self-Organizing Neural Integration of Pose-Motion Features for Human Action Recognition. Frontiers in Neurorobotics, 9(3), 10.3389/fnbot.2015.00003, 2015
4. Y-J. Chang, S-F. Chen, J-D. Huang. A Kinect-based system for physical rehabilitation: A pilot study for young adults with motor disabilities. Research in Developmental Disabilities 32(6):2566-2570, 2011
5. C.J. Su. Personal rehabilitation exercise assistant with kinect and dynamic time warping. Intl. Journal of Information and Education Technology, 3(4):448-454, 2013
6. A. Paiement, L. Tao, S. Hannuna, C. Camplani, M. Damen, and M. Mirmehdi. Online quality assessment of human movement from skeleton data. In: British Machine Vision Conference (BMVC), 2014
7. E. Velloso, A. Bulling, and H. Gellersen. MotionMA: Motion modelling and analysis by demonstration. In: SIGCHI Conference on Human Factors in Computing Systems, 1309-1318 (ACM), 2013
8. H. Pirsiavash, C. Vondrick, and A. Torralba. Assessing the quality of actions. In: ECCV, 556-571, 2014
9. G.I. Parisi, F. v. Stosch, S. Magg, S. Wermter. Learning human motion feedback with neural self-organization. In: IJCNN, pp. 2973-2978, 2015
10. T. Martinetz. Competitive Hebbian learning rule forms perfectly topology preserving maps. In: ICANN, pp. 427-434, Springer, 1993
11. S. Marsland, J. Shapiro, and U. Nehmzow. A self-organising network that grows when required. Neural Networks 15:1041-1058, 2002
12. G.J. Chappell and J.G. Taylor. The temporal Kohonen map. Neural networks 6:441-445, Elsevier, 1993
13. T. Voegtlin. Recursive self-organizing maps. Neural Networks 15(8):979-991, 2002
14. M. Hagenbuchner, A. Sperduti, and A.C. Tsoi. A self-organizing map for adaptive processing of structured data. Neural Networks, 14(3):491-505, 2003
15. M. Strickert, B. Hammer. Merge SOM for temporal data. Neurocomputing 64:39-71, 2005
16. N. Fritzke. A Growing Neural Gas Network Learns Topologies. In: NIPS 7, pp. 625-632, MIT Press, 1995
17. A. Andreakis, N.v. Hoyningen-Huene, M. Beetz M. Incremental unsupervised time series analysis using merge growing neural gas. In: WSOM, pp. 10-18 (Springer), 2009
18. K. Dautenhahn. Robots as social actors: Aurora and the case of autism. In: Third Cognitive Technology Conference, 1999
19. C. D. Kidd and C. Breazeal. A robotic weight loss coach. In: AAAI, pp. 1985-1986, AAAI Press, 2007
20. M. Nalin, I. Baroni, A. Sanna, and C. Pozzi. Robotic companion for diabetic children: emotional and educational support to diabetic children, through an interactive robot. In: ACM SIGCHI, pp. 260-263, 2012.