Abstract
Basic postures such as sit, stand and lie are ubiquitous in human interaction. In order to build robots that aid and support humans in their daily life, we need to understand how posture categories can be learned and recognized. This paper presents an unsupervised learning approach to posture recognition for a biped humanoid robot. The approach is based on Slow Feature Analysis (SFA), a biologically inspired algorithm for extracting slowly changing signals from signals varying on a fast time scale. Two experiments are carried out: First, we consider the problem of recognizing static postures in a multimodal sensory stream which consists of visual and proprioceptive stimuli. Secondly, we show how to extract a low-dimensional representation of the sensory state space which is suitable for posture recognition in a more complex setting. We point out that the beneficial performance of SFA in this task can be related to the fact that SFA computes manifolds which are used in robotics to model invariants in motion and behavior. Based on this insight, we also propose a method for using SFA components for guided exploration of the state space.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Berkes P (2006) Temporal slowness as an unsupervised learning principle. PhD thesis, Humboldt-Universitat zu Berlin
Berkes P, Wiskott L (2002) Applying Slow Feature Analysis to Image Sequences
Yields a Rich Repertoire of Complex Cell Properties. In: Dorronsoro JR (ed)
Proc. Intl. Conf. on Artificial Neural Networks - ICANN’02, Springer, Lecture Notes in Computer Science, pp 81–86
Berkes P, Wiskott L (2006) On the analysis and interpretation of inhomogeneous quadratic forms as receptive fields. Neural Computation 18(8):1868–1895
Franzius M, Sprekeler H, Wiskott L (2007) Slowness and sparseness lead to place, head-direction, and spatial-view cells. PLoS Computational Biology 3(8):e166
Hild M, Siedel T, Benckendorff C, Thiele C, Spranger M (2012) Myon, a New
Humanoid. In: Steels L, Hild M (eds) Language Grounding in Robots, Springer, New York
Hofer S, Hild M (2010) Using Slow Feature Analysis to Improve the Reactivity of a Humanoid Robot’s Sensorimotor Gait Pattern. In: International Conference on Neural Computation (ICNC 2010), Valencia, Spain, pp 212 – 219
Li S, de Vel O, Coomans D (1995) Comparative performance analysis of non-linear dimensionality reduction methods. Tech. rep., James Cook University, North
Moeslund TB, Hilton A, Kruger V (2006) A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding 104(2-3):90–126, DOI 10.1016/j.cviu.2006.08.002
Mukundan R, Ramakrishnan K (1998) Moment functions in image analysis: theory and applications. World Scientific Publishing
Pearson K (1901) On lines and planes of closest fit to systems of points in space. Philosophical Magazine 2:559–572
Plagemann C, Ganapathi V, Koller D, Thrun S (2010) Real-time identification and localization of body parts from depth images. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp 3108–3113, DOI 10.1109/ROBOT.2010.5509559
Ronald P (2007) Vision-based human motion analysis: An overview. Computer Vision and Image Understanding 108(1-2):4–18, DOI 10.1016/j.cviu.2006.10.016, URL http://www.sciencedirect.com/science/article/pii/S1077314206002293
Rousseeuw P (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20(1):53–65
Roweis S, Saul L (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326
Selig J (2005) Geometric Fundamentals of Robotics. Springer, New York
Spranger M, Loetzsch M (2009) The semantics of sit, stand, and lie embodied in
robots. In: Taatgen NA, van Rijn H (eds) Proceedings of the 31th Annual Conference of the Cognitive Science Society (Cogsci09), Cognitive Science Society, Austin, TX, pp 2546–2552
Spranger M, Hofer S, Hild M (2009) Biologically inspired posture recognition and posture change detection for humanoid robots. In: Proc. IEEE International Conference on Robotics and Biomimetics (ROBIO), Guilin, China, pp 562–567
Steels L, SprangerM(2008) The robot in the mirror. Connection Science 20(4):337– 358
Wiskott L (1998) Learning Invariance Manifolds. In: Proc. of the 5th Joint Symp. on Neural Computation, May 16, San Diego, CA, Univ. of California, San Diego, CA, vol 8, pp 196–203
Wiskott L (2003a) Estimating Driving Forces of Nonstationary Time Series with Slow Feature Analysis
Wiskott L (2003b) Slow Feature Analysis: A Theoretical Analysis of Optimal Free Responses. Neural Computation 15(9):2147–2177
Wiskott L, Sejnowski T (2002) Slow Feature Analysis: Unsupervised Learning of Invariances. Neural Computation 14(4):715–770
Zito T, Wilbert N, Wiskott L, Berkes P (2009) Modular toolkit for Data Processing (MDP): a Python data processing frame work
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Höfer, S., Spranger, M., Hild, M. (2012). Posture Recognition Based on Slow Feature Analysis. In: Steels, L., Hild, M. (eds) Language Grounding in Robots. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-3064-3_6
Download citation
DOI: https://doi.org/10.1007/978-1-4614-3064-3_6
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4614-3063-6
Online ISBN: 978-1-4614-3064-3
eBook Packages: Computer ScienceComputer Science (R0)