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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 11/2018

08.10.2016 | Original Article

Linear SVM-based recognition of elementary juggling movements using correlation dimension of Euler Angles of a single arm

verfasst von: Francesco Camastra, Francesco Esposito, Antonino Staiano

Erschienen in: Neural Computing and Applications | Ausgabe 11/2018

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Abstract

Recognizing the human arm movements has several applications, and it can be performed in a number of ways through the use of one or more sensor devices that the technology offers. This paper aims to exploit the exercises performed by jugglers in order to recognize the arm movements on the basis of the only information on the arm orientation provided by the Euler Angles. The proposed recognizer has two modules, i.e., a feature extractor and a classifier. The former reconstructs the dynamics of the system and estimates three correlation dimensions, each associated with a given Euler Angle. The latter is formed by a Linear Support Vector Machine. Extensive experimentations show the effectiveness of the proposed approach.
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Fußnoten
1
Dynamics has to be intended in terms of a dynamical system, namely a system where a mathematical function describes the dependence of a point in a geometrical space.
 
2
A manifold (http://​en.​wikipedia.​org/​wiki/​Manifold) is a mathematical space in which every point has a neighborhood which resembles Euclidean space, but in which the global structure may be more complicated. In a one-dimensional manifold every point has a neighborhood that looks like a line segment; in a two-dimensional manifold the neighborhood looks like a disk. \(\mathbb {R}^n\) is a n-dimensional manifold.
 
3
In dynamical systems, an attractor is a set of numerical values toward a system tends to evolve asymptotically, as time increases, for a wide variety of initial conditions of the system [38].
 
4
Takens–Mañé embedding theorem is a consequence of a Whitney Embedding Theorem [47] stating that a generic map from an S-dimensional manifold to a \((2S+1)\)-dimensional Euclidean space is an embedding, i.e., the image of the S-dimensional manifold is completely unfolded in the larger space. Therefore, two data points in the S-dimensional manifold do not map to the same point in the \((2S+1)\)-dimensional space.
 
5
Colibri is a registered trademark of Trivisio Prototyping GmbH.
 
6
The maximum number of balls, for few seconds, that a professional juggler is able to handle in his movements is seven.
 
7
The database is available, on request, for further investigations.
 
8
A KNN experiment can be performed by LVQ-pak command: eveninit -noc 2 -din fileinput -cout fileoutput -knn 3 where noc stands for the number of codevectors (i.e., 2), din corresponds to the name of input data file, cout provides the name of the codevector file, and knn denotes the number of k nearest neighbors (i.e., 3).
 
Literatur
1.
Austin H (1974) A computational view of the skill of juggling. Tech. Rep. 317, MIT, Laboratory of Artificial Intelligence
2.
Banos O, Damas M, Pomares H, Prieto A, Rojas I (2012) Daily living activity recognition based on statistical feature quality group selection. Expert Syst Appl 39(9):8013–8021CrossRef
3.
Beek P (1989) Juggling dynamics. Free University Press, Amsterdam
4.
Beek PJ, Lewbel A (1995) The science of juggling. Sci Am 273(11):92–97CrossRef
5.
Biswas D, Cranny A, Gupta N, Maharatna K, Achner J, Klemke J, Jöbges M, Ortmann S (2015) Recognizing upper limb movements with wrist worn inertial sensors using k-means clustering classification. Hum Mov Sci 40:59–76CrossRef
6.
Botia J, Villa A, Palma J (2012) Ambient assisted living system for in-home monitoring of healthy independent elders. Expert Syst Appl 39(9):8136–8148CrossRef
7.
Bühler M, Koditschek D, Kindlmann P (1990) Planning and control of robotic juggling tasks. In: The fifth international symposium on robotic research, The MIT Press, pp 321–332
8.
Burdea G, Coiffet P (2003) Virtual reality technology. Wiley, New York
9.
Camastra F, Filippone M (2009) A comparative evaluation of nonlinear dynamics methods for time series prediction. Neural Comput Appl 18(8):1021–1029CrossRef
10.
Camastra F, Staiano A (2016) Intrinsic dimension estimation: advances and open problems. Inf Sci 328:26–41CrossRef
11.
Carter KM, Hero AO, Raich R (2007) De-biasing for intrinsic dimension estimation. In: Proceedings of IEEE statistics signal processing workshop, pp 601–605
12.
Chaaraoui A, Climent-Pérez P, Flórez-Revuelta F (2012) A review on vision techniques applied to human behaviour analysis for ambient-assisted living. Expert Syst Appl 39(12):10,873–10,888CrossRef
13.
Chang CC, Lin CJ (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):1–27CrossRef
14.
Chernbumroong S, Cang S, Atkins A, Yu H (2013) Elderly activities recognition and classification for applications in assisted living. Expert Syst Appl 40(5):1662–1674CrossRef
15.
Chernbumroong S, Cang S, Yu H (2014) A practical multi-sensor activity recognition system for home-based care. Decis Support Syst 66:61–70CrossRef
16.
Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20:1–25MATH
17.
Dubovikov MM, Starchenko NV, Dubovikov MS (2004) Dimension of the minimal cover and fractal analysis of time series. Phys A Stat Mech Appl 339(3–4):591–608MathSciNetCrossRef
18.
19.
Eckmann JP, Ruelle D (1992) Fundamental limitations for estimating dimensions and lyapounov exponents in dynamical systems . Phys D 56:185–187MathSciNetCrossRefMATH
20.
Einbeck J, Kalantana Z (2013) Intrinsic dimensionality estimation for high-dimensional data sets: new approaches for the computation of correlation dimension. J EmergTechnol Web Intell 5(2):91–97
21.
Fan M, Qiao H, Zhang B (2009) Intrinsic dimension estimation of manifolds by incising balls. Pattern Recognit 42:780–787CrossRefMATH
22.
Field M, Stirling D, Pan Z, Ros M, Naghdy F (2015) Recognizing human motions through mixture modeling of inertial data. Pattern Recognit 48(8):2394–2406CrossRef
23.
Fuentes D, Gonzalez-Abril L, Angulo C, Ortega J (2012) Online motion recognition using an accelerometer in a mobile device. Expert Syst Appl 39:2461–2465CrossRef
24.
Goldstein H (1980) Classical mechanics. Addison-Wesley, BostonMATH
25.
26.
Hastie T, Tibshirani R, Friedman R (2001) The elements of statistical learning. Springer, New YorkCrossRefMATH
27.
Haykin S, Bo Li X (1995) Detection of signals in chaos. Proc IEEE 83(1):95–122CrossRef
28.
Joachim T (1999) Making large-scale SVM learning practical. In: Schölkopf B, Burges CJC (eds) Advances in Kernel methods—support vector learning. MIT Press, Cambridge, pp 169–184
29.
Kober J, Graham R, Mistry M (2012) Playing catch and juggling with a humanoid robot. In: Proceedings of \(12^{th}\) IEEE-RAS international conference on humanoid robots, IEEE, pp 875–881
30.
Kohonen T (1995) Learning vector quantization. In: Arbib MA (ed) The handbook of brain theory and neural networks. MIT Press, Cambridge, pp 537–540
31.
32.
Kohonen T, Hynninen J, Kangas J, Laaksonen J, Torkkola K (1996) Lvq-pak: The learning vector quantization program package. Tech. Rep. A30, Helsinki University of Technology, Laboratory of Computer and Information Science
33.
Landau L, Lifshitz EM (1996) Mechanics. Butterworth-Heinemann, OxfordMATH
34.
Mañé R (1981) On the dimension of compact invariant sets of certain nonlinear maps. In: Rand D, Young L-S (eds) Dynamical systems and turbolence, Warwick 1980. Springer, Berlin, pp 230–242CrossRef
35.
Nikolic D, Moca V, Singer W, Muresan R (2008) Properties of multivariate data investigated by fractal dimensionality. J Neurosci Methods 172:27–33CrossRef
36.
North B, Blake A, Isard M, Rittscher J (2000) Learning and classification of complex dynamics. IEEE Trans Pattern Anal Mach Intell 22(9):1016–1034CrossRef
37.
Ordóõez F, Iglesias J, de Toledo P, Ledezma A, Sanchis A (2013) Online activity recognition using evolving classifiers. Expert Syst Appl 40(4):1248–1255CrossRef
38.
Ott E (1988) Chaos in dynamical systems. Cambridge University Press, CambridgeMATH
39.
Packard N, Crutchfield J, Farmer J, Shaw R (1980) Geometry from a time series. Phys Rev Lett 45(1):712–716CrossRef
40.
Polster B (2003) The Mathematics of Juggling. Springer, BerlinMATH
41.
Richards GR (2004) A fractal forecasting model for financial time series. J Forecast 23(8):586–601CrossRef
42.
Shannon C (1993) Scientific aspects of juggling. In: C.E. Shannon Collected Papers, Wiley, pp 230–242
43.
Stone M (1974) Cross-validatory choice and assessment of statistical prediction. J RStat Soc 36(1):111–147MathSciNetMATH
44.
Takens F (1981) Detecting strange attractor in turbolence. In: Rand D, Young L-S (eds) Dynamical Systems and Turbolence, Warwick 1980. Springer, Berlin, Heidelberg, pp 366–381CrossRef
45.
Takens F (1985) On the numerical determination of the dimension of anattractor. In: Braaksma BLJ, Broer HW, Takens F (eds) Dynamical systems and bifurcations, Proceedings Groningen 1984. Addison Wesley, New York, pp 99–106
46.
Vapnik V (1998) Statistical learning theory. Wiley, New YorkMATH
47.
48.
Ziethen KH, Allen A (1985) Juggling, the art and its artists. Werner Rausch and Werner Luft Inc, Berlin
Metadaten
Titel
Linear SVM-based recognition of elementary juggling movements using correlation dimension of Euler Angles of a single arm
verfasst von
Francesco Camastra
Francesco Esposito
Antonino Staiano
Publikationsdatum
08.10.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 11/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2616-x

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