Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Computer Based Stylometric Analysis of Texts in Polish Language Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Exercise Recognition Using Averaged Hidden Markov Models

Author : Aleksandra Postawka

Published in: Artificial Intelligence and Soft Computing

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This paper presents a novel learning algorithm for Hidden Markov Models (HMMs) based on multiple learning sequences. For each activity a few left-to-right HMMs are created and then averaged into singular model. Averaged models’ structure is defined by a proposed Sequences Concatenation Algorithm which has been included in this paper. Also the modification of action recognition algorithm for such averaged models has been described.
The experiments have been conducted for the problem of modeling and recognition of chosen 13 warm-up exercises. The input data have been collected using the depth sensor Microsoft Kinect 2.0. The experiments results confirm that an averaged model combines the features of all component models and thus recognizes more sequences. The obtained models do not confuse modeled activities with others.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Spectral Clustering for Cell Formation with Minimum Dissimilarities Distance
next chapter A Study of Cluster Validity Indices for Real-Life Data
Literature
1.
Postawka, A., Śliwiński, P.: A kinect-based support system for children with autism spectrum disorder. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS, vol. 9693, pp. 189–199. Springer, Cham (2016). doi:10.​1007/​978-3-319-39384-1_​17
2.
Jalal, A., Kamal, S., Kim, D.: A depth video sensor-based life-logging human activity recognition system for elderly care in smart indoor environments. Sensors 14, 11735–11759 (2014)CrossRef
3.
Pal, M., Saha, S., Konar, A.: Distance matching based gesture recognition for healthcare using microsofts kinect sensor. In: International Conference on Microelectronics, Computing and Communications, pp. 1–6 (2016)
4.
Postawka, A., Śliwiński, P.: Recognition and modeling of atypical children behavior. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS, vol. 9119, pp. 757–767. Springer, Cham (2015). doi:10.​1007/​978-3-319-19324-3_​68 CrossRef
5.
Hai, P.T., Kha, H.H.: An efficient star skeleton extraction for human action recognition using hidden Markov models. In: IEEE Sixth International Conference on Communications and Electronics, pp. 351–356 (2016)
6.
Siddiqui, S.A., Snober, Y., Raza, S., Khan, F.M., Syed, T.Q.: Arm gesture recognition on microsoft Kinect Using a Hidden Markov Model-based representations of poses. In: International Conference on Information and Communication Technologies, pp. 1–6 (2015)
7.
Petkovic, M., Jonker, W., Zivkovic, Z.: Recognizing strokes in tennis videos using hidden Markov models. In: IASTED International Conference on Visualization, Imaging and Image Processing, pp. 512–516 (2001)
8.
Anbarsanti, N., Prihatmanto, A.S.: Dance modelling, learning and recognition system of aceh traditional dance based on hidden Markov model. In: International Conference on Information Technology Systems and Innovation, pp. 86–92 (2014)
9.
Feng-Shun Lin, J., Kulić, D.: Segmenting human motion for automated rehabilitation exercise analysis. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2881–2884 (2012)
10.
Kamal, S., Jalal, A.: Hybrid feature extraction approach for human detection, tracking and activity recognition using depth sensors. Arab. J. Sci. Eng. 41, 1043–1051 (2016)CrossRef
11.
Yin, J., Yang, Q., Junfen Pan, J.: Sensor-based abnormal human-activity detection. IEEE Trans. Knowl. Data Eng. 20, 1082–1090 (2008)CrossRef
12.
Ángeles Mendoza, M., Pérez de la Blanca, N.: HMM-based action recognition using contour histograms. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS, vol. 4477, pp. 394–401. Springer, Heidelberg (2007). doi:10.​1007/​978-3-540-72847-4_​51 CrossRef
13.
Jiang, M., Chen, Y., Zhao, Y., Cai, A.: A real-time fall detection system based on HMM and RVM. In: Proceedings of Visual Communications and Image Processing, pp. 1–6 (2013)
14.
Davis, R.I.A., Walder, C.J., Lovell, B.C.: Improved classification using hidden Markov averaging from multiple observation sequences. In: Proceedings of the Fourth Australasian Workshop on Signal Processing and Applications, pp. 89–92 (2002)
15.
Eddy, S.R.: Profile hidden Markov models. Bioinformatics 14, 755–763 (1998)CrossRef
16.
Rabiner, L.R., Juang, B.H.: An introduction to hidden Markov models. IEEE ASSP Mag. 3, 4–16 (1986)CrossRef
Metadata
Title
Exercise Recognition Using Averaged Hidden Markov Models
Author
Aleksandra Postawka
Copyright Year
2017
Book
Artificial Intelligence and Soft Computing

Print ISBN: 978-3-319-59059-2

Electronic ISBN: 978-3-319-59060-8

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59060-8_14

Premium Partner

    Image Credits