single-jc.php

JACIII Vol.21 No.7 pp. 1240-1250
doi: 10.20965/jaciii.2017.p1240
(2017)

Paper:

Time-Series Data Analysis Using Sliding Window Based SVD for Motion Evaluation

Yinlai Jiang*1, Isao Hayashi*2, Shuoyu Wang*3, and Kenji Ishida*4

*1The University of Electro-Communications
Chofu, Tokyo 182-8585, Japan

*2Kansai University
Takatsuki, Osaka 569-1095, Japan

*3Kochi University of Technology
Kami, Kochi 782-8502, Japan

*4Kurihara Central Hospital
Kurihara, Miyagi 987-2205, Japan

Received:
December 30, 2016
Accepted:
August 31, 2017
Published:
November 20, 2017
Keywords:
singular value decomposition, time-series data analysis, sliding window, motion analysis, walking difficulty evaluation
Abstract

A method based on singular value decomposition (SVD) is proposed for extracting features from motion time-series data observed with various sensing systems. Matrices consisting of the sliding window (SW) subsets of time-series data are decomposed, yielding singular vectors as the patterns of the motion, and the singular values as a scalar, by which the corresponding singular vectors describe the matrices.

The sliding window based singular value decomposition was applied to analyze acceleration during walking. Three levels of walking difficulty were simulated by restricting the right knee joint in the measurement. The accelerations of the middles of the shanks and the back of the waist were measured and normalized before the SW-SVD was performed.The results showed that the first singular values inferred from the acceleration data of the restricted side (the right shank) significantly related to the increase of the restriction among all the subjects while there were no common trends in the singular values of the left shank and the waist. The SW-SVD was suggested to be a reliable method to evaluate walking disability. Furthermore, a 2D visualization tool is proposed to provide intuitive information about walking difficulty which can be used in walking rehabilitation to monitor recovery.

References
  1. [1] A. Kendon, “Gesture: Visible Action as Utterance,” Cambridge University Press, Cambridge, Sep. 2004.
  2. [2] A. L. Hubbard, S. M. Wilson, D. E. Callan, and M. Dapretto, “Giving speech a hand: gesture modulates activity in auditory cortex during speech perception,” Human Brain Mapping, Vol.30, No.3, pp. 1028-1037, Mar. 2009.
  3. [3] L. E. Holt and S. L. Beilock, “Expertise and its embodiment: Examining the impact of sensorimotor skill expertise on the representation of action-related text,” Psychonomic Bulletin& Review, Vol.13, No.4, pp. 694-701, Aug. 2006.
  4. [4] D. C. Spencer, “Habit(us), body techniques and body callusing: An ethnography of mixed martial arts,” Body & Society, Vol.15, No.4, pp. 119-143, Dec. 2009.
  5. [5] S. Furuya, R. Osu, and H. Kinoshita, “Effective utilization of gravity during arm downswing in keystroke by expert pianists,” Neuroscience, Vol.164, No.2, pp. 822-831, Dec. 2009.
  6. [6] D. Gafurov, E. Snekkenes, and P. Bours, “Spoof Attacks on Gait Authentication System,” IEEE Trans. on Information Forensics and Security, Vol.2, No.3, pp. 491-502, Sep. 2007.
  7. [7] T. Takeda, K. Kuramoto, S. Kobashi, and Y. Hata, “A challenge to biometrics by sole pressure while walking,” Proc. 2011 IEEE Int. Conf. on Fuzzy Systems, pp. 1430-1435, June 2011.
  8. [8] D. M. Wolpert, R. C. Miall, and M. Kawato, “Internal models in the cerebellum,” Trends in Cognitive Sciences, Vol.2, No.9, pp. 338-347, Sep. 1998.
  9. [9] H. Imamizu and M. Kawato, “Cerebellar internal models: Implications for the dexterous use of tools,” The Cerebellum, Vol.11, No.2, pp. 325-335, Jun. 2012.
  10. [10] T. M. Hall, F. de Carvalho, and A. Jackson, “A Common Structure Underlies Low-Frequency Cortical Dynamics in Movement, Sleep, and Sedation,” Neuron, Vol.83, No.5, pp. 1185-1199, Sep. 2014.
  11. [11] Y. Nakayama, K. Kudo, and T. Ohtsuki, “Variability and fluctuation in running gait cycle of trained runners and non-runners,” Gait & Posture, Vol.31, No.3, pp. 331-335, Mar. 2010.
  12. [12] R. Williamson, and B. J. Andrews, “Gait event detection for FES using accelerometers and supervised machine learning,” IEEE Trans. on Rehabilitation Engineering, Vol.8, No.3, pp. 312-319, Sep. 2000.
  13. [13] T. Pylvanainen, “Accelerometer based gesture recognition using continuous HMMs,” in Pattern Recognition and Image Analysis, New York: Springer Berlin/Heidelberg, pp. 639-646, Jun. 2005.
  14. [14] J. K. Aggarwal and M. S. Ryoo, “Human activity analysis: A review,” ACM Computing Surveys, Vol.43, No.3, Article 16, 43 pages, 2011.
  15. [15] S. Mitra and T. Acharya, “Gesture recognition: A survey,” IEEE Trans. on Systems, Man, and Cybernetics, Part C, Vol.37, No.3, pp. 311-324, May 2007.
  16. [16] A. D. Wilson and A. F. Bobick, “Parametric hidden Markov models for gesture recognition,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.21, No.9, pp. 884-900, Sep 1999.
  17. [17] J. F. Lichtenauer, E. A. Hendriks, and M. J. T. Reinders, “Sign language recognition by combining statistical DTW and independent classification,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.30, No.11, pp. 2040-2046, Nov. 2008.
  18. [18] M. V. Lamar, M. S. Bhuiyan, and A. Iwata, “Hand gesture recognition using T-CombNET: A new neural network model,” IEICE Trans. on Information and Systems, Vol.E83-D, No.11, pp. 1986-1995, Nov. 2000.
  19. [19] T. E. Jerde, J. F. Soechting, and M. Flanders, “Biological constraints simplify the recognition of hand shapes,” IEEE Trans. on Biomedical Engineering, Vol.50, No.2, pp. 265-269, Feb. 2003.
  20. [20] A. Daffertshofer, C. J. Lamoth, O. G. Meijer, and P. J. Beek, “PCA in studying coordination and variability: A tutorial,” Clinical Biomechanics, Vol.19, No.4, pp. 415-428, May 2004.
  21. [21] T. L. Jakobsen, M. Christensen, S. S. Christensen, M. Olsen, and T. Bandholm, “Reliability of knee joint range of motion and circumference measurements after total knee arthroplasty: Does tester experience matter?,” Physiotherapy Research Int., Vol.15, No.3, pp. 126-134, Sep. 2010.
  22. [22] M. E. Wall, A. Rechtsteiner, and L. M. Rocha, “Singular value decomposition and principal component analysis,” in D. P. Berrar, W. Dubitzky, and M. Granzow (eds.), A Practical Approach to Microarray Data Analysis, pp. 91-109, Kluwer, Norwell, MA 2003.
  23. [23] D. B. Skillicorn, “Understanding Complex Datasets: Data Mining with Matrix Decompositions,” Florida, USA: Chapman and Hall/CRC, May 2007.
  24. [24] H. Nakanishi, S. Kanata, H. Hattori, T. Sawaragi, and Y. Horiguchi, “Extraction of coordinative structures of motions by segmentation using singular spectrum transformation,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.8, pp. 1019-1029, Oct. 2011.
  25. [25] A. Cavallo and P. Falco, “Online Segmentation and Classification of Manipulation Actions From the Observation of Kinetostatic Data,” IEEE Trans. on Human-Machine Systems, Vol.44, No.2, pp.256-269, Apr. 2014.
  26. [26] I. Hayashi, Y. Jiang, and S. Y. Wang, “Acquisition of Embodied Knowledge on Gesture Motion by Singular Spectrum Analysis,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.8, pp. 1011-1018, 2011.
  27. [27] Y. Jiang, I. Hayashi, and S. Wang, “Knowledge acquisition method based on singular value decomposition for human motion analysis,” IEEE Trans. on Knowledge and Data Engineering, Vol.26, No.12, pp.3038-3050, Dec. 2014.
  28. [28] A. Pantelopoulos and N. G. Bourbakis, “A survey on wearable sensor-based systems for health monitoring and prognosis,” IEEE Trans. on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol.40, No.1, pp.1-12, Jan. 2010.
  29. [29] S. Cheng, K. Tom, L. Thomas, and M. Pecht, “A wireless sensor system for prognostics and health management,” IEEE Sensors J., Vol.10, No.4, pp.856-862, Apr. 2010.
  30. [30] J. J. Kavanagh and H. B. Menz, “Accelerometry: a technique for quantifying movement patterns during walking,” Gait & Posture, Vol.28, No.1, pp.1-15, Jul. 2008.
  31. [31] H. Lau and K. Tong, “The reliability of using accelerometer and gyroscope for gait event identification on persons with dropped foot,” Gait and Posture, Vol.27, No.2, pp. 248-257, Feb. 2008.
  32. [32] M. Yoneyama, Y. Kurihara, K. Watanabe, and H. Mitoma, “Accelerometry-based gait analysis and its application to parkinson’s disease assessment - part 2 : a new measure for quantifying walking behavior,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, Vol.21, No.6, pp. 999-1005, Nov. 2013.
  33. [33] M. Iosa, G. Morone, A. Fusco, L. Pratesi, M. Bragoni, P. Coiro, M. Multari, V. Venturiero, D. De Angelis, and S. Paolucci, “Effects of walking endurance reduction on gait stability in patients with stroke,” Stroke Research and Treatment, article ID 810415, 6 pages, 2012.
  34. [34] O. Chuy, Y. Hirata, and K. Kosuge, “A new control approach for a robotic walking support system in adapting user characteristics,” IEEE Trans. on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol.36, No.6, pp. 725-733, Nov. 2006.
  35. [35] K. Wakita, J. Huang, P. Di, K. Sekiyama, and T. Fukuda, “Human-walking-intention-based motion control of an omnidirectional-type cane robot,” IEEE/ASME Trans. on Mechatronics, Vol.18, No.1, pp. 285-296, Feb. 2013.
  36. [36] C. B. Redd and S. J. M. Bamberg, “A wireless sensory feedback device for real-time gait feedback and training,” IEEE/ASME Trans. on Mechatronics, Vol.17, No.3, pp. 425-433, Jun. 2012.
  37. [37] D. Piovesan, P. Morasso, P. Giannoni, and M. Casadio, “Arm stiffness during assisted movement after stroke: the influence of visual feedback and training,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, Vol.21, No.3, pp. 454-465, May 2013.
  38. [38] M. Yoneyama, “Visualising gait symmetry/asymmetry from acceleration data,” Computer Methods in Biomechanics and Biomedical Engineering, Vol.18, No.9, pp. 923-930, Jul. 2015.
  39. [39] R. Moe-Nilssen and J. L. Helbostad, “Estimation of gait cycle characteristics by trunk accelerometry,” J. of Biomechanics, Vol.37, No.1, pp. 121-126, Jan. 2004.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Dec. 12, 2017