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Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 343084, 7 pages
http://dx.doi.org/10.1155/2013/343084
Research Article

SVM versus MAP on Accelerometer Data to Distinguish among Locomotor Activities Executed at Different Speeds

Department of Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy

Received 18 July 2013; Revised 7 October 2013; Accepted 18 October 2013

Academic Editor: Strahinja Dosen

Copyright © 2013 Maurizio Schmid et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Patel, H. Park, P. Bonato, L. Chan, and M. Rodgers, “A review of wearable sensors and systems with application in rehabilitation,” Journal of NeuroEngineering and Rehabilitation, p. 21, 2012. View at Publisher · View at Google Scholar · View at Scopus
  2. J. J. Kavanagh and H. B. Menz, “Accelerometry: a technique for quantifying movement patterns during walking,” Gait and Posture, vol. 28, no. 1, pp. 1–15, 2008. View at Publisher · View at Google Scholar · View at Scopus
  3. K. Tong and M. H. Granat, “A practical gait analysis system using gyroscopes,” Medical Engineering and Physics, vol. 21, no. 2, pp. 87–94, 1999. View at Publisher · View at Google Scholar · View at Scopus
  4. K. J. O'Donovan, R. Kamnik, D. T. O'Keeffe, and G. M. Lyons, “An inertial and magnetic sensor based technique for joint angle measurement,” Journal of Biomechanics, vol. 40, no. 12, pp. 2604–2611, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. A. K. Bourke, J. V. O'Brien, and G. M. Lyons, “Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm,” Gait and Posture, vol. 26, no. 2, pp. 194–199, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. R. P. Troiano, D. Berrigan, K. W. Dodd, L. C. Mâsse, T. Tilert, and M. Mcdowell, “Physical activity in the United States measured by accelerometer,” Medicine and Science in Sports and Exercise, vol. 40, no. 1, pp. 181–188, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. D. M. Karantonis, M. R. Narayanan, M. Mathie, N. H. Lovell, and B. G. Celler, “Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring,” IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 1, pp. 156–167, 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. R. Muscillo, S. Conforto, M. Schmid, P. Caselli, and T. D'Alessio, “Classification of motor activities through derivative dynamic time warping applied on accelerometer data,” in Proceedings of the 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society (EMBC '07), pp. 4930–4933, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. C. V. C. Bouten, W. P. H. G. Verboeket-van de Venne, K. R. Westerterp, M. Verduin, and J. D. Janssen, “Daily physical activity assessment: comparison between movement registration and doubly labeled water,” Journal of Applied Physiology, vol. 81, no. 2, pp. 1019–1026, 1996. View at Google Scholar · View at Scopus
  10. E. P. Meijer, A. H. C. Goris, L. Wouters, and K. R. Westerterp, “Physical inactivity as a determinant of the physical activity level in the elderly,” International Journal of Obesity, vol. 25, no. 7, pp. 935–939, 2001. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Yoshioka, M. Ayabe, T. Yahiro et al., “Long-period accelerometer monitoring shows the role of physical activity in overweight and obesity,” International Journal of Obesity, vol. 29, no. 5, pp. 502–508, 2005. View at Publisher · View at Google Scholar · View at Scopus
  12. C. V. C. Bouten, K. R. Westerterp, M. Verduin, and J. D. Janssen, “Assessment of energy expenditure for physical activity using a triaxial accelerometer,” Medicine and Science in Sports and Exercise, vol. 26, no. 12, pp. 1516–1523, 1994. View at Google Scholar · View at Scopus
  13. K. Y. Chen and M. Sun, “Improving energy expenditure estimation by using a triaxial accelerometer,” Journal of Applied Physiology, vol. 83, no. 6, pp. 2112–2122, 1997. View at Google Scholar · View at Scopus
  14. J. Staudenmayer, D. Pober, S. Crouter, D. Bassett, and P. Freedson, “An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer,” Journal of Applied Physiology, vol. 107, no. 4, pp. 1300–1307, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. Y. Hao and R. Foster, “Wireless body sensor networks for health-monitoring applications,” Physiological Measurement, vol. 29, no. 11, pp. R27–R56, 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. E. Jovanov, A. Milenkovic, C. Otto, and P. C. De Groen, “A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation,” Journal of NeuroEngineering and Rehabilitation, vol. 2, article 6, 2005. View at Publisher · View at Google Scholar · View at Scopus
  17. D. Hendelman, K. Miller, C. Baggett, E. Debold, and P. Freedson, “Validity of accelerometry for the assessment of moderate intensity physical activity in the field,” Medicine and Science in Sports and Exercise, vol. 32, no. 9, pp. S442–S449, 2000. View at Google Scholar · View at Scopus
  18. S. J. Preece, J. Y. Goulermas, L. P. J. Kenney, and D. Howard, “A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data,” IEEE Transactions on Biomedical Engineering, vol. 56, no. 3, pp. 871–879, 2009. View at Publisher · View at Google Scholar · View at Scopus
  19. S. J. Preece, J. Y. Goulermas, L. P. J. Kenney, D. Howard, K. Meijer, and R. Crompton, “Activity identification using body-mounted sensors—a review of classification techniques,” Physiological Measurement, vol. 30, no. 4, pp. R1–R33, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. S. Pirttikangas, K. Fujinami, and T. Nakajima, “Feature selection and activity recognition from wearable sensors,” in Proceedings of the 3rd International Symposium on Ubiquitous Computing Systems, vol. 4239 of Lecture Notes in Computer Science, pp. 516–527, Seoul, Korea, 2006. View at Scopus
  21. P. H. Veltink, H. B. J. Bussmann, W. De Vries, W. L. J. Martens, and R. C. Van Lummel, “Detection of static and dynamic activities using uniaxial accelerometers,” IEEE Transactions on Rehabilitation Engineering, vol. 4, no. 4, pp. 375–385, 1996. View at Publisher · View at Google Scholar · View at Scopus
  22. R. Muscillo, M. Schmid, S. Conforto, and T. D'Alessio, “Early recognition of upper limb motor tasks through accelerometers: real-time implementation of a DTW-based algorithm,” Computers in Biology and Medicine, vol. 41, no. 3, pp. 164–172, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. B. Najafi, K. Aminian, A. Paraschiv-Ionescu, F. Loew, C. J. Büla, and P. Robert, “Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly,” IEEE Transactions on Biomedical Engineering, vol. 50, no. 6, pp. 711–723, 2003. View at Publisher · View at Google Scholar · View at Scopus
  24. J. Pärkkä, M. Ermes, P. Korpipää, J. Mäntyjärvi, J. Peltola, and I. Korhonen, “Activity classification using realistic data from wearable sensors,” IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 1, pp. 119–128, 2006. View at Publisher · View at Google Scholar · View at Scopus
  25. D. Wu, K. Warwick, Z. Ma et al., “Prediction of parkinson's disease tremor onset using a radial basis function neural network based on particle swarm optimization,” International Journal of Neural Systems, vol. 20, no. 2, pp. 109–116, 2010. View at Publisher · View at Google Scholar · View at Scopus
  26. F. Riganti Fulginei and A. Salvini, “Hysteresis model identification by the flock-of-starlings optimization,” International Journal of Applied Electromagnetics and Mechanics, vol. 30, no. 3-4, pp. 321–331, 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. S. Conforto and T. D'Alessio, “Real time monitoring of muscular fatigue from dynamic surface myoelectric signals using a complex covariance approach,” Medical Engineering and Physics, vol. 21, no. 4, pp. 225–234, 1999. View at Publisher · View at Google Scholar · View at Scopus
  28. R. Muscillo, M. Schmid, S. Conforto, and T. D'Alessio, “An adaptive Kalman-based Bayes estimation technique to classify locomotor activities in young and elderly adults through accelerometers,” Medical Engineering and Physics, vol. 32, no. 8, pp. 849–859, 2010. View at Publisher · View at Google Scholar · View at Scopus
  29. J. W. Sammon, “A non-linear mapping for data structure analysis,” IEEE Transactions on Computers, no. 5, pp. 401–4099, 1969. View at Google Scholar
  30. F. R. Fulginei, A. Laudani, A. Salvini, and M. Parodi, “Automatic and parallel optimized learning for neural networks performing mimo applications,” Advances in Electrical and Computer Engineering, vol. 13, no. 1, pp. 3–12, 2013. View at Google Scholar
  31. F. R. Fulginei, A. Salvini, and M. Parodi, “Learning optimization of neural networks used for MIMO applications based on multivariate functions decomposition,” Inverse Problems in Science and Engineering, vol. 20, no. 1, pp. 29–39, 2012. View at Publisher · View at Google Scholar · View at Scopus
  32. F. R. Fulginei and A. Salvini, “Neural network approach for modelling hysteretic magnetic materials under distorted excitations,” IEEE Transactions on Magnetics, vol. 48, no. 2, pp. 307–310, 2012. View at Publisher · View at Google Scholar · View at Scopus
  33. D. Marquardt, “An algorithm for least-squares estimation of nonlinear parameters,” SIAM Journal on Applied Mathematics, vol. 11, pp. 431–441, 1963. View at Google Scholar
  34. M. Gneo, M. Schmid, S. Conforto, and T. D'Alessio, “A free geometry model-independent neural eye-gaze tracking system,” Journal of Neuroengineering and Rehabilitation, vol. 9, p. 82, 2012. View at Publisher · View at Google Scholar
  35. G. Capizzi, S. Coco, C. Giuffrida, and A. Laudani, “A neural network approach for the differentiation of numerical solutions of 3-D electromagnetic problems,” IEEE Transactions on Magnetics, vol. 40, no. 2, pp. 953–956, 2004. View at Publisher · View at Google Scholar · View at Scopus
  36. P. Del Vecchio and A. Salvini, “Neural Network and Fourier Descriptor macromodeling dynamic hysteresis,” IEEE Transactions on Magnetics, vol. 36, no. 4 I, pp. 1246–1249, 2000. View at Publisher · View at Google Scholar · View at Scopus
  37. A. Salvini and C. Coltelli, “Prediction of dynamic hysteresis under highly distorted exciting fields by neural networks and actual frequency transplantation,” IEEE Transactions on Magnetics, vol. 37, no. 5 I, pp. 3315–3319, 2001. View at Publisher · View at Google Scholar · View at Scopus
  38. B. F. Mitchell, V. F. Dem'yanov, and V. N. Malozemov, “Finding the point of a polyhedron closest to the origin,” SIAM Journal on Control, vol. 12, no. 1, pp. 19–26, 1974. View at Publisher · View at Google Scholar · View at Scopus
  39. D. Cai, X. He, and J. Han, “Document clustering using locality preserving indexing,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 12, pp. 1624–1637, 2005. View at Publisher · View at Google Scholar · View at Scopus
  40. H. Chan, M. Yang, H. Wang et al., “Assessing gait patterns of healthy adults climbing stairs employing machine learning techniques,” International Journal of Intelligent Systems, vol. 28, pp. 257–270, 2013. View at Google Scholar