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Phantom: Prediction of Human Motion with Distributed Body Sensors

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Book cover Prediction and Classification of Respiratory Motion

Part of the book series: Studies in Computational Intelligence ((SCI,volume 525))

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Abstract

Tracking human motion with distributed body sensors has the potential to promote a large number of applications such as health care, medical monitoring, and sports medicine. In distributed sensory systems, the system architecture and data processing cannot perform the expected outcomes because of the limitations of data association. For the collaborative and complementary applications of motion tracking (Polhemus Liberty AC magnetic tracker), we propose a distributed sensory system with multi-channel interacting multiple model estimator (MC-IMME). To figure out interactive relationships among distributed sensors, we used a Gaussian mixture model (GMM) for clustering. With a collaborative grouping method based on GMM and expectation-maximization (EM) algorithm for distributed sensors, we can estimate the interactive relationship of multiple sensor channels and achieve the efficient target estimation to employ a tracking relationship within a cluster. Using multiple models with improved control of filter divergence, the proposed MC-IMME can achieve the efficient estimation of the measurement as well as the velocity from measured datasets with distributed sensory data. We have newly developed MC-IMME to improve overall performance with a Markov switch probability and a proper grouping method. The experiment results showed that the prediction overshoot error can be improved in the average 19.31 % with employing a tracking relationship.

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References

  1. S.J. Lee, Y. Motai, M. Murphy, Respiratory motion estimation with hybrid implementation of extended kalman filter. IEEE Trans. Ind. Electron. PP(99), (2011)

    Google Scholar 

  2. H. Ghasemzadeh, R. Jafari, Physical movement monitoring using body sensor networks: a phonological approach to construct spatial decision trees. IEEE Trans Ind Inform 7(1), 66–77 (2011)

    Article  Google Scholar 

  3. H. Chen, Yo. Li, Enhanced particles with Pseudolikelihoods for three-dimensional tracking. IEEE Trans. Ind. Electron. 56(8), 2992–2997 (2009)

    Google Scholar 

  4. D. Smith, S. Singh, Approaches to multisensor data fusion in target tracking: a survey. IEEE Trans Knowl Data Eng 18(12), 1696–1710 (2006)

    Article  Google Scholar 

  5. D. Naso, B. Turchiano, P. Pantaleo, A Fuzzy-Logic based optical sensor for online weld defect-detection. IEEE Trans Ind Inform 1(4), 259–273 (2005)

    Article  Google Scholar 

  6. T. Mukai, M. Ishikawa, An active sensing method using estimated errors for multisensor fusion systems. IEEE Trans Ind Electro. 43(3), 380–386 (1996)

    Article  Google Scholar 

  7. J. Liu, J. Liu, M. Chu, J. Liu, J. Reich, F. Zhao, Distributed state representation for tracking problems in sensor networks, in Proceeding of Information Processing in Sensor Networks, pp. 234–242 (2004)

    Google Scholar 

  8. K. Zhou, S.I. Roumeliotis, Optimal motion strategies for range-only constrained multisensor target tracking. IEEE Trans Robot 24(5), 1168–1185 (2008)

    Article  Google Scholar 

  9. J.G. García, J.G. Ortega, A.S. García, S.S. Martínez, Robotic software architecture for multisensor fusion system. IEEE Trans Ind Electron 56(3), 766–777 (2009)

    Article  Google Scholar 

  10. N. Bellotto, H. Hu, Multisensor-based human detection and tracking for mobile service robots. IEEE Trans. Syst. Man Cybern. B Cybern. 39(1), 167–181 (2009)

    Google Scholar 

  11. T. Kirubarajan, H. Wang, Y. Bar-Shalom, K.R. Pattipati, Efficient multisensor fusion using multidimensional data association. IEEE Trans Aerosp Electron Syst 37(2), 386–400 (2001)

    Article  Google Scholar 

  12. Z. Khan, T. Balch, F. Dellaert, MCMC data association and sparse factorization updating for real time multitarget tracking with merged and multiple measurements. IEEE Trans Pattern Anal Mach Intel 28(12), 1960–1972 (2006)

    Article  Google Scholar 

  13. L. Hong, S. Cong, D. Wicker, Distributed multirate interacting multiple model fusion (DMRIMMF) with application to out-of-sequence GMTI data. IEEE Trans. Autom. Control 49(1), 102–107 (2004)

    Google Scholar 

  14. S Blackman, R. Popoli, Design and Analysis of Modern Tracking Systems (Artech House, Boston, 1999)

    Google Scholar 

  15. P.R. Poulsenb, B. Cho, A. Sawant, D. Ruan, P.J. Keall, Detailed analysis of latencies in image-based dynamic MLC tracking. Med Phys 37(9), 4998–5005 (2010)

    Article  Google Scholar 

  16. X.R. Li, V.P. Jilkov, Survey of maneuvering target tracking—Part V: multiple model methods. IEEE Trans Aerosp Electron Syst 41(4), 1255–1321 (2005)

    Article  Google Scholar 

  17. E. Mazor, A. Averbuch, Y. Bar-Shalom, J. Dayan, Interacting multiple model methods in target tracking: a survey. IEEE Trans Aerosp Electron Syst 34(1), 103–123 (1998)

    Article  Google Scholar 

  18. A.K. Jana, A Hybrid FLC-EKF scheme for temperature control of a refinery debutanizer column. IEEE Trans Ind Inform 6(1), 25–35 (2010)

    Article  Google Scholar 

  19. L.C. Yang, J.H. Yang, E.M. Feron, Multiple model estimation for improving conflict detection algorithms. IEEE Conf Syst Man Cybern 1, 242–249 (2004)

    Google Scholar 

  20. H. Bom, Y. Bar-Shalom, The interacting multiple model algorithm for systems with Markovian switching coefficients. IEEE Trans Autom Control 33(8), 780–783 (1988)

    Article  Google Scholar 

  21. L. Campo, P. Mookerjee, Y. Bar-Shalom, State estimation for systems with sojourn-time-dependent markov model switching. IEEE Trans Autom Control 36(2), 238–243 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  22. X.R. Li, Y. Zhang, Numerically robust implementation of multiple-model algorithms. IEEE Trans Aerosp Electron Syst 36(1), 266–278 (2000)

    Article  Google Scholar 

  23. X.R. Li, Z. Zhao, X. Li, General model-set design methods for multiple-model approach. IEEE Trans Autom Control 50(9), 1260–1276 (2005)

    Article  Google Scholar 

  24. L. Hong, Multirate interacting multiple model filtering for target tracking using multirate models. IEEE Trans Autom Control 44(7), 1326–1340 (1999)

    Article  MATH  Google Scholar 

  25. W. Farrell, Interacting multiple model filter for tactical ballistic missile tracking. IEEE Trans Aerosp Electron Syst. 44(2), 418–426 (2008)

    Article  Google Scholar 

  26. X.R. Li, Y. Bar-Shalom, Performance prediction of the interacting multiple model algorithm. IEEE Trans Aerosp Electron Syst 29(3), 755–771 (1993)

    Article  Google Scholar 

  27. P. Drineas, A. Frieze, R. Kannan, S. Vempala, V. Vinay, Clustering large graphs via the singular value decomposition. Mach Learn 56, 9–33 (2004)

    Article  MATH  Google Scholar 

  28. L. Xu, How many clusters? A YING–YANG machine based theory for a classical open problem in pattern recognition. Proc IEEE Int Conf Neural Netw 3, 1546–1551 (1996)

    Google Scholar 

  29. Xu Lei, Bayesian Ying-Yang machine, clustering and number of clusters. Pattern Recogn Lett 18, 1167–1178 (1997)

    Article  Google Scholar 

  30. P. Guo, C.L. Philip, M.R. Lyu, Cluster number selection for a small set of samples using the Bayesian Ying-Yang model. IEEE Trans Neural Netw 13(3), 757–763 (2002)

    Article  Google Scholar 

  31. Nikos Vlassis, Aristidis Likas, A Greedy EM algorithm for gaussian mixture learning. Neural Process Lett 15(1), 77–87 (2002)

    Article  MATH  Google Scholar 

  32. F. Pernkopf, D. Bouchaffra, Genetic-based EM algorithm for learning Gaussian mixture models. IEEE Trans Pattern Anal Mach Intel 27(8), 1344–1348 (2005)

    Article  Google Scholar 

  33. T. Kanungo, D.M. Mount, N.S. Netanyahu, C.D. Piatko, R. Silverman, A.Y. Wu, An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans Pattern Anal Mach Intel 24(7), 881–892 (2002)

    Article  Google Scholar 

  34. S.P. Chatzis, D.I. Kosmopoulos, T.A. Varvarigou, Robust sequential data modeling using an outlier tolerant hidden markov model. IEEE Trans Pattern Anal Mach Intel 31(9), 1657–1669 (2009)

    Article  Google Scholar 

  35. S. Har-Peled, B. Sadri, How fast is the k-means method? Algorithmica 41(3), 185–202 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  36. R. Nock, F. Nielsen, On weighting clustering. IEEE Trans Pattern Anal Mach Intel 28(8), 1223–1235 (2006)

    Article  Google Scholar 

  37. A.Y. Ng, M.I. Jordan, Y. Weiss, On spectral clustering: analysis and an algorithm. Adv Neural Inf Process 14, 849–856 (2002)

    Google Scholar 

  38. U. von Luxburg, A tutorial on spectral clustering. Stat Comput 17(4), 395–416 (2007)

    Article  MathSciNet  Google Scholar 

  39. F. Caron, M. Davy, A. Doucet, E. Duflos, P. Vanheeghe, Bayesian inference for dynamic models with Dirichlet process mixtures. IEEE Trans Signal Process 56(1), 71–84 (2008)

    Article  MathSciNet  Google Scholar 

  40. S. Kim, P. Smyth, H. Stern, A Bayesian Mixture approach to modeling spatial activation patterns in multisite fMRI data. IEEE Trans Med Imag 29(6), 1260–1274 (2010)

    Article  Google Scholar 

  41. M. Ramoni, P. Sebastiani, P. Cohen, Bayesian clustering by dynamics. Mach Learn 47(1), 91–121 (2001)

    Article  Google Scholar 

  42. R.L. Streit, P.K. Willett, Detection of random transient signals via hyperparameter estimation. IEEE Trans Signal Process 47(7), 1823–1834 (1999)

    Article  Google Scholar 

  43. Y. Bar-Shalom, X.R. Li, T. Kirubarajan, Estimation with Applications to Tracking and Navigation (Wiley, New York, 2001)

    Google Scholar 

  44. R. Karlsson, T. Schön, F. Gustafsson, Complexity analysis of the marginalized particle filter. IEEE Trans Signal Process 53(11), 4408–4411 (2005)

    Article  MathSciNet  Google Scholar 

  45. H. Himberg, Y. Motai, Head orientation prediction: delta quaternions versus quaternions. IEEE Trans. Syst. Man Cybern. B Cybern. 39(6), 1382–1392 (2009)

    Google Scholar 

  46. M.H. Kim, S. Lee, K.C. Lee, Kalman predictive redundancy system for fault tolerance of safety-critical systems. IEEE Trans Ind Inform 6(1), 46–53 (2010)

    Article  Google Scholar 

  47. V.P. Jilkov, X.R. Li, Bayesian estimation of transition probabilities for markovian jump systems by stochastic simulation. IEEE Trans. Signal Process. 52(6), 1620–1630 (2004)

    Google Scholar 

  48. R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification (Wiley, New York, 2001)

    Google Scholar 

  49. G. McLachlan, D. Peel, Finite Mixture Models (Wiley, New York, 2000)

    Google Scholar 

  50. A.P. Dempster, N.M. Laird, D.B. Rubin, Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Series B 39(1), 1–38 (1977)

    MathSciNet  MATH  Google Scholar 

  51. G. J. McLachlan and K. E. Basford, Mixture Models: Inference and applications to clustering, Marcel Dekker, 1988

    Google Scholar 

  52. J. Kleinberg, É. Tardos, Chap. 2, Algorithm Design (Pearson Education, Botson, 2006)

    Google Scholar 

  53. H. Shirato, S. Shimizu, K. Kitamura, T. Nishioka, K. Kagei, S. Hashimoto, H. Aoyama, T. Kunieda, N. Shinohara, H. Dosaka-Akita, K. Miyasaka, Four-dimensional treatment planning and fluoroscopic real-time tumor tracking radiotherapy for moving tumor. Int J Radiat Oncol Biol Phys 48(2), 435–442 (2000)

    Article  Google Scholar 

  54. http://www.polhemus.com

  55. P. Orbanz, Y.W. Teh, in Bayesian Nonparametric Models, eds. by C. Sammut, G.I. Webb. Encyclopedia of Machine Learning (Springer, Berlin, 2010)

    Google Scholar 

  56. K.P. Burnham, D.R. Anderson, Model selection and multi-model inference: a practical information-theoretic approach (Springer, New York, 2002)

    Google Scholar 

  57. J.G. Ramírez, Statistical intervals: confidence, prediction, enclosure. SAS Institute Inc., white paper, 2009

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Texas A&M University—Texarkana, Texarkana, TX, USA

    Suk Jin Lee

  2. Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA, USA

    Yuichi Motai

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  1. Suk Jin Lee
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  2. Yuichi Motai
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Correspondence to Suk Jin Lee .

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Lee, S.J., Motai, Y. (2014). Phantom: Prediction of Human Motion with Distributed Body Sensors. In: Prediction and Classification of Respiratory Motion. Studies in Computational Intelligence, vol 525. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41509-8_3

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  • DOI: https://doi.org/10.1007/978-3-642-41509-8_3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41508-1

  • Online ISBN: 978-3-642-41509-8

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