Abstract
Understanding the biomechanics of the human foot during each stage of walking is important for the objective evaluation of movement dysfunction, accuracy of diagnosis, and prediction of foot impairment. Extracting causal relations from amongst the muscle activities, toe trajectories, and plantar pressures during walking assists in recognizing several disease conditions, and understanding the hidden complexity of human foot functions, thus, facilitating appropriate therapy and treatment. To extract these relations, we applied the Bayesian Network (BN) model to data collected in the stance phase of walking. For a better understanding of foot function, the experimental data were divided into three stages (initial contact, loading response to mid-stance, and terminal stance to pre-swing). BNs were constructed for these three stages of data for normal walking and simulated hemiplegic walking, then compared and analyzed. Results showed that BNs extracted could express the underlying mechanism of foot function.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Takemura H, Iwama H, Ueda J, Matumoto Y, Ogasawara T (2003) A study of the toe function for human walking. JSME Symp Welfare Eng 3:97–100
Kaapel-Bargas A, Woolf R, Cornwall M, McPoil T (1998) The windlass mechanism during normal walking and passive first metatarsalphalangeal joint extension. Clin Biomech 13(3):190–194
Nishiwaki K, Kagami S, Kuniyoshi Y, Inaba M, Inoue H (2002) Toe joints that enhance bipedal and fullbody motion of humanoid robots. In: Paper presented at the proceedings of the 2002 IEEE international conference on robotics & automation (ICRA02), Washington, DC, pp. 3105—3110, 11–15 May 2002
Hutton WC, Dhanendran M (1979) A Study of the distribution of load under the normal foot during walking. Int Orthop (SICOT) 3(2):153–157
Kong K, Tomizuka M, (2008) Estimation of abnormalities in a human gait using sensor-embedded shoes. In: Paper presented at the proceedings of the 2008 IEEE/ASME international conference on advanced intelligent mechatronics, 2–5 July 2008
Warren GL, Maher RM, Higbie EJ (2004) Temporal patterns of plantar pressures and lower-leg muscle activity during walking: effect of speed. Gait Posture 19:91–100
Nergui M, Murai C, Koike Y, Yu W, Acharya R (2011) Probabilistic information structure of human walking. J Med Syst 35(5):835–844, Springer
Goldberg EJ, Nepture RR (2007) Compensatory strategies during normal walking in response to muscle weakness and increased hip joint stiffness. Gait Posture 25:360–367
Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Mateo, CA
Jensen FV (2001) Bayesian networks and decision graphs. Springer, New York
Nikovski D (2000) Constructing bayesian networks for medical diagnosis from incomplete and partially correct statistics. IEEE Trans Knowl Data Eng 12(4):509–516
Suojanen M, Andreassen S, Olesen KG (2001) A method for diagnosing multiple diseases in MUNIN. IEEE Trans Biomed Eng 48(5):522–532
Meloni A, Ripoli A, Positano V, Landini L (2009) Mutual information preconditioning improves structure learning of bayesian networks from medical databases. IEEE Trans Inf Technol Biomed 13(6):984–989
Meloni A, Landini L, Ripoli A, Positano V (2009) Improved learning of bayesian networks in biomedicine. Paper presented at the Ninth international conference on intelligent systems design and applications, 30 Nov–2 Dec 2009
Rose C, Smaili C, Charpillet F (2005) A dynamic bayesian network for handling uncertainty in a decision support system adapted to the monitoring of patients treated by hemodialysis. Paper presented at the proceedings of the 17th IEEE international conference on tools with artificial intelligence (ICTAI’05), 14–16 Nov 2005
Nan B, Okamoto M, Tsuji T (2009) A hybrid motion classification approach for EMG-based human—robot interfaces using bayesian and neural networks. IEEE Trans Rob 25(3):502–511
Athanasiou M, Clark JY (2007) A bayesian network model for the diagnosis of the caring procedure for wheelchair users with spinal injury. Paper presented at the twentieth IEEE international symposium on computer-based medical systems (CBMS’07), 20–22 June 2007
Yu W, Yamaguchi H, Maruishi M, Yokoi H, Mano Y, Kakazu Y (2002) EMG automatic switch for FES control for hemiplegics using artificial neural network. Elsevier. Robot Auton Syst 40(2):213–224
Ebara K, Ohashi M, Kubota T (1999) Rehabilitation program for walking impairment. Ishiyaku Publishers Inc, Tokyo
Neapolitan RE (2004) Learning bayesian networks. Prentice Hall, New Jersey
Bøttcher SG, Dethlefsen C (2003) Learning bayesian networks with R third Edition. In: Paper presented at the proceedings of the international workshop on distributed statistical computing, 20–22 Mar 2003
Cheng C, Ansari R, Khokhar A (2004) Cyclic articulated human motion tracking by sequential ancestral simulation. Paper presented at the IEEE computer society conference on computer vision and pattern recognition (CVPR’04), 27 June–2 July 2004
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Nergui, M., Inoue, J., Chieko, M., Yu, W., Acharya, U.R. (2014). Understanding Foot Function During Stance Phase by Bayesian Network Based Causal Inference. In: Dua, S., Acharya, U., Dua, P. (eds) Machine Learning in Healthcare Informatics. Intelligent Systems Reference Library, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40017-9_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-40017-9_6
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40016-2
Online ISBN: 978-3-642-40017-9
eBook Packages: EngineeringEngineering (R0)