Abstract
We study the problem of analyzing and classifying frontal view human gait data by registration and modeling on a video data. In this study, we suppose that frontal view gait data as a mixing of scale changing, human movements and speed changing parameter. Our gait model is based on human gait structure and temporal-spatial relations between camera and subject. To demonstrate the effectiveness of our method, we conducted two sets of experiments, assessing the proposed method in gait analysis for young/elderly person and abnormal gait detecting. In abnormal gait detecting experiment, we apply K-NN classifier, using the estimated parameters, to perform normal/abnormal gait detect, and present results from an experiment involving 120 subjects (young person), and 60 subjects (elderly person). As a result, our method shows high detection rate.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Akaike H (1973) Information theory and an extension of the maximum likelihood principle. International symposium on information theory, pp 267–281
Barnich O, Droogenbroeck MV (2009) Frontal-view gait recognition by intra-and inter-frame rectangle size distribution. Pattern Recogn Lett 30:893–901
Borel S, Schneider P, Newman CJ (2011) Video analysis software increases the interrater reliability of video gait assessments in children with cerebral palsy. Gait Posture 33(4):727–729
Burnham KP, Anderson DR (2010) Model selection and multi-model inference: a practical information-theoretic approach. Springer, New York
Collins SH, Adamczyk PG, Kuo AD (2009) Dynamic arm swinging in human walking. Proc R Soc B: Biol Sci 276(1673):3679–3688
Gage JR (1982) Gait analysis for decision-making in cerebral palsy. Bull Hosp Jt Dis Orthop Inst 43(2):147–163
Grunt S, van Kampen PJ, Krogt MM, Brehm MA, Doorenbosch CAM, Becher JG (2010) Reproducibility and validity of video screen measurements of gait in children with spastic cerebral palsy. Gait Posture 31(4):489–494
Kadaba MP, Ramakrishnan HK, Wootten ME (1990) Measurement of lower extremity kinematics during level walking. J Orthop Res 8(3):383–392
Kirtley C (2006) Clinical gait analysis: theory and practice, 1e, 1 edn. Churchill Livingstone
Lee TKM, Belkhatir M, Lee PA (2008) Fronto-normal gait incorporating accurate practical looming compensation. Pattern Recogn
Okusa K, Kamakura T (2012) Normal/Abnormal gait analysis based on the statistical registration and modeling of the frontal view gait data. Lecture notes in engineering and computer science: proceedings of the world congress on engineering and computer science (2012) WCECS 2012, 24–26 Oct 2012. USA, San Francisco, pp 443–448
Okusa K, Kamakura T (2012) A statistical registration of scale changing and moving objects with application to the human gait analysis. (in Japanese). Bull Jpn Soc Comput Statist 24(2)
Okusa K, Kamakura T (2012) Statistical registration and modeling of frontal view gait data with application to the human recognition. In: International conference on computer statistics (COMPSTAT 2012), pp 677–688
Okusa K, Kamakura T, Murakami H (2011) A statistical registration of scales of moving objects with application to walking data (in Japanese). Bull Jpn Soc Comput Statist 23(2):94–111
Olshen RA, Biden EN, Wyatt MP, Sutherland DH (1989) Gait analysis and the bootstrap. Ann Statist, pp 1419–1440
Perry J, Burnfield J (2010) Gait analysis: normal and pathological function. Slack Incorporated
Soriano M, Araullo A, Saloma C (2004) Curve spreads-a biometric from front-view gait video. Pattern Recogn Lett 25(14):1595–1602
Sugiura N (1978) Further analysts of the data by Akaike’s information criterion and the finite corrections. Commun Statist Theory Methods
Whittle MW (1991) Gait analysis: an introduction. Butterworth-Heinemann
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Okusa, K., Kamakura, T. (2014). Human Gait Modeling and Statistical Registration for the Frontal View Gait Data with Application to the Normal/Abnormal Gait Analysis. In: Kim, H., Ao, SI., Amouzegar, M., Rieger, B. (eds) IAENG Transactions on Engineering Technologies. Lecture Notes in Electrical Engineering, vol 247. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6818-5_37
Download citation
DOI: https://doi.org/10.1007/978-94-007-6818-5_37
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-6817-8
Online ISBN: 978-94-007-6818-5
eBook Packages: EngineeringEngineering (R0)