Skip to main content
SpringerLink
Log in

Classification of Normal and Pathological Gait in Young Children Based on Foot Pressure Data

  • Original Article
  • Published:
Neuroinformatics Aims and scope Submit manuscript

Abstract

Human gait recognition, an active research topic in computer vision, is generally based on data obtained from images/videos. We applied computer vision technology to classify pathology-related changes in gait in young children using a foot-pressure database collected using the GAITRite walkway system. As foot positioning changes with children’s development, we also investigated the possibility of age estimation based on this data. Our results demonstrate that the data collected by the GAITRite system can be used for normal/pathological gait classification. Combining age information and normal/pathological gait classification increases the accuracy of the classifier. This novel approach could support the development of an accurate, real-time, and economic measure of gait abnormalities in children, able to provide important feedback to clinicians regarding the effect of rehabilitation interventions, and to support targeted treatment modifications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Aggarwal, J., & Ryoo, M. (2011). Human activity analysis: A review. ACM Computing Surveys, 43(3), 16:1–16:43.

    Article  Google Scholar 

  • Alam, M., & Hamida, E. (2014). Surveying wearable human assistive technology for life and safety critical applications: Standards, challenges and opportunities. Sensors, 14(5), 9153–9209.

    Article  PubMed  PubMed Central  Google Scholar 

  • Beck, R., Andriacchi, T., Kuo, K., Fermier, R., & Galante, J. (1981). Changes in the gait patterns of growing children. Journal of Bone and Joint Surgery, 63(9), 1452–1457.

    Article  CAS  PubMed  Google Scholar 

  • Belda-Lois, J.M., del Horno, S.M., Bermejo-Bosch, I., Moreno, J.C., Pons, J.L., Farina, D., Iosa, M., Molinari, M., Tamburella, F., Ramos, A., Caria, A., Solis-Escalante, T., Brunner C., & Rea, M. (2011). Rehabilitation of gait after stroke: A review towards a top-down approach. Journal of neuroengineering and rehabilitation, 8, 66.

    Article  PubMed  PubMed Central  Google Scholar 

  • Bilney, B., Morris, M., & Webster, K. (2003). Concurrent related validity of the gaitrite® walkway system for quantification of the spatial and temporal parameters of gait. Gait & posture, 17(1), 68–74.

    Article  Google Scholar 

  • Bladen, M., Alderson, L., Khair, K., Liesner, R., Green, J., & Main, E. (2007). Can early subclinical gait changes in children with haemophilia be identified using the gaitrite® walkway. Haemophilia, 13(5), 542–547.

    Article  CAS  PubMed  Google Scholar 

  • Bonato, P. (2005). Advances in wearable technology and applications in physical medicine and rehabilitation. Journal of NeuroEngineering and Rehabilitation, 2(1), 2.

    Article  PubMed  PubMed Central  Google Scholar 

  • Boulay, B., Brémond, F., & Thonnat, M. (2006). Applying 3d human model in a posture recognition system. Pattern Recognition Letters, 27(15), 1788–1796.

    Article  Google Scholar 

  • Breiman, L. (2001). Random forests. Machine learning, 45(1), 5–32.

    Article  Google Scholar 

  • Cai, D., He, X., Han, J., & Zhang, H. (2006). Orthogonal laplacianfaces for face recognition. Image Processing. Transactions on IEEE, 15(11), 3608–3614.

    Google Scholar 

  • Cook, R.E., Schneider, I., Hazlewood, M.E., Hillman, S.J., & Robb, J.E. (2003). Gait analysis alters decision-making in cerebral palsy. Journal of pediatric orthopedics, 23(3), 292–5.

    PubMed  Google Scholar 

  • Crea, S., Donati, M., De Rossi, S.M.M., Oddo, C.M., & Vitiello, N. (2014). A wireless flexible sensorized insole for gait analysis. Sensors, 14(1), 1073–1093.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Dusing, S., & Thorpe, D. (2007). A normative sample of temporal and spatial gait parameters in children using the gaitrite electronic walkway. Gait & posture, 25(1), 135–139.

    Article  Google Scholar 

  • Gafurov, D. (2007). A survey of biometric gait recognition: Approaches, security and challenges. In Annual Norwegian Computer Science Conference, pp 19–21.

  • GAITRite, C. (2011). Gaitrite operating manual. In Havertown: CIR Systems, MAP/CIR Inc.

  • Guo, G., Li, S., & Chan, K. (2000). Face recognition by support vector machines. In Automatic Face and Gesture Recognition, IEEE, pp 196–201.

  • Hamers, F.P.T., Koopmans, G.C., & Joosten, E.A.J. (2006). Catwalkassisted gait analysis in the assessment of spinal cord injury. Journal of neurotrauma, 23(3), 537–48.

    Article  PubMed  Google Scholar 

  • Hartley, R., & Zisserman, A. (2003). Multiple view geometry in computer vision: Cambridge university press.

  • de-la Herran, A.M., Garcia-Zapirain, B., & Mendez-Zorrilla, A. (2014). Gait analysis methods: An overview of wearable and non-wearable systems, highlighting clinical applications. Sensors, 14(2), 3362–3394.

    Article  Google Scholar 

  • Kale, A., Sundaresan, A., Rajagopalan, A., Cuntoor, N., Roy-Chowdhury, A., Kruger, V., & Chellappa, R. (2004). Identification of humans using gait. Image Processing, 13(9), 1163–1173.

    Article  Google Scholar 

  • Kressig, R., & Beauchet, O. (2006). Guidelines for clinical applications of spatio-temporal gait analysis in older adults. Aging clinical and experimental research, 18(2), 174–176.

    Article  PubMed  Google Scholar 

  • Lance, J. (1980). Symposium synopsis. Spasticity: disordered motor control.

  • Law, M., King, G., Russell, D., MacKinnon, E., Hurley, P., & Murphy, C. (1999). Measuring outcomes in children’s rehabilitation: a decision protocol. Archives of physical medicine and rehabilitation, 80(6), 629–36.

    Article  CAS  PubMed  Google Scholar 

  • Leonard, C., & Hirschfeld, H. (1995). Myotatic reflex responses of non-disabled children and children with spastic cerebral palsy. Developmental Medicine & Child Neurology, 37(9), 783–799.

    Article  CAS  Google Scholar 

  • Linden, M., & Bjorkman, M. (2013). Embedded sensor systems for health-providing the tools in future healthcare. Studies in health technology and informatics, 200, 161–163.

    Google Scholar 

  • Liu, C., Nakashima, K., Sako, H., & Fujisawa, H. (2003). Handwritten digit recognition: benchmarking of state-of-the-art techniques. Pattern Recognition, 36(10), 2271–2285.

    Article  Google Scholar 

  • Majnemer, A. (2010). Benefits of using outcome measures in pediatric rehabilitation. Physical & occupational therapy in pediatrics, 30(3), 165–7.

    Article  Google Scholar 

  • Menz, H., Latt, M., Tiedemann, A., Mun San Kwan, M., & Lord, S. (2004). Reliability of the gaitrite walkway system for the quantification of temporo-spatial parameters of gait in young and older people. Gait & posture, 20(1), 20–25.

    Article  Google Scholar 

  • Naito, Y., Kimura, Y., Hashimoto, T., Mori, M., & Takemoto, Y. (2013). Quantification of gait using insole type foot pressure monitor: clinical application for chronic hemiplegia. Journal of UOEH, 36(1), 41–48.

    Article  Google Scholar 

  • Nelson, A., Zwick, D., Brody, S., Doran, C., Pulver, L., Rooz, G., Sadownick, M., Nelson, R., & Rothman, J. (2002). The validity of the gaitrite and the functional ambulation performance scoring system in the analysis of parkinson gait. NeuroRehabilitation, 17(3), 255–262.

    PubMed  Google Scholar 

  • Nixon, M., & Carter, J. (2006). Automatic recognition by gait. Proceedings of the IEEE, 94(11), 2013–2024.

    Article  Google Scholar 

  • van den Noort, J.C., Ferrari, A., Cutti, A.G., Becher, J.G., & Harlaar, J. (2013). Gait analysis in children with cerebral palsy via inertial and magnetic sensors. Medical & biological engineering & computing, 51(4), 377–86.

    Article  Google Scholar 

  • Ostadabbas, S., Saeed, A., Nourani, M., & Pompeo, M. (2012). Sensor architectural tradeoff for diabetic foot ulcer monitoring. In Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, pp 6687–6690.

  • Osuna, E., Freund, R., & Girosi, F. (1997). Training support vector machines: an application to face detection. In Computer Vision and Pattern Recognition, IEEE, pp 130–136.

  • Pataky, T.C., Mu, T., Bosch, K., Rosenbaum, D., & Goulermas, J.Y. (2012). Gait recognition: highly unique dynamic plantar pressure patterns among 104 individuals. Journal of The Royal Society Interface, 9(69), 790–800.

    Article  Google Scholar 

  • Pellegrini, S., & Iocchi, L. (2008). Human posture tracking and classification through stereo vision and 3d model matching. Journal on Image and Video Processing, 2008, 7.

    Google Scholar 

  • Peng, B., & Qian, G. (2009). Binocular full-body pose recognition and orientation inference using multilinear analysis. In Tensors in Image Processing and Computer Vision, Springer, pp 215–236.

  • Pontil, M., & Verri, A. (1998). Support vector machines for 3d object recognition. Pattern Analysis and Machine Intelligence, 20(6), 637–646.

    Article  Google Scholar 

  • Qian, G., Zhang, J., & Kidané, A. (2008). People identification using gait via floor pressure sensing and analysis. In Smart sensing and context, Springer, pp 83–98.

  • Sarkar, S., Phillips, P., Liu, Z., Vega, I., Grother, P., & Bowyer, K. (2005). The humanid gait challenge problem: Data sets, performance, and analysis. Pattern Analysis and Machine Intelligence, 27(2), 162–177.

    Article  Google Scholar 

  • von Schroeder, H.P., Coutts, R.D., Lyden, P.D., Billings, E., & Nickel, V.L. (1995). Gait parameters following stroke: a practical assessment. Journal of rehabilitation research and development, 32(1), 25–31.

    CAS  PubMed  Google Scholar 

  • Sutherland, D., Olshen, R., Cooper, L., & Woo, S. (1980). The development of mature gait. Journal of Bone and Joint Surgery, 62(3), 336–53.

    Article  CAS  PubMed  Google Scholar 

  • Takeda, T., Ye, H., Taniguchi, K., Asari, K., Sakai, Y., Kuramoto, K., Kobashi, S., & Hata, Y. (2010). Foot age estimation by gait sole pressure changes. In 2010 IEEE International Conference on Systems Man and Cybernetics (SMC), IEEE, pp 1204–1208.

  • Vapnik, V. (1999). An overview of statistical learning theory. Neural Networks, 10(5), 988–999.

    Article  CAS  PubMed  Google Scholar 

  • Wang, F., Stone, E., Skubic, M., Keller, J.M., Abbott, C., & Rantz, M. (2013). Toward a passive low-cost in-home gait assessment system for older adults. Biomedical and Health Informatics. Journal of IEEE, 17 (2), 346–355.

    Google Scholar 

  • Wang, J., She, M., Nahavandi, S., & Kouzani, A. (2010). A review of vision-based gait recognition methods for human identification. In International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE, pp 320–327.

  • Wang, L., Tan, T., Ning, H., & Hu, W. (2003). Silhouette analysis-based gait recognition for human identification. Pattern Analysis and Machine Intelligence, 25(12), 1505–1518.

    Article  Google Scholar 

  • Webb, A. (2003). Statistical pattern recognition: Wiley.

  • Webster, K., Wittwer, J., & Feller, J. (2005). Validity of the gaitrite walkway system for the measurement of averaged and individual step parameters of gait. Gait & posture, 22(4), 317–321.

    Article  Google Scholar 

  • Yan, S., Xu, D., Zhang, B., & Zhang, H. (2005). Graph embedding: A general framework for dimensionality reduction. In Computer Vision and Pattern Recognition, IEEE, vol 2, pp 830–837.

  • Yoo, J.H., & Nixon, M.S. (2011). Automated markerless analysis of human gait motion for recognition and classification. Etri Journal, 33(2), 259–266.

    Article  Google Scholar 

  • Yun, J. (2011). User identification using gait patterns on ubifloorii. Sensors, 11(3), 2611–2639.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This project was partly supported by IDeA CTR grant NIH/NIGMS Award Number U54GM104942, and a grant from the Center for Identification Technology Research (CITeR).

Author information

Authors and Affiliations

  1. Beijing Advanced Innovation Center for Imaging Technology, Beijing, 100048, People’s Republic of China

    Guodong Guo

  2. College of Information Engineering, Capital Normal University, Beijing, 100048, China

    Guodong Guo

  3. Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, 26506, USA

    Guodong Guo & Wenbin Chen

  4. Child Neurology, Department of Pediatrics, West Virginia University, Morgantown, WV, 26506, USA

    Keegan Guffey

  5. Neurology, Neurocritical care Children’s National Medical Center, 111 Michigan Avenue, Washington, DC, USA

    Paola Pergami

Authors
  1. Guodong Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Keegan Guffey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenbin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Paola Pergami
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guodong Guo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, G., Guffey, K., Chen, W. et al. Classification of Normal and Pathological Gait in Young Children Based on Foot Pressure Data. Neuroinform 15, 13–24 (2017). https://doi.org/10.1007/s12021-016-9313-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12021-016-9313-x

Keywords

Search

Navigation