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.
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Aggarwal, J., & Ryoo, M. (2011). Human activity analysis: A review. ACM Computing Surveys, 43(3), 16:1–16:43.
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.
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.
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.
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.
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.
Bonato, P. (2005). Advances in wearable technology and applications in physical medicine and rehabilitation. Journal of NeuroEngineering and Rehabilitation, 2(1), 2.
Boulay, B., Brémond, F., & Thonnat, M. (2006). Applying 3d human model in a posture recognition system. Pattern Recognition Letters, 27(15), 1788–1796.
Breiman, L. (2001). Random forests. Machine learning, 45(1), 5–32.
Cai, D., He, X., Han, J., & Zhang, H. (2006). Orthogonal laplacianfaces for face recognition. Image Processing. Transactions on IEEE, 15(11), 3608–3614.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Majnemer, A. (2010). Benefits of using outcome measures in pediatric rehabilitation. Physical & occupational therapy in pediatrics, 30(3), 165–7.
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.
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.
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.
Nixon, M., & Carter, J. (2006). Automatic recognition by gait. Proceedings of the IEEE, 94(11), 2013–2024.
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.
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.
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.
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.
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.
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.
Sutherland, D., Olshen, R., Cooper, L., & Woo, S. (1980). The development of mature gait. Journal of Bone and Joint Surgery, 62(3), 336–53.
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.
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.
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.
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.
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.
Yun, J. (2011). User identification using gait patterns on ubifloorii. Sensors, 11(3), 2611–2639.
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).
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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
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DOI: https://doi.org/10.1007/s12021-016-9313-x