Current Science

Open Access Open Access  Restricted Access Subscription Access

Gait Parameters in School Going Children Using a Marker-Less Approach


Affiliations
1 Center of Excellence in Industrial and Product Design, PEC University of Technology, Chandigarh 160 012, India
2 National Institute of Industrial Engineering, Powai, Mumbai 400 087, India
 

The burden of course work in Indian schools has exposed the school children to various postural/gait disorders due to heavy backpack. Therefore, it is paramount to develop a low cost, non-intrusive and reliable method for calculation of gait parameters. This study assessed the spatiotemporal parameters such as height of earlobe (HoE), stride length (SL) and stride width (SW) using the markerless sensor Kinect v2 and conventional techniques pursued in Indian clinics. Sixty school children (aged 11 to 15 years) were monitored through both the techniques while performing walking trials. To assess the agreement between the techniques Bland-Altman 95% bias, percentage error (PE), Pearson's correlation coefficients (r1) and concordance correlation coefficients (r2) were determined. Each parameter obtained from both techniques possessed strong correlation (r1 and 2 > 0.90). Gait analysis using the Kinect V2 sensor is an acceptable, unobtrusive and economical method. The effect of relative backpack weight (RBW), i.e. (bag weight to body weight percentage) and strategies of backpack packing recommended by the American Occupational Therapy Association on the selected parameters was studied. The effect of RBW on the variation in parameters was evaluated using the regression curve whereas the effect of proper packing was evaluated by paired sample T test. RBW has positive correlation with SW (r1 = 0.631), negative correlation with HoE (r1 = -0.387) but shows no correlation with SL. Recommended packing strategy of schoolbag by AOTA shows results to reduce the unwanted variation in gait parameters.

Keywords

Packing, Heavy Backpack, Spatiotemporal Parameters, Kinect V2, School Children.
User
Notifications
Font Size

  • MHRD, 2013; http://mhrd.gov.in/Department of School Education and Literacy/AR2013-14.pdf

  • Grimmer, K., Dansie, B., Milanese, S., Pirunsan, U. and Trott, P., Adolescent standing postural response to backpack loads: a randomised controlled experimental study. BMC Musculoskeletal Disorders, 2002, 3(1), 10; Song, Q., Yu, B., Zhang, C., Sun, W. and Mao, D., Effects of backpack weight on posture, gait patterns and ground reaction forces of male children with obesity during stair descent. Res. Sports Med., 2014, 22(2), 172–184.

  • Negrini, S. and Carabalona, R., Backpacks on Schoolchildren’s perceptions of load, associations with back pain and factors determining the load. Spine, 2002, 27(2), 187–195.

  • Hong, Y. and Brueggemann, G. P., Changes in gait patterns in 10year-old boys with increasing loads when walking on a treadmill. Gait Posture, 2000, 11(3), 254–259.

  • Voll, H. J. and Klimt, F., On strain in children caused by carrying school bags. Das Offent Gesundheitswes, 1977, 39(7), 369.

  • Tinsworth, D. K. and McDonald, J., Special study: Injuries and deaths associated with children’s playground equipment, Washington, DC, US Consumer Product Safety Commission, 2001.

  • Hong, Y., Wong, A. S. and Robinson, P. D., Effects of load carriage on heart rate, blood pressure and energy expenditure in children. Ergonomics, 2000, 43(6), 717–727.

  • Pascoe, D., Pascoe, D. E., Wang, Y. T., Shim, D. M. and Kim, C. K., Influence of carrying book bags on gait cycle and posture of youths. Ergonomics, 1997, 40(6), 631–641.

  • Ramaprasad, M., Allias, J. and Raghuveer, A. K., Effect of backpack weight on postural angles in preadolescent children. Indian Paediatr., 2009, 47(7), 575–580.

  • Motmans, R. R. and Tomlow, S., Trunk muscle activity in different modes of carrying schoolbags. Ergonomics, 2006, 49(2), 127–138.

  • Johansson, G., Visual motion perception. Sci. Am., 1975, 232, 76–88.

  • Heisele, B. and Woehler, C., Motion-based recognition of pedestrians in pattern recognition. In Proceedings of the Fourteenth International Conference on IEEE, 1998, vol. 2, pp. 1325–1330.

  • Hayashi, Y., Sakata, M., Nakao, T. and Ohno, T., Alphanumeric character recognition using a connectionist model with the pocket algorithm. Neural Networks Int. Joint Conf. IEEE, 1989, 2, 606.

  • Huang, F. S., Wong, L. T., Wang, C. J. and Chung, J. M., Effect of backpack on selected gait parameters of primary school children. ISBS Conf. Proc. Arch., 2005, 1(1).

  • Jafarnezhadgero, A., Farahpour, N. and Damavandi, M., The acute effects of the application of arch support insole on ground reaction forces during walking. J. Res. Rehabilitat. Sci., 2005, 11(3), 145–159.

  • Corazza, S., Mundermann, L. and Andriacchi, T., Markerless motion capture method for the estimation of human body kinematics. 3D Analysis of Human Movement, 9th International Symposium, 2009, 28–30.

  • Stone, E. and Skubic, M., Evaluation of an inexpensive depth camera for in-home gait assessment. J. Ambient Intell. Smart Environ., 2011, 3(4), 349–361.

  • Stone, E. and Skubic, M., Unobtrusive, continuous, in-home gait measurement using the Microsoft Kinect. Biomed. Eng., IEEE Trans., 2013, 60(10), 2925–2932.

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

  • Schmitz, A., Ye, B., Shapiro, R., Yang, R. and Noehren, B., Accuracy and repeatability of joint angles measured using a single camera markerless motion capture system. J. Biomech., 2014, 47(2), 587–591.

  • Gabel, M., Renshaw, E. and Schuster, A., Full body gait analysis with Kinect. In Engineering in Medicine and Biology Society (EMBC), Annual International Conference of the IEEE, 2012, 1964-67.

  • Microsoft; http://www.microsoft.com/en-us/Kin-ect for windows/

  • Gouelle, A., Megrot, F., Presedo, A., Husson, I., Yelnik, A. and Penneçot, G. F., The gait variability index: a new way to quantify fluctuation magnitude of spatiotemporal parameters during gait. Gait Posture, 2013, 38(3), 461–465.

  • Wang, T., Wang, M., Liu, X. and Wang, T., A human body posture identification algorithm-based on Kinect. In International Conference on LEMCS, 2015.

  • Bland, J. M. and Altman, D. G., Calculating correlation coefficients with repeated observations: Part 2 – correlation between subjects. Br. Med. J., 1995, 310(6980), 633.

  • Backpack awareness: one of many ways that occupational therapists serve students, American occupational Therapy Association, 2009.

  • Sun, B., Liu, X., Wu, X. and Wang, H., Human gait modeling and gait analysis based on Kinect. In Robotics and Automation (ICRA), IEEE International Conference, 2014, pp. 3173–3178.

  • Motiian, S., Pergami, P., Guffey, K., Mancinelli, C. A. and Doretto, G., Automated extraction and validation of children’s gait parameters with the Kinect. Biomed. Eng. Online, 2015, 14(1), 11.


Abstract Views: 323

PDF Views: 111




  • Gait Parameters in School Going Children Using a Marker-Less Approach

Abstract Views: 323  |  PDF Views: 111

Authors

Ishanth Gupta
Center of Excellence in Industrial and Product Design, PEC University of Technology, Chandigarh 160 012, India
Parveen Kalra
Center of Excellence in Industrial and Product Design, PEC University of Technology, Chandigarh 160 012, India
Rauf Iqbal
National Institute of Industrial Engineering, Powai, Mumbai 400 087, India

Abstract


The burden of course work in Indian schools has exposed the school children to various postural/gait disorders due to heavy backpack. Therefore, it is paramount to develop a low cost, non-intrusive and reliable method for calculation of gait parameters. This study assessed the spatiotemporal parameters such as height of earlobe (HoE), stride length (SL) and stride width (SW) using the markerless sensor Kinect v2 and conventional techniques pursued in Indian clinics. Sixty school children (aged 11 to 15 years) were monitored through both the techniques while performing walking trials. To assess the agreement between the techniques Bland-Altman 95% bias, percentage error (PE), Pearson's correlation coefficients (r1) and concordance correlation coefficients (r2) were determined. Each parameter obtained from both techniques possessed strong correlation (r1 and 2 > 0.90). Gait analysis using the Kinect V2 sensor is an acceptable, unobtrusive and economical method. The effect of relative backpack weight (RBW), i.e. (bag weight to body weight percentage) and strategies of backpack packing recommended by the American Occupational Therapy Association on the selected parameters was studied. The effect of RBW on the variation in parameters was evaluated using the regression curve whereas the effect of proper packing was evaluated by paired sample T test. RBW has positive correlation with SW (r1 = 0.631), negative correlation with HoE (r1 = -0.387) but shows no correlation with SL. Recommended packing strategy of schoolbag by AOTA shows results to reduce the unwanted variation in gait parameters.

Keywords


Packing, Heavy Backpack, Spatiotemporal Parameters, Kinect V2, School Children.

References





DOI: https://doi.org/10.18520/cs%2Fv111%2Fi10%2F1668-1675