Journal of the Ergonomics Society of Korea (대한인간공학회지)

Ergonomics Society of Korea (대한인간공학회)
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Analysis of Lower-Limb Motion during Walking on Various Types of Terrain in Daily Life

  • Received : 2016.04.19
  • Accepted : 2016.09.19
  • Published : 2016.10.31
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Abstract

Objective:This research analyzed the lower-limb motion in kinetic and kinematic way while walking on various terrains to develop Foot-Ground Contact Detection (FGCD) algorithm using the Inertial Measurement Unit (IMU). Background: To estimate the location of human in GPS-denied environments, it is well known that the lower-limb kinematics based on IMU sensors, and pressure insoles are very useful. IMU is mainly used to solve the lower-limb kinematics, and pressure insole are mainly used to detect the foot-ground contacts in stance phase. However, the use of multiple sensors are not desirable in most cases. Therefore, only IMU based FGCD can be an efficient method. Method: Orientation and acceleration of lower-limb of 10 participants were measured using IMU while walking on flat ground, ascending and descending slope and stairs. And the inertial information showing significant changes at the Heel strike (HS), Full contact (FC), Heel off (HO) and Toe off (TO) was analyzed. Results: The results confirm that pitch angle, rate of pitch angle of foot and shank, and acceleration in x, z directions of the foot are useful in detecting the four different contacts in five different walking terrain. Conclusion: IMU based FGCD Algorithm considering all walking terrain possible in daily life was successfully developed based on all IMU output signals showing significant changes at the four steps of stance phase. Application: The information of the contact between foot and ground can be used for solving lower-limb kinematics to estimating an individual's location and walking speed.

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References

  1. Adrian, M. and Cooper, J.M., The Biomechanics of Human Movement, Benchmark Press, 1995.
  2. Ahn, S.C., Hwang, S.J., Kang, S.J. and Kim, Y.H., Development and Evaluation of a New Gait Phase Detection System Using FSR Sensors and a Gyrosensor, Journal of the Korean Society for Precision Engineering, 21, 196-203, 2004.
  3. Bamberg, S.J.M., Benbasat, A.Y., Scarborough, D.M., Krebs, D.E. and Paradiso, J.A., Gait Analysis Using a Shoe-integrated Wireless Sensor System, IEEE Transactions on Information Technology in Biomedicine, 12, 413-423, 2008. https://doi.org/10.1109/TITB.2007.899493
  4. Banos, O., Toth, M.A., Damas, M., Pomares, H. and Rojas, I., Dealing with The Effects of Sensor Displacement in Wearable Activity Recognition, Sensors, 14, 9995-10023, 2014. https://doi.org/10.3390/s140609995
  5. Benocci, M., Rocchi, L., Farella, E., Chiari, L. and Benini, L., A Wireless System for Gait and Posture Analysis Based on Pressure Insoles and Inertial Measurement Units, Proceeding of the 3rd International Conference on Pervasive Computing Technologies for Healthcare, (pp. 1-6), London, 2009.
  6. Bergmann, J.H.M., Mayagoitia, R.E. and Smith, I.C.H., A Novel Method for Determining Ground-referenced Contacts During Stair Ascent: Comparing Relative Hip Position to Quiet Standing Hip Height, Gait & Posture, 31(2), 164-168, 2010. https://doi.org/10.1016/j.gaitpost.2009.09.018
  7. Chen, M., Yan, J. and Xu, Y., Gait Pattern Classification with Integrated Shoes, Proceedings of the annual Conference on Intelligent Robots and Systems, (pp. 833-839), St. Louis, 2009.
  8. Choi, J.H. and Hong, W.H., A Suggestion on a New Correction Coefficient for SIMULEX Egress Model to Predict Agent's Stair Slope Travel Time in a High-rise Building, Journal of the Architectural Institute of Korea: Planning & Design, 29, 285-292, 2013.
  9. Dadashi, F., Mariani, B., Rochat, S., Bula, C.J., Santos-Eggimann, B. and Aminian, K., Gait and Foot Clearance Parameters Obtained Using Shoe-worn Inertial Sensors in a Large-population Sample of Older Adults, Sensors, 14, 443-457, 2014.
  10. D'Attanasio Honiger, S., Micallef, J.P., Peruchon, E., Guiraud, D. and Rabischong, P., A Robust, Economic and Ergonomic Sensor Device for Gate Phase Detection for an Implanted FES System, Proceeding of the annual Conference on International Functional Electrical Stimulation Society, Cleveland, 2001.
  11. Eng, J.J., and Winter, D.A., Kinetic Analysis of the Lower Limbs During Walking: What Information Can be Gained From a Threedimensional Model?, Journal of biomechanics, 28, 753-758, 1995. https://doi.org/10.1016/0021-9290(94)00124-M
  12. Ferrari, A., Ginis, P., Hardegger, M., Casamassima, F., Rocchi, L. and Chiari, L., A Mobile Kalman-Filter Based Solution for the Real-Time Estimation of Spatio-Temporal Gait Parameters, Neural Systems and Rehabilitation Engineering, IEEE Transactions On, (99), 2015.
  13. Hogg, R.W., Rankin, A.L., Roumeliotis, S.I., McHenry, M.C., Helmick, D.M., Bergh, C.F. and Matthies, L., Algorithms and Sensors for Small Robot Path Following, Proceeding of Annual Conference for the Society of IEEE International Conference on Robotics and Automation (ICRA), Washington, 2002.
  14. John, J.C., Introduction to Robotics, Pearson Prentice Hall, 2005.
  15. Kim, M. and Lee, D., Development of an IMU based foot-ground contact detection (FGCD) algorithm, Ergonomics, (just-accepted), 1-23, 2016.
  16. Li, Y., Luo, X., Ren, X.T. and Wang, J.J., A Robust Humanoid Robot Navigation Algorithm with ZUPT, Proceeding of Annual International Conference on Mechatronics and Automation, Sichuan Chengdu, 2012.
  17. Li, Y. and Wang, J.J., A Robust Pedestrian Navigation Algorithm with Low Cost IMU, Proceeding of Annual International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sydney, 2012.
  18. Lieberman, D.E., Venkadesan, M., Werbel, W.A., Daoud, A.I., D'Andrea, S., Davis, I.S., Mang'Eni, O.R. and Pitsiladis, Y., Foot Strike Patterns and Collision Forces in Habitually Barefoot Versus Shod Runners, Nature, 463, 531-535, 2010. https://doi.org/10.1038/nature08723
  19. Morioka, K., Lee, J.H. and Hashimoto, H., Human-following Mobile Robot in a Distributed Intelligent Sensor Network, Industrial Electronics, IEEE Transactions On, 51, 229-237, 2004. https://doi.org/10.1109/TIE.2003.821894
  20. Ng, T.C., Ibanez-Guzman, J., Shen, J., Gong, Z., Wang, H. and Cheng, C., Vehicle Following with Obstacle Avoidance Capabilities in Natural Environments, Proceeding of Annual International Conference on Robotics and Automation (ICRA), New Orleans, 2004.
  21. Nistler, J.R. and Selekwa, M.F., Gravity Compensation in Accelerometer Measurements for Robot Navigation on Inclined Surfaces, Procedia Computer Science, 6, 413-418, 2011. https://doi.org/10.1016/j.procs.2011.08.077
  22. Pappas, I.P., Keller, T., Mangold, S., Popovic, M.R., Dietz, V. and Morari, M., A Reliable Gyroscope-based Gait-phase Detection Sensor Embedded in a Shoe Insole, IEEE Sensors Journal, 4, 268-274, 2004. https://doi.org/10.1109/JSEN.2004.823671
  23. Pappas, I.P., Popovic, M.R., Keller, T., Dietz, V. and Morari, M., A Reliable Gait Phase Detection System, Neural Systems and Rehabilitation Engineering, IEEE Transactions On, 9, 113-125, 2001.
  24. Park, S.J., Lee, J.S., Gang, D.H., Jung, E.H., Jung, H.J. and Park, S.B., Research on Walking Speed and Stride According to Age, Proceeding of Annual Conference on The Ergonomics Society of Korea, Busan, 2007.
  25. Salarian, A., Burkhard, P.R., Vingerhoets, F.J., Jolles, B.M. and Aminian, K., A Novel Approach to Reducing Number of Sensing Units for Wearable Gait Analysis Systems, Biomedical Engineering, IEEE Transactions On, 60, 72-77, 2013. https://doi.org/10.1109/TBME.2012.2223465
  26. Seco, F., Prieto, C. and Guevara, J., A Comparison of Pedestrian Dead-reckoning Algorithms Using a Low-cost MEMS IMU, Proceeding of the Annual Conference on the Society of IEEE International Symposium on Intelligent Signal Processing, Budapest, 2009.
  27. Shimizu, T., Awai, M., Yamashita, A. and Kaneko, T., Mobile Robot System Realizing Human Following and Autonomous Returning Using Laser Range Finder and Camera. Proceeding of the Annual Conference on the Society of Fontiers of Computer Vision (FCV), Kawasaki, 2012.
  28. Yuan, Q. and Chen, I., Human Velocity and Dynamic Behavior Tracking Method for Inertial Capture System, Sensors and Actuators A: Physical, 183, 123-131, 2012. https://doi.org/10.1016/j.sna.2012.06.003
  29. Yuan, Q. and Chen, I., Localization and Velocity Tracking of Human via 3 IMU Sensors, Sensors and Actuators A: Physical, 212, 25-33, 2014. https://doi.org/10.1016/j.sna.2014.03.004