Abstract
Initial contact (IC) and toe-off (TO) events are tools for measuring and analyzing human gait. As a simpler method for detecting gait events that depend only on inertial measurement unit (IMU) signals is needed, in this study, we propose a simpler signal feature-based method for detecting gait events that is feasible for use in in-shoe motion sensor (IMS) systems, and these exact features are used to determine the timings of IC and TO according to biomechanical knowledge. We then evaluate the precision of the method. Twenty-six healthy subjects were recruited to participate in the experiments, during which an IMS along with a Vicon 3D motion analyzer was applied to measure the trajectory of the foot and to judge the IC and TO timings. Temporal features of the foot to ground kinematic waveform at the time of IC and TO are newly discovered by synchronizing the two systems. The temporal precision of an algorithm for automatic IC and TO detection is evaluated on the basis of root mean square error (RMSE) and intraclass correlation coefficient (ICC). The RMSE of the TO detection was 1.22%, and that of the IC was 1.40%. The ICC of the TO detection was 0.7011, and that of the IC was 0.7721. The results demonstrate the high detection accuracy and reliability of this simpler IC and TO automatic detection algorithm for IMSs.
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
References
Hodgins, D.: The importance of measuring human gait. Med. Device. Technol. 19(5), 42, 44–47 (2008)
Keijsers, N.L.W., Horstink, M.W.I.M., Gielen, S.C.A.M.: Ambulatory motor assessment in Parkinson’s disease. Mov. Disord. 21(1), 34–44 (2006)
Hausdorff, J.M., Lertratanakul, A., Cudkowicz, M.E., Peterson, A.L., Kaliton, D., Goldberger, A.L.: Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis. J. Appl. Physiol. 88(6), 2045–2053 (2000)
Hausdorff, J.M., Rios, D.A., Edelberg, H.K.: Gait variability and fall risk in community-living older adults: A 1-year prospective study. Arch. Phys. Med. Rehabil. 82(8), 1050–1056 (2001)
Neumann, D.A.: Kinesiology of the musculoskeletal system: foundations of physical rehabilitation, 2nd edn., pp. 636–650. Mosby, St Louis, MO, USA (2010)
Morris, M.E., Matyas, T.A., Iansek, R., Summers, J.J.: Temporal stability of gait in Parkinson’s disease. Phys. Ther. 76(7), 763–777 (1996)
Ornetti, P., Maillefert, J.F., Laroche, D., Morisset, C., Dougados, M., Gossec, L.: Gait analysis as a quantifiable outcome measure in hip or knee osteoarthritis: a systematic review. Joint Bone Spine 77(5), 421–425 (2010)
Whittle, M.W.: Clinical gait analysis: A review. Hum. Mov. Sci. 15(3), 369–387 (1996)
MacDonald, C., Smith, D., Brower, R., Ceberio, M., Sarkodie-Gyan, T.: Determination of human gait phase using fuzzy inference. In: Proceedings of 10th IEEE International Conference on Rehabilitation Robotics, pp. 661–665. Noordwijk, The Netherland (2007)
Lau, H., Tong, K.: The reliability of using accelerometer and gyroscope for gait event identification on persons with dropped foot. Gait & Posture 27(2), 248–257 (2008)
Gouwanda, D., Gopalai, A.A.: A robust real-time gait event detection using wireless gyroscope and its application on normal and altered gaits. Med. Eng. Phys. 37, 219–225 (2015)
Stamatakis, J., Cremers, J., Maquet, D., Macq, B., Garraux, G.: Gait feature extraction in Parkinson's disease using low-cost accelerometers. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 7900–7903. IEEE (2011)
Williamson, R., Andrews, B.J.: Gait event detection for FES using accelerometers and supervised machine learning. IEEE Trans. Rehabil. Eng. 8, 312–319 (2000)
Mannini, A., Sabatini, A.M.: A hidden Markov model-based technique for gait segmentation using a foot-mounted gyroscope. In: Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), pp. 4369–4373. Boston, MA, USA (2011)
Jung, P.G., Oh, S., Lim, G., Kong, K.: A mobile motion capture system based on inertial sensors and smart shoes. J. Dyn Syst Meas Control 136(1) (2014)
Jagos, H., Pils, K., Haller, M., Wassermann, C., Chhatwal, C., Rafolt, D., Rattay, F.: Mobile gait analysis via eSHOEs instrumented shoe insoles: A pilot study for validation against the gold standard GAITRite®. J. Med. Eng. Technol. 41(5), 375–386 (2017)
Mijailović, N., Gavrilović, M., Rafajlović, S., ÐuricJovicic, M., Popović, D.: Gait phases recognition from accelerations and ground reaction forces: application of neural networks. Telfor J. 1, 34–36 (2009)
Endo, K., Herr, H.: Human walking model predicts joint mechanics, electromyography and mechanical economy. In 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4663–4668. IEEE (2009)
Madgwick, S.: An efficient orientation filter for inertial and inertial/magnetic sensor arrays. Report x-io and University of Bristol (UK) 25, 113–118 (2010)
Nilufar, S., Morrow, A.A., Lee, J.M., Perkins, T.J.: FiloDetect: automatic detection of filopodia from fluorescence microscopy images. BMC Syst. Boil. 7, 66 (2013)
Hartmann, A., Luzi, S., Murer, K., deBie, R.A., de Bruin. E.D.: Concurrent validity of a trunk tri-axial accelerometer system for gait analysis in older adults. Gait Posture 29, 444–448 (2009)
Abu-Faraj, Z.O., Harris, G.F., Smith, P.A., Hassani, S.: Human gait and clinical movement analysis. In: Wiley Encyclopedia of Electrical and Electronics Engineering, 2nd edn, pp. 1–34. Wiley, New York, USA (2015)
Takahashi, K.Z., Gross, M.T., Van Werkhoven, H., Piazza, S.J., Sawicki, G.S.: Adding stiffness to the foot modulates soleus force-velocity behaviour during human walking. Scientific Reports 6, 29870 (2016)
Nene, A., Mayagoitia, R., Veltink, P.: Assessment of rectus femoris function during initial swing phase. Gait & Posture 9(1), 1–9 (1999)
Farris, R.J., Quintero, H.A., Withrow, T.J., Goldfarb, M.: Design of a joint-coupled orthosis for FES-aided gait. In: 2009 IEEE International Conference on Rehabilitation Robotics, pp. 246–252. IEEE (2009)
Jagos, H., Reich, S., Rattay, F., Mehnen, L., Pils, K., Wassermann, C., Reichel, M.: Determination of gait parameters from the wearable motion analysis system eSHOE. Biomedical Engineering/Biomedizinische Technik (2013)
Mannini, A., Sabatini, A.M.: Gait phase detection and discrimination between walking-jogging activities using hidden Markov models applied to foot motion data from a gyroscope. Gait Posture 36, 657–661 (2012)
Mariani, B., Rouhani, H., Crevoisier, X., Aminian, K.: Quantitative estimation of foot-flat and stance phase of gait using foot-worn inertial sensors. Gait Posture 37, 229–234 (2013)
Evans, R. L., & Arvind, D. K. (2014, June). Detection of gait phases using orient specks for mobile clinical gait analysis. In 2014 11th International Conference on Wearable and Implantable Body Sensor Networks (pp. 149–154). IEEE.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Huang, C., Fukushi, K., Wang, Z., Kajitani, H., Nihey, F., Nakahara, K. (2021). Initial Contact and Toe-Off Event Detection Method for In-Shoe Motion Sensor. In: Ahad, M.A.R., Inoue, S., Roggen, D., Fujinami, K. (eds) Activity and Behavior Computing. Smart Innovation, Systems and Technologies, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-15-8944-7_7
Download citation
DOI: https://doi.org/10.1007/978-981-15-8944-7_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-8943-0
Online ISBN: 978-981-15-8944-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)