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Quantitative assessment of dual gait analysis based on inertial sensors with body sensor network

Quantitative assessment of dual gait analysis based on inertial sensors with body sensor network Purpose – Due to the complex mechanism during walking, human gait takes plenty of information reflecting human motion. The method of quantitative measurement of gait makes a profound influence in many fields, such as clinical medicine, biped robot control strategy and so on. The purpose of this paper is to present a gait analysis system based on inertial measurement unit (IMU) and combined with body sensor network (BSN). Design/methodology/approach – The authors placed two wireless inertial nodes on the left and right ankles, so that the acceleration and angular velocity could be obtained from both sides at the same time. By using the kinematical model of the human gait, many methods such as time series analysis, pattern recognition and numerical analysis, are introduced to fuse the inertial data and estimate the sagittal gait parameters. Findings – The gait parameters evaluation gains a practical precision, especially in the gait phase detection and the process of how the two feet cooperate with each other has been analyzed to learn about the mechanism of biped walking. Research limitations/implications – The gait analysis procedure is off line, so that the system ensures sampling at a high rate. Originality/value – This gait analysis system can be utilized to measure quantitative gait parameters. Further, the coordination of dual gait pattern is presented. Last but not least, the system can also be put into capturing and analyzing the motion of other parts of the body. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Sensor Review Emerald Publishing

Quantitative assessment of dual gait analysis based on inertial sensors with body sensor network

Zhelong Wang; Sen Qiu; Zhongkai Cao; Ming Jiang
Sensor Review , Volume 33 (1): 9 – Jan 18, 2013
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Publisher
Emerald Publishing
Copyright
Copyright © 2013 Emerald Group Publishing Limited. All rights reserved.
ISSN
0260-2288
DOI
10.1108/02602281311294342
Publisher site
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Abstract

Purpose – Due to the complex mechanism during walking, human gait takes plenty of information reflecting human motion. The method of quantitative measurement of gait makes a profound influence in many fields, such as clinical medicine, biped robot control strategy and so on. The purpose of this paper is to present a gait analysis system based on inertial measurement unit (IMU) and combined with body sensor network (BSN). Design/methodology/approach – The authors placed two wireless inertial nodes on the left and right ankles, so that the acceleration and angular velocity could be obtained from both sides at the same time. By using the kinematical model of the human gait, many methods such as time series analysis, pattern recognition and numerical analysis, are introduced to fuse the inertial data and estimate the sagittal gait parameters. Findings – The gait parameters evaluation gains a practical precision, especially in the gait phase detection and the process of how the two feet cooperate with each other has been analyzed to learn about the mechanism of biped walking. Research limitations/implications – The gait analysis procedure is off line, so that the system ensures sampling at a high rate. Originality/value – This gait analysis system can be utilized to measure quantitative gait parameters. Further, the coordination of dual gait pattern is presented. Last but not least, the system can also be put into capturing and analyzing the motion of other parts of the body.

Journal

Sensor Review – Emerald Publishing

Published: Jan 18, 2013

Keywords: Motion; Limbs; Sensors; Human anatomy; Inertial; Gait; Human motion; Wireless; Data fusion

References

  • A force‐based approach for joint efforts estimation during the double support phase of gait
    Alonso, F.J.; Cuadrado, J.; Lugris, U.; Pamies‐Vila, R.
  • Multisensor approach to walking distance estimation with foot inertial sensing
    Alvarez, D.; Alvarez, J.C.; Gonzalez, R.C.; Lopez, A.M.; Rodriguez‐Uria, J.
  • A high precision reference data set for pedestrian navigation using foot‐mounted inertial sensors
    Angermann, M.; Kemptner, T.; Khider, M.; Robertson, P.
  • Virtual walkway system and prediction of gait mode transition for the control of the gait simulator
    Arima, S.; Makikawa, M.; Shiozawa, N.
  • A tele‐gait monitoring system with an inertial measurement unit and smart shoes
    Bae, J.; Kong, K.; Tomizuka, M.
  • Three dimensional inertial sensing of foot movements for automatic tuning of a two channel implantable drop‐foot stimulator
    Bultstra, G.; Buschman, R.; Hemssems, J.; Hermens, H.; Slycke, P.; Veltink, P.H.
  • Sensor fusion applied to position estimation and gait identification of an ankle‐foot orthosis
    Caltran, C.; Siqueira, A.A.G.
  • A step toward GPS/INS personal navigation systems: real‐time assessment of gait by foot inertial sensing
    Cavallo, F.; Sabatini, A.M.; Genovese, V.
  • Assessment of spatio‐temporal gait parameters using inertial measurement units in neurological populations
    Collett, J.; Dawes, H.; Esser, P.; Feltham, M.G.; Howells, K.
  • A reliable gait phase detection system
    Dietz, V.; Keller, T.; Morari, M.; Pappas, I.P.I.; Popovic, M.R.
  • Foot mounted inertial system for pedestrian navigation
    Godha, S.; Lachapelle, G.
  • An inertial and magnetic sensor based technique for joint angle measurement
    Kamnik, R.; Lyons, G.M.; O'Donovan, K.J.; O'Keeffe, D.T.
  • Wearable accelerometer system for measuring the temporal parameters of gait
    Lee, J.A.; Cho, S.H.; Lee, J.W.; Lee, K.H.; Yang, H.K.
  • A walking monitoring shoe system for simultaneous plantar‐force measurement and gait‐phase detection
    Lee, K.M.; Wang, D.; Yang, C.; Yu, H.
  • Improved gait recognition by gait dynamics normalization
    Liu, Z.; Sarkar, S.
  • Human gait recognition at sagittal plane
    Metaxax, D.; Vogler, C.; Zhang, R.
  • Pedestrian inertial navigation with gait phase detection assisted zero velocity updating
    Park, S.K.; Suh, Y.S.
  • A zero velocity detection algorithm using inertial sensors for pedestrian navigation systems
    Park, S.K.; Suh, Y.S.
  • Assessment of walking features from foot inertial sensing
    Sabatini, A.M.; Martelloni, C.; Scapellato, S.; Cavallo, F.
  • Evaluation of zero‐velocity detectors for foot‐mounted inertial navigation systems
    Skog, I.; Nilsson, J.; Handel, P.
  • Realtime gait kinematics classification using LDA and SVM
    Wang, F.; Wen, S.; Wu, C.
  • Dual gait generative models for human motion estimation from a single camera
    Zhang, X.; Fan, G.
  • Inertial sensors for motion detection of human upper limbs
    Zhou, H.; Hu, H.