Literature DB >> 16210830

Clinical application of acceleration sensor to detect the swing phase of stroke gait in functional electrical stimulation.

Yoichi Shimada1, Shigeru Ando, Toshiki Matsunaga, Akiko Misawa, Toshiaki Aizawa, Tsuyoshi Shirahata, Eiji Itoi.   

Abstract

Functional electrical stimulation (FES) can improve the gait of stroke patients by stimulating the peroneal nerve in the swing phase of the affected leg, causing dorsiflexion of the foot that allows the toes to clear the ground. A sensor can trigger the electrical stimulation automatically during the stroke gait. We previously used a heel sensor system, which detects the contact pressure of the heel, in FES to correct foot drop gait. However, the heel sensor has disadvantages in cosmetics and durability. Therefore, we have replaced the heel sensor with an acceleration sensor that can detect the swing phase based on the acceleration speed of the affected leg, using a machine learning technique (Neural Network). We have used a signal for heel contact in a gait using the heel sensor before training with the Neural Network. The accuracy of the Neural Network detector was compared with a swing phase detector based on the heel sensor. The Neural Network detector was able to detect similarly the swing phase in the heel sensor. The largest difference in timing of the swing phase was less than 60 milliseconds in normal subjects and 80 milliseconds in stroke patients. We were able to correct foot drop gait using FES with an acceleration sensor and Neural Network detector. The present results indicate that an acceleration sensor positioned on the thigh, which is cosmetically preferable to systems in which the sensor is farther from the entry point of the electrodes, is useful for correction of stroke gait using FES.

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Mesh:

Year:  2005        PMID: 16210830     DOI: 10.1620/tjem.207.197

Source DB:  PubMed          Journal:  Tohoku J Exp Med        ISSN: 0040-8727            Impact factor:   1.848


  6 in total

1.  Support vector machine for classification of walking conditions using miniature kinematic sensors.

Authors:  Hong-Yin Lau; Kai-Yu Tong; Hailong Zhu
Journal:  Med Biol Eng Comput       Date:  2008-03-18       Impact factor: 2.602

Review 2.  Technological advances in interventions to enhance poststroke gait.

Authors:  Lynne R Sheffler; John Chae
Journal:  Phys Med Rehabil Clin N Am       Date:  2013-05       Impact factor: 1.784

3.  Gait event detection using a thigh-worn accelerometer.

Authors:  Reed D Gurchiek; Cole P Garabed; Ryan S McGinnis
Journal:  Gait Posture       Date:  2020-06-06       Impact factor: 2.840

Review 4.  Gait analysis using wearable sensors.

Authors:  Weijun Tao; Tao Liu; Rencheng Zheng; Hutian Feng
Journal:  Sensors (Basel)       Date:  2012-02-16       Impact factor: 3.576

5.  Estimation of step-by-step spatio-temporal parameters of normal and impaired gait using shank-mounted magneto-inertial sensors: application to elderly, hemiparetic, parkinsonian and choreic gait.

Authors:  Diana Trojaniello; Andrea Cereatti; Elisa Pelosin; Laura Avanzino; Anat Mirelman; Jeffrey M Hausdorff; Ugo Della Croce
Journal:  J Neuroeng Rehabil       Date:  2014-11-11       Impact factor: 4.262

6.  Continuous gait cycle index estimation for electrical stimulation assisted foot drop correction.

Authors:  Christine Azevedo Coste; Jovana Jovic; Roger Pissard-Gibollet; Jérôme Froger
Journal:  J Neuroeng Rehabil       Date:  2014-08-09       Impact factor: 4.262

  6 in total

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