Development of artificial neural network based automatic stride length estimation method using IMU: Validation test with healthy subjects

Yoshitaka Nozaki, Takashi Watanabe

Research output: Contribution to journalArticlepeer-review

Abstract

Rehabilitation and evaluation of motor function are important for motor disabled patients. In stride length estimation using an IMU attached to the foot, it is necessary to detect the time of the movement state, in which acceleration should be integrated. In our previous study, acceleration thresholds were used to determine the integration section, so it was necessary to adjust the threshold values for each subject. The purpose of this study was to develop a method for estimating stride length automatically using an artificial neural network (ANN). In this paper, a 4-layer ANN with feature extraction layers trained by autoencoder was tested. In addition, the methods of searching for the local minimum of acceleration or ANN output after detecting the movement state section by ANN were examined. The proposed method estimated the stride length for healthy subjects with error of −1.88 ± 2.36%, which was almost the same as the previous threshold based method (−0.97 ± 2.68%). The correlation coefficients between the estimated stride length and the reference value were 0.981 and 0.976 for the proposed and previous methods, respectively. The error ranges excluding outliers were between −7.03% and 3.23%, between −7.13% and 5.09% for the proposed and previous methods, respectively. The proposed method would be effective because the error range was smaller than the conventional method and no threshold adjustment was required.

Original languageEnglish
Pages (from-to)2027-2031
Number of pages5
JournalIEICE Transactions on Information and Systems
VolumeE103D
Issue number9
DOIs
Publication statusPublished - 2020 Sep 1

Keywords

  • Gait
  • Inertial sensor
  • Neural-network
  • Rehabilitation
  • Stride-length

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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