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

Yoshitaka Nozaki, Takashi Watanabe

研究成果: Article査読

1 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)2027-2031
ページ数5
ジャーナルIEICE Transactions on Information and Systems
E103D
9
DOI
出版ステータスPublished - 2020 9 1

ASJC Scopus subject areas

  • ソフトウェア
  • ハードウェアとアーキテクチャ
  • コンピュータ ビジョンおよびパターン認識
  • 電子工学および電気工学
  • 人工知能

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