Development of Artificial Neural Network Based Automatic Stride Length Estimation Method Using IMU: Validation Test with Healthy Subjects

Yoshitaka NOZAKI; Takashi WATANABE

doi:10.1587/transinf.2019EDL8227

Development of Artificial Neural Network Based Automatic Stride Length Estimation Method Using IMU: Validation Test with Healthy Subjects

Yoshitaka NOZAKI, Takashi WATANABE

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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.

Publication: IEICE TRANSACTIONS on Information Vol.E103-D No.9 pp.2027-2031

Publication Date: 2020/09/01

Publicized: 2020/06/10

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2019EDL8227

Type of Manuscript: LETTER

Category: Biological Engineering

Authors

Yoshitaka NOZAKI
Tohoku University
Takashi WATANABE
Tohoku University

Keyword

inertial sensor, stride-length, neural-network, gait, rehabilitation

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Yoshitaka NOZAKI, Takashi WATANABE, "Development of Artificial Neural Network Based Automatic Stride Length Estimation Method Using IMU: Validation Test with Healthy Subjects" in IEICE TRANSACTIONS on Information, vol. E103-D, no. 9, pp. 2027-2031, September 2020, doi: 10.1587/transinf.2019EDL8227.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8227/_p

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@ARTICLE{e103-d_9_2027,
author={Yoshitaka NOZAKI, Takashi WATANABE, },
journal={IEICE TRANSACTIONS on Information},
title={Development of Artificial Neural Network Based Automatic Stride Length Estimation Method Using IMU: Validation Test with Healthy Subjects},
year={2020},
volume={E103-D},
number={9},
pages={2027-2031},
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.},
keywords={},
doi={10.1587/transinf.2019EDL8227},
ISSN={1745-1361},
month={September},}

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TY - JOUR
TI - Development of Artificial Neural Network Based Automatic Stride Length Estimation Method Using IMU: Validation Test with Healthy Subjects
T2 - IEICE TRANSACTIONS on Information
SP - 2027
EP - 2031
AU - Yoshitaka NOZAKI
AU - Takashi WATANABE
PY - 2020
DO - 10.1587/transinf.2019EDL8227
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2020
AB - 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.
ER -