TY - JOUR
T1 - Development of artificial neural network based automatic stride length estimation method using IMU
T2 - Validation test with healthy subjects
AU - Nozaki, Yoshitaka
AU - Watanabe, Takashi
N1 - Funding Information:
This work was partly supported by JSPS KAKENHI Grant Number JP19K22735.
Publisher Copyright:
© 2020 The Institute of Electronics, Information and Communication Engineers
PY - 2020/9/1
Y1 - 2020/9/1
N2 - 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.
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.
KW - Gait
KW - Inertial sensor
KW - Neural-network
KW - Rehabilitation
KW - Stride-length
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U2 - 10.1587/transinf.2019EDL8227
DO - 10.1587/transinf.2019EDL8227
M3 - Article
AN - SCOPUS:85092066027
VL - E103D
SP - 2027
EP - 2031
JO - IEICE Transactions on Information and Systems
JF - IEICE Transactions on Information and Systems
SN - 0916-8532
IS - 9
ER -