Estimation of lower-limb sagittal joint moments during gait using vertical ground reaction force

被引:2
|
作者
Inai T. [1 ]
Takabayashi T. [2 ]
机构
[1] QOL and Materials Research Group, National Institute of Advanced Industrial Science and Technology
[2] Institute for Human Movement and Medical Sciences, Niigata University of Health and Welfare
基金
日本学术振兴会;
关键词
Feedforward neural network; Gait; Ground reaction force; Joint moment; Principal component analysis;
D O I
10.1016/j.jbiomech.2022.111389
中图分类号
学科分类号
摘要
Lower-limb sagittal joint moments during gait are important variables for evaluating the risk of disease progression, such as that of orthopedic diseases. Therefore, quantifying lower-limb sagittal joint moments during walking is important to continuously evaluate the risk of disease progression. A motion capture system and force plate are employed in the calculation of lower-limb sagittal joint moments during gait. However, they cannot be used during daily walking. Therefore, it is important to estimate these moments during walking from the vertical ground reaction force (vGRF), which can be measured using a wearable sensor, such as an insole device. Thus, this study aimed to estimate the lower-limb sagittal joint moments during gait using only the vGRF and confirmed its accuracy. This study included 188 healthy adults, and each participant walked at a comfortable speed (10 trials). We estimated the moments from the vGRF using a feedforward neural network. Our major findings are that our method can estimate lower-limb sagittal joint moments using the vGRF with accuracies of NRMSE¯ within 6.0–11.7% (NRMSEs¯ of the hip, knee, and ankle were 8.4, 11.7, and 6.0%, respectively). To the best of our knowledge, this study is the first to estimate lower-limb sagittal joint moments (including those of the hip, knee, and ankle joints) during gait using only the vGRF. Our method may be useful to estimate lower-limb sagittal joint moments during daily walking using only the vGRF, which can be measured by an insole device in the future. © 2022 Elsevier Ltd
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