Spine & Movement Biomechanics Lab, Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada.
Spine & Movement Biomechanics Lab, Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada.
Gait Posture. 2023 Jul;104:90-96. doi: 10.1016/j.gaitpost.2023.06.006. Epub 2023 Jun 9.
The development of plantar pressure insoles has made them a potential replacement for force plates. These wearable devices can measure multiple steps and might be used outside of the lab environment for rehabilitation and evaluation of sport performance. However, they can only measure the vertical force which does not completely represent the vertical ground reaction force. In addition, they are not able to measure shear forces which play an import role in the dynamic performance of individuals. Indirect approaches might be implemented to improve the accuracy of the force estimated by plantar pressure systems.
The aim of this study was to predict the vertical and shear components of ground reaction force from plantar pressure data using recurrent neural networks.
Ground reaction force and plantar pressure data were collected from 16 healthy individuals during 10 trials of walking and five trials of jogging using Bertec force plates at 1000 Hz and FScan plantar pressure insoles at 100 Hz. A long short-term memory neural network was built to consider the time dependency of pressure and force data in predictions. The data were split into three subsets of train, to train the model, evaluate, to optimize the model hyperparameters, and test sets, to assess the accuracy of the model predictions.
The results of this study showed that our long short-term memory model could accurately predict the shear and vertical force components during walking and jogging. The predictions were more accurate during walking compared to jogging. In addition, the predictions of mediolateral force had higher error and lower correlation compared to vertical and anteroposterior components.
The long short-term memory model developed in this study may be an acceptable option for accurate estimation of ground reaction force during outdoor activities which can have significant impacts in rehabilitation, sport performance, and gaming.
足底压力鞋垫的发展使它们成为力板的潜在替代品。这些可穿戴设备可以测量多个步骤，并且可以在实验室环境之外用于康复和评估运动表现。然而，它们只能测量垂直力，而不能完全代表垂直地面反作用力。此外，它们无法测量剪切力，而剪切力在个体的动态性能中起着重要作用。可以采用间接方法来提高足底压力系统估计力的准确性。
本研究的目的是使用递归神经网络从足底压力数据预测地面反作用力的垂直和剪切分量。
使用 Bertec 力板以 1000 Hz 和 FScan 足底压力鞋垫以 100 Hz 的频率收集 16 名健康个体在 10 次步行试验和 5 次慢跑试验中的地面反作用力和足底压力数据。构建长短期记忆神经网络来考虑压力和力数据在预测中的时间依赖性。数据分为三个子集：训练集用于训练模型，评估集用于优化模型超参数，测试集用于评估模型预测的准确性。
本研究的结果表明，我们的长短期记忆模型可以准确预测步行和慢跑时的剪切力和垂直力分量。与慢跑相比，预测在步行时更准确。此外，与垂直和前后向分量相比，横向力的预测误差更高，相关性更低。
本研究中开发的长短期记忆模型可能是准确估计户外活动中地面反作用力的一种可接受的选择，这在康复、运动表现和游戏等方面具有重要意义。
