Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
Biomedical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu 632014, India.
J Healthc Eng. 2021 Feb 18;2021:5541255. doi: 10.1155/2021/5541255. eCollection 2021.
A high-accuracy gait data prediction model can be used to design prosthesis and orthosis for people having amputations or ailments of the lower limb. The objective of this study is to observe the gait data of different subjects and design a neural network to predict future gait angles for fixed speeds. The data were recorded via a Biometrics goniometer, while the subjects were walking on a treadmill for 20 seconds each at 2.4 kmph, 3.6 kmph, and 5.4 kmph. The data were then imported into Matlab, filtered to remove movement artifacts, and then used to design a neural network with 60% data for training, 20% for validation, and remaining 20% for testing using the LevenbergMarquardt method. The mean-squared error for all the cases was in the order of 10 or lower confirming that our method is correct. For further comparison, we randomly tested the neural network function with untrained data and compared the expected output with actual output of the neural network function using Pearson's correlation coefficient and correlation plots. We conclude that our framework can be successfully used to design prosthesis and orthosis for lower limb. It can also be used to validate gait data and compare it to expected data in rehabilitation engineering.
一种高精度的步态数据预测模型可用于为下肢截肢或患病的人设计假肢和矫形器。本研究的目的是观察不同受试者的步态数据，并设计一个神经网络来预测固定速度下的未来步态角度。数据通过 Biometrics 测角仪记录，受试者在跑步机上以 2.4、3.6 和 5.4 公里/小时的速度行走 20 秒。然后将数据导入 Matlab，进行滤波以去除运动伪影，然后使用 Levenberg-Marquardt 方法使用 60%的数据进行训练、20%的数据进行验证和剩余 20%的数据进行测试来设计神经网络。所有情况下的均方误差都在 10 以内，这证实了我们的方法是正确的。为了进一步比较，我们使用未经训练的数据随机测试了神经网络功能，并使用 Pearson 相关系数和相关图比较了神经网络功能的预期输出和实际输出。我们得出结论，我们的框架可成功用于设计下肢假肢和矫形器。它还可用于验证步态数据并将其与康复工程中的预期数据进行比较。
