Department of Biomedical Engineering, National Taiwan University, Taipei 10617, Taiwan.
Department of Information Management, National Taiwan University, Taipei 10617, Taiwan.
Sensors (Basel). 2023 Nov 8;23(22):9040. doi: 10.3390/s23229040.
Monitoring dynamic balance during gait is critical for fall prevention in the elderly. The current study aimed to develop recurrent neural network models for extracting balance variables from a single inertial measurement unit (IMU) placed on the sacrum during walking. Thirteen healthy young and thirteen healthy older adults wore the IMU during walking and the ground truth of the inclination angles (IA) of the center of pressure to the center of mass vector and their rates of changes (RCIA) were measured simultaneously. The IA, RCIA, and IMU data were used to train four models (uni-LSTM, bi-LSTM, uni-GRU, and bi-GRU), with 10% of the data reserved to evaluate the model errors in terms of the root-mean-squared errors (RMSEs) and percentage relative RMSEs (rRMSEs). Independent -tests were used for between-group comparisons. The sensitivity, specificity, and Pearson's r for the effect sizes between the model-predicted data and experimental ground truth were also obtained. The bi-GRU with the weighted MSE model was found to have the highest prediction accuracy, computational efficiency, and the best ability in identifying statistical between-group differences when compared with the ground truth, which would be the best choice for the prolonged real-life monitoring of gait balance for fall risk management in the elderly.
监测步态中的动态平衡对于预防老年人跌倒至关重要。本研究旨在开发一种基于递归神经网络的模型,从放置在骶骨上的单个惯性测量单元(IMU)中提取行走时的平衡变量。13 名健康的年轻成年人和 13 名健康的老年人在行走时佩戴了 IMU,同时测量了重心压力向量与质心向量之间的倾斜角度(IA)及其变化率(RCIA)的真实值。IA、RCIA 和 IMU 数据用于训练四个模型(单 LSTM、双 LSTM、单 GRU 和双 GRU),其中 10%的数据保留用于根据均方根误差(RMSE)和百分比相对 RMSE(rRMSE)评估模型误差。使用独立样本 t 检验进行组间比较。还获得了模型预测数据与实验地面真实数据之间的效应量的敏感性、特异性和 Pearson r。与地面真实值相比,加权均方误差模型的双 GRU 具有最高的预测准确性、计算效率和识别组间统计差异的能力,这将是老年人跌倒风险管理中进行长时间真实生活步态平衡监测的最佳选择。