Inertial-measurement-unit (IMU)-based wearable gait-monitoring systems provide kinematic information but kinetic information, such as ground reaction force (GRF) are often needed to assess gait symmetry and joint loading. Recent studies have reported methods for predicting GRFs from IMU measurement data by using artificial neural networks (ANNs). To obtain reliable predictions, the ANN requires a large number of measurement inputs at the cost of wearable convenience. Recognizing that the dynamic relationship between the center of mass (CoM) and GRF can be well represented by using spring mechanics, in this study we propose two GRF prediction methods based on the implementation of walking dynamics in a neural network. Method 1 takes inputs to the network that were CoM kinematics data and Method 2 employs forces approximated from CoM kinematics by applying spring mechanics. The gait data of seven young healthy subjects were collected at various walking speeds. Leave-one-subject-out cross-validation was performed with normalized root mean square error and r as quantitative measures of prediction performance. The vertical and anteroposterior (AP) GRFs obtained using both methods agreed well with the experimental data, but Method 2 yielded improved predictions of AP GRF compared to Method 1 (p = 0.005). These results imply that knowledge of the dynamic characteristics of walking, combined with a neural network, could enhance the efficiency and accuracy of GRF prediction and help resolve the tradeoff between information richness and wearable convenience of wearable technologies.

基于惯性测量单元（IMU）的可穿戴步态监测系统可提供运动学信息，但通常需要动力学信息（例如地面反作用力（GRF））来评估步态对称性和关节负荷。最近的研究报告了通过使用人工神经网络（ANN）从IMU测量数据预测GRF的方法。为了获得可靠的预测，ANN需要大量的测量输入，但以佩戴方便为代价。认识到质心（CoM）和GRF之间的动力学关系可以通过使用弹簧力学很好地表示，在这项研究中，我们提出了两种基于神经网络中行走动力学实现的GRF预测方法。方法1将作为CoM运动学数据的网络输入作为输入，方法2通过应用弹簧力学采用从CoM运动学近似的力。以不同的步行速度收集了七名年轻健康受试者的步态数据。采用标准化的均方根误差和r作为预测绩效的定量指标。使用这两种方法获得的垂直和前后（GAP）GRF与实验数据吻合得很好，但是与方法1相比，方法2对AP GRF的预测得到了改进（p = 0.005）。这些结果表明，有关步行动态特征的知识与神经网络相结合，可以提高GRF预测的效率和准确性，并有助于解决信息丰富性与可穿戴技术的可穿戴便利性之间的折衷问题。