Gait pattern prediction is an essential function of individualized motion control of the lower-limb exoskeleton. This paper presents a novel gait pattern prediction model based on the least absolute shrinkage and selection operator (LASSO) regression. Gait data were collected from one hundred and twenty healthy young adults (78 males and 42 females), who were instructed to walk back and forth on a 12-meter-long walking platform while having their heel strike coinciding with the beat of a metronome. The lower-limb joint (i.e., the hip, knee and ankle) angular kinematics were segmented, resampled and transformed into Fourier coefficients. The LASSO regression model with age, gender and 14 anthropometric parameters as prediction variables was trained and used to estimate the Fourier coefficients which were then applied in the lower-limb joint angle trajectory reconstruction. The results showed that the root mean square errors between the actual and predicted joint angle trajectories ranged from 3.41° to 4.55°. The parameters of the linear fit method further revealed the waveform similarity between the actual and predicted joint angle time series. These results suggested that the proposed model was able to accurately predict lower-limb joint kinematics during gait. Application of the proposed model can help resolve the overfitting problem, and provides a new solution to individualized gait pattern prediction.

步态模式预测是下肢外骨骼个性化运动控制的基本功能。本文提出了一种基于最小绝对收缩和选择算子（LASSO）回归的新型步态模式预测模型。步态数据是从120位健康的年轻人（78位男性和42位女性）中收集的，他们被指示在一个12米长的行走平台上来回走动，同时其脚跟撞击与节拍器的节拍相吻合。将下肢关节（即髋部，膝盖和踝部）的角度运动学进行分段，重采样并转换为傅立叶系数。具有年龄的LASSO回归模型，性别和14个人体测量学参数作为预测变量被训练并用于估计傅立叶系数，然后将其应用于下肢关节角轨迹重建。结果表明，实际和预测的关节角轨迹之间的均方根误差范围为3.41°至4.55°。线性拟合方法的参数进一步揭示了实际和预测的关节角度时间序列之间的波形相似性。这些结果表明，所提出的模型能够准确地预测步态下肢的关节运动学。该模型的应用可以帮助解决过拟合问题，并为个性化步态模式预测提供了新的解决方案。结果表明，实际和预测的关节角轨迹之间的均方根误差范围为3.41°至4.55°。线性拟合方法的参数进一步揭示了实际和预测的关节角度时间序列之间的波形相似性。这些结果表明，所提出的模型能够准确地预测步态下肢的关节运动学。该模型的应用可以帮助解决过拟合问题，并为个性化步态模式预测提供了新的解决方案。结果表明，实际和预测的关节角轨迹之间的均方根误差范围为3.41°至4.55°。线性拟合方法的参数进一步揭示了实际和预测的关节角度时间序列之间的波形相似性。这些结果表明，所提出的模型能够准确地预测步态下肢的关节运动学。该模型的应用可以帮助解决过拟合问题，并为个性化步态模式预测提供了新的解决方案。