Applied Rehabilitation Technology ART Lab, Department for Trauma Surgery, Orthopaedics and Plastic Surgery, Universitätsmedizin Göttingen (UMG), 37075, Göttingen, Germany.
Bioinspir Biomim. 2021 Oct 12;16(6). doi: 10.1088/1748-3190/ac245f.
Estimation of joints' trajectories is commonly used in human gait analysis, and in the development of motion planners and high-level controllers for prosthetics, orthotics, exoskeletons and humanoids. Human locomotion is the result of the cooperation between leg joints and limbs. This suggests the existence of underlying relationships between them which lead to a harmonic gait. In this study we aimed to estimate knee and ankle trajectories using thigh and shank angles. To do so, an estimation approach was developed that continuously mapped the inputs to the outputs, which did not require switching rules, speed estimation, gait percent identification or look-up tables. The estimation algorithm was based on a nonlinear auto-regressive model with exogenous inputs. The method was then combined with wavelets theory, and then the two were used in a neural network. To evaluate the estimation performance, three scenarios were developed which used only one source of inputs (i.e., only shank angles or only thigh angles). First, knee angles(outputs) were estimated using thigh angles(inputs). Second, ankle angles(outputs) were estimated using thigh angles(inputs), and third, the ankle angles were estimated using shank angles (inputs). The proposed approach was investigated for 22 subjects at different walking speeds and the leave-one-subject-out procedure was used for training and testing the estimation algorithm. Average root mean square errors were 3.9°-5.3° and 2.1°-2.3° for knee and ankle angles, respectively. Average mean absolute errors (MAEs) MAEs were 3.2°-4° and 1.7°-1.8°, and average correlation coefficientswere 0.95-0.98 and 0.94-0.96 for knee and ankle angles, respectively. The limitations and strengths of the proposed approach are discussed in detail and the results are compared with several studies.
关节轨迹估计常用于人体步态分析，以及为假肢、矫形器、外骨骼和仿人机器人开发运动规划器和高级控制器。人类运动是腿部关节和肢体协同作用的结果。这表明它们之间存在潜在的关系，从而导致协调的步态。在这项研究中，我们旨在使用大腿和小腿角度来估计膝盖和脚踝轨迹。为此，开发了一种估计方法，该方法连续将输入映射到输出，而无需切换规则、速度估计、步态百分比识别或查找表。估计算法基于具有外部输入的非线性自回归模型。该方法随后与小波理论结合，并将两者用于神经网络中。为了评估估计性能，开发了三种仅使用一个输入源（即仅使用小腿角度或仅使用大腿角度）的情况。首先，使用大腿角度（输入）估计膝盖角度（输出）。其次，使用大腿角度（输入）估计脚踝角度（输出），第三，使用小腿角度（输入）估计脚踝角度（输出）。该方法针对 22 名不同步行速度的受试者进行了研究，并采用了受试者留一法进行训练和测试估计算法。平均均方根误差（RMSE）分别为 3.9°-5.3°和 2.1°-2.3°，用于膝盖和脚踝角度。平均绝对误差（MAE）分别为 3.2°-4°和 1.7°-1.8°，平均相关系数分别为 0.95-0.98 和 0.94-0.96，用于膝盖和脚踝角度。详细讨论了该方法的局限性和优势，并将结果与几项研究进行了比较。
