Zabre-Gonzalez Erika V, Riem Lara, Voglewede Philip A, Silver-Thorn Barbara, Koehler-McNicholas Sara R, Beardsley Scott A
Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, United States.
Department of Mechanical Engineering, Marquette University, Milwaukee, WI, United States.
Front Neurosci. 2021 Aug 18;15:709422. doi: 10.3389/fnins.2021.709422. eCollection 2021.
A hallmark of human locomotion is that it continuously adapts to changes in the environment and predictively adjusts to changes in the terrain, both of which are major challenges to lower limb amputees due to the limitations in prostheses and control algorithms. Here, the ability of a single-network nonlinear autoregressive model to continuously predict future ankle kinematics and kinetics simultaneously across ambulation conditions using lower limb surface electromyography (EMG) signals was examined. Ankle plantarflexor and dorsiflexor EMG from ten healthy young adults were mapped to normal ranges of ankle angle and ankle moment during level overground walking, stair ascent, and stair descent, including transitions between terrains (i.e., transitions to/from staircase). Prediction performance was characterized as a function of the time between current EMG/angle/moment inputs and future angle/moment model predictions (prediction interval), the number of past EMG/angle/moment input values over time (sampling window), and the number of units in the network hidden layer that minimized error between experimentally measured values (targets) and model predictions of ankle angle and moment. Ankle angle and moment predictions were robust across ambulation conditions with root mean squared errors less than 1° and 0.04 Nm/kg, respectively, and cross-correlations (R) greater than 0.99 for prediction intervals of 58 ms. Model predictions at critical points of trip-related fall risk fell within the variability of the ankle angle and moment targets (Benjamini-Hochberg adjusted > 0.065). EMG contribution to ankle angle and moment predictions occurred consistently across ambulation conditions and model outputs. EMG signals had the greatest impact on noncyclic regions of gait such as double limb support, transitions between terrains, and around plantarflexion and moment peaks. The use of natural muscle activation patterns to continuously predict variations in normal gait and the model's predictive capabilities to counteract electromechanical inherent delays suggest that this approach could provide robust and intuitive user-driven real-time control of a wide variety of lower limb robotic devices, including active powered ankle-foot prostheses.
人类运动的一个显著特征是它能不断适应环境变化并对地形变化进行预测性调整，而由于假肢和控制算法的局限性，这两者对下肢截肢者来说都是重大挑战。在此，研究了单网络非线性自回归模型利用下肢表面肌电图（EMG）信号在不同行走条件下同时连续预测未来踝关节运动学和动力学的能力。在水平地面行走、上楼梯和下楼梯（包括地形转换，即进出楼梯的转换）过程中，将十名健康年轻成年人的踝关节跖屈肌和背屈肌EMG映射到踝关节角度和踝关节力矩的正常范围。预测性能被表征为当前EMG/角度/力矩输入与未来角度/力矩模型预测之间的时间（预测间隔）、过去随时间变化的EMG/角度/力矩输入值的数量（采样窗口）以及网络隐藏层中使实验测量值（目标值）与踝关节角度和力矩的模型预测之间误差最小化的单元数量的函数。在不同行走条件下，踝关节角度和力矩预测具有稳健性，均方根误差分别小于1°和0.04 Nm/kg，对于58 ms的预测间隔，互相关（R）大于0.99。与绊倒相关的跌倒风险关键点处的模型预测落在踝关节角度和力矩目标值的变化范围内（Benjamini-Hochberg校正后>0.065）。在不同行走条件和模型输出中，EMG对踝关节角度和力矩预测的贡献始终存在。EMG信号对步态的非周期性区域影响最大，如双支撑期、地形转换以及跖屈和力矩峰值附近。利用自然肌肉激活模式连续预测正常步态变化以及模型的预测能力来抵消机电固有延迟，表明这种方法可为包括主动动力踝足假肢在内的各种下肢机器人设备提供稳健且直观的用户驱动实时控制。
