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 (R2) 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 p > 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.

中文翻译：

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跨行走条件及其转换的未来踝关节角度和力矩的连续肌电预测。

人类运动的一个标志是它不断适应环境的变化并预测性地适应地形的变化，由于假肢和控制算法的限制，这两者都是下肢截肢者面临的主要挑战。在这里，检查了单网络非线性自回归模型使用下肢表面肌电图 (EMG) 信号在步行条件下同时连续预测未来踝关节运动学和动力学的能力。来自十名健康年轻人的踝关节跖屈肌和背屈肌 EMG 被映射到在水平地上行走、楼梯上升和楼梯下降期间的踝关节角度和踝关节力矩的正常范围，包括地形之间的过渡（即，过渡到/从楼梯）。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 EMG/角度/力矩输入值随时间的数量（采样窗口）以及网络隐藏层中最小化实验测量值（目标）与脚踝角度和力矩模型预测之间误差的单元数。脚踝角度和力矩预测在步行条件下是稳健的，均方根误差分别小于 1° 和 0.04 Nm/kg，并且互相关 (R2) 大于 0.99，预测间隔为 58 毫秒。与旅行相关的跌倒风险关​​键点的模型预测落在脚踝角度和力矩目标的可变性范围内（Benjamini-Hochberg 调整后的 p > 0.065）。EMG 对踝关节角度和力矩预测的贡献在步行条件和模型输出中始终如一。EMG 信号对步态的非循环区域影响最大，例如双肢支撑、地形之间的过渡以及跖屈和力矩峰值附近。使用自然肌肉激活模式来持续预测正常步态的变化以及该模型抵消机电固有延迟的预测能力表明，这种方法可以为各种下肢机器人设备提供强大而直观的用户驱动实时控制，包括有源动力踝足假肢。地形之间以及跖屈和力矩峰值附近的过渡。使用自然肌肉激活模式来持续预测正常步态的变化，以及该模型抵消机电固有延迟的预测能力表明，这种方法可以为各种下肢机器人设备提供强大且直观的用户驱动实时控制，包括有源动力踝足假肢。地形之间以及跖屈和力矩峰值附近的过渡。使用自然肌肉激活模式来持续预测正常步态的变化以及该模型抵消机电固有延迟的预测能力表明，这种方法可以为各种下肢机器人设备提供强大而直观的用户驱动实时控制，包括有源动力踝足假肢。