Powered prostheses conventionally rely on impedance controllers that require extensive manual tuning and explicit mode classification. In this work, we present real-time deployment of an end-to-end prosthesis controller that estimates continuous actuator signals from onboard sensors, eliminating the need for intent classifiers and subject-specific tuning. Temporal Convolutional Networks were trained on a multi-terrain dataset from 18 individuals with transfemoral amputation and deployed in real time across five locomotion modes. Four participants (three able-bodied, one with transfemoral amputation) ambulated across level ground, ramp ascent and descent, and stair ascent and descent. During level walking, the deployed controller reproduced the training-data scaling of peak ankle torque with walking speed (deployed 0.85 Nm/kg per m/s, p = 0.001; training 0.96 Nm/kg per m/s, 95% CI [0.42, 1.50], p = 0.002), after excluding one outlier traced to atypical prosthesis loading. During ramp ascent, the controller scaled knee pre-flexion with grade (deployed 2.92 deg/deg, p = 0.027; training 3.30 deg/deg, 95% CI [1.83, 4.77], p < 0.001). During ramp descent, the controller increased resistive knee torque relative to level walking (deployed +0.16 Nm/kg, p < 0.001; training +0.16 Nm/kg, p = 0.008). Seamless stair transitions were generated for both intact- and prosthetic-side-leading sequences in ascent and descent, despite the training data containing only one limb-leading sequence. These results provide initial evidence towards end-to-end control that can provide unified, mode-adaptive prosthetic assistance without subject-specific tuning.

翻译：动力假肢传统上依赖需要大量手动调谐和显式模式分类的阻抗控制器。本研究展示了端到端假肢控制器的实时部署，该控制器通过板载传感器估算连续执行器信号，消除了对意图分类器和受试者特定调谐的需求。基于18例经股截肢受试者的多地形数据集训练的时间卷积网络被部署，并在五种运动模式下实时运行。四名参与者（三名健全受试者，一名经股截肢受试者）行进了平地、斜坡上行与下行、楼梯上行与下行。在平地行走中，部署的控制器再现了踝关节峰值力矩随行走速度的训练数据缩放关系（部署参数：0.85 Nm/kg per m/s，p = 0.001；训练参数：0.96 Nm/kg per m/s，95% CI [0.42, 1.50]，p = 0.002），排除一个因非典型假肢负载导致的异常值后。在斜坡上行中，控制器按坡度缩放膝关节预屈角（部署参数：2.92 deg/deg，p = 0.027；训练参数：3.30 deg/deg，95% CI [1.83, 4.77]，p < 0.001）。在斜坡下行中，控制器相对于平地行走增加了膝关节阻力矩（部署参数：+0.16 Nm/kg，p < 0.001；训练参数：+0.16 Nm/kg，p = 0.008）。尽管训练数据仅包含一种肢体引导序列，但控制器在上下楼梯中为健侧和假肢侧交替引导序列生成了无缝切换。这些结果提供了端到端控制的初步证据，表明其能在无需受试者特定调谐的情况下提供统一、模式自适应的假肢辅助。