Hocoma AG, Volketswil, Switzerland.
ETH Transfer, ETH Zurich, Zurich, Switzerland.
J Neuroeng Rehabil. 2023 Sep 21;20(1):121. doi: 10.1186/s12984-023-01226-4.
Walking impairments are a common consequence of neurological disorders and are assessed with clinical scores that suffer from several limitations. Robot-assisted locomotor training is becoming an established clinical practice. Besides training, these devices could be used for assessing walking ability in a controlled environment. Here, we propose an adaptive assist-as-needed (AAN) control for a treadmill-based robotic exoskeleton, the Lokomat, that reduces the support of the device (body weight support and impedance of the robotic joints) based on the ability of the patient to follow a gait pattern displayed on screen. We hypothesize that the converged values of robotic support provide valid and reliable information about individuals' walking ability.
Fifteen participants with spinal cord injury and twelve controls used the AAN software in the Lokomat twice within a week and were assessed using clinical scores (10MWT, TUG). We used a regression method to identify the robotic measure that could provide the most relevant information about walking ability and determined the test-retest reliability. We also checked whether this result could be extrapolated to non-ambulatory and to unimpaired subjects.
The AAN controller could be used in patients with different injury severity levels. A linear model based on one variable (robotic knee stiffness at terminal swing) could explain 74% of the variance in the 10MWT and 61% in the TUG in ambulatory patients and showed good relative reliability but poor absolute reliability. Adding the variable 'maximum hip flexor torque' to the model increased the explained variance above 85%. This did not extend to non-ambulatory nor to able-bodied individuals, where variables related to stance phase and to push-off phase seem more relevant.
The novel AAN software for the Lokomat can be used to quantify the support required by a patient while performing robotic gait training. The adaptive software might enable more challenging training conditions tuned to the ability of the individuals. While the current implementation is not ready for assessment in clinical practice, we could demonstrate that this approach is safe, and it could be integrated as assist-as-needed training, rather than as assessment.
ClinicalTrials.gov Identifier: NCT02425332.
行走障碍是神经障碍的常见后果，其评估采用临床评分，但存在多种局限性。机器人辅助运动训练正成为一种既定的临床实践。除了训练之外，这些设备还可以用于在受控环境中评估行走能力。在这里，我们为基于跑步机的机器人外骨骼 Lokomat 提出了一种自适应按需辅助 (AAN) 控制，该控制根据患者跟随屏幕上显示的步态模式的能力，减少设备的支撑（体重支撑和机器人关节的阻抗）。我们假设机器人支撑的收敛值可以提供有关个体行走能力的有效且可靠的信息。
15 名脊髓损伤患者和 12 名对照组在一周内两次使用 Lokomat 中的 AAN 软件，并使用临床评分（10MWT、TUG）进行评估。我们使用回归方法来确定能够提供与行走能力最相关信息的机器人测量值，并确定测试-重测的可靠性。我们还检查了该结果是否可以外推至非步行和非健全个体。
AAN 控制器可用于不同损伤严重程度的患者。基于一个变量（末端摆动时的机器人膝关节刚度）的线性模型可以解释 10MWT 中 74%的方差和 TUG 中 61%的方差，在步行患者中显示出良好的相对可靠性，但绝对可靠性较差。将“最大髋关节屈肌扭矩”变量添加到模型中，可将解释的方差增加到 85%以上。但这不适用于非步行和健全个体，在这些个体中，与站立阶段和推离阶段相关的变量似乎更为重要。
新型 Lokomat 的 AAN 软件可用于量化患者在进行机器人步态训练时所需的支撑。自适应软件可以为个体能力提供更具挑战性的训练条件。虽然当前的实施还不能用于临床实践中的评估，但我们可以证明这种方法是安全的，可以将其作为按需辅助训练而不是评估来进行集成。
ClinicalTrials.gov 标识符：NCT02425332。
