BACKGROUND In clinical practice, therapists choose the amount of assistance for robot-assisted training. This can result in outcomes that are influenced by subjective decisions and tuning of training parameters can be time-consuming. Therefore, various algorithms to automatically tune the assistance have been developed. However, the assistance applied by these algorithms has not been directly compared to manually-tuned assistance yet. In this study, we focused on subtask-based assistance and compared automatically-tuned (AT) robotic assistance with manually-tuned (MT) robotic assistance. METHODS Ten people with neurological disorders (six stroke, four spinal cord injury) walked in the LOPES II gait trainer with AT and MT assistance. In both cases, assistance was adjusted separately for various subtasks of walking (in this study defined as control of: weight shift, lateral foot placement, trailing and leading limb angle, prepositioning, stability during stance, foot clearance). For the MT approach, robotic assistance was tuned by an experienced therapist and for the AT approach an algorithm that adjusted the assistance based on performances for the different subtasks was used. Time needed to tune the assistance, assistance levels and deviations from reference trajectories were compared between both approaches. In addition, participants evaluated safety, comfort, effect and amount of assistance for the AT and MT approach. RESULTS For the AT algorithm, stable assistance levels were reached quicker than for the MT approach. Considerable differences in the assistance per subtask provided by the two approaches were found. The amount of assistance was more often higher for the MT approach than for the AT approach. Despite this, the largest deviations from the reference trajectories were found for the MT algorithm. Participants did not clearly prefer one approach over the other regarding safety, comfort, effect and amount of assistance. CONCLUSION Automatic tuning had the following advantages compared to manual tuning: quicker tuning of the assistance, lower assistance levels, separate tuning of each subtask and good performance for all subtasks. Future clinical trials need to show whether these apparent advantages result in better clinical outcomes.

中文翻译：

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机器人辅助步态训练在神经系统疾病患者中的自动调整与手动调整。

背景技术在临床实践中，治疗师选择用于机器人辅助训练的辅助量。这可能会导致结果受到主观决定的影响，并且调整训练参数可能很耗时。因此，已经开发了用于自动调整辅助的各种算法。但是，尚未将这些算法所应用的辅助与手动调整的辅助直接进行比较。在这项研究中，我们专注于基于子任务的协助，并将自动调整（AT）机器人协助与手动调整（MT）机器人协助进行了比较。方法十个神经系统疾病（六个中风，四个脊髓损伤）的人在AT和MT的帮助下走入LOPES II步态训练器。在这两种情况下 针对步行的各个子任务分别调整了辅助功能（在本研究中定义为以下各项的控制：体重转移，侧脚放置，尾随和前肢角度，预置位，站立时的稳定性，足部间隙）。对于MT方法，由经验丰富的治疗师调整机器人辅助，对于AT方法，使用根据不同子任务的性能调整辅助的算法。在两种方法之间比较了调整辅助所需的时间，辅助水平和偏离参考轨迹的时间。此外，参与者评估了AT和MT方法的安全性，舒适性，效果和援助量。结果对于AT算法，比MT方法要更快地达到稳定的辅助水平。发现这两种方法在每个子任务的协助上有很大差异。MT方法比AT方法的援助金额通常更高。尽管如此，对于MT算法，发现与参考轨迹的最大偏差。在安全性，舒适性，效果和援助量方面，参与者显然没有比其他方法更偏爱一种方法。结论与手动调整相比，自动调整具有以下优点：辅助调整更快，辅助级别更低，每个子任务分别调整以及所有子任务的良好性能。未来的临床试验需要证明这些明显的优势是否会带来更好的临床效果。尽管如此，对于MT算法，发现与参考轨迹的最大偏差。在安全性，舒适性，效果和援助量方面，参与者显然没有比其他方法更偏爱一种方法。结论与手动调整相比，自动调整具有以下优点：辅助调整更快，辅助级别更低，每个子任务分别调整以及所有子任务的良好性能。未来的临床试验需要证明这些明显的优势是否会带来更好的临床效果。尽管如此，对于MT算法，发现与参考轨迹的最大偏差。在安全性，舒适性，效果和援助量方面，参与者显然没有比其他方法更偏爱一种方法。结论与手动调整相比，自动调整具有以下优点：辅助调整更快，辅助级别更低，每个子任务分别调整以及所有子任务的良好性能。未来的临床试验需要证明这些明显的优势是否会带来更好的临床效果。辅助调整更快，辅助级别更低，每个子任务分别调整以及所有子任务的良好性能。未来的临床试验需要证明这些明显的优势是否会带来更好的临床效果。辅助调整更快，辅助级别更低，每个子任务分别调整以及所有子任务的良好性能。未来的临床试验需要证明这些明显的优势是否会带来更好的临床效果。