Department of Biomechanical Engineering, University of Twente, Enschede, The Netherlands.
Department of BioMechanical Engineering, Delft University of Technology, Delft, The Netherlands.
J Neuroeng Rehabil. 2020 Jan 28;17(1):9. doi: 10.1186/s12984-019-0630-9.
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.
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.
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.
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.
在临床实践中，治疗师会选择机器人辅助训练的辅助量。这可能会导致结果受到主观决策的影响，并且调整训练参数可能会很耗时。因此，已经开发了各种自动调整辅助的算法。然而，这些算法应用的辅助尚未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辅助，并比较了自动调整（AT）机器人辅助和手动调整（MT）机器人辅助。
10 名患有神经疾病的人（6 名中风，4 名脊髓损伤）在 LOPES II 步态训练器中使用 AT 和 MT 辅助进行行走。在这两种情况下，分别针对行走的各种子任务（在本研究中定义为控制：重心转移、侧足放置、后导和前导肢体角度、预定位、站立稳定性、足廓清）调整机器人辅助。对于 MT 方法，机器人辅助由经验丰富的治疗师进行调整，而对于 AT 方法，则使用根据不同子任务的表现调整辅助的算法。比较了两种方法之间调整辅助所需的时间、辅助水平和偏离参考轨迹的情况。此外，参与者评估了 AT 和 MT 方法的安全性、舒适度、效果和辅助量。
对于 AT 算法，与 MT 方法相比，更快地达到稳定的辅助水平。发现两种方法提供的每个子任务的辅助水平存在相当大的差异。与 AT 方法相比，MT 方法提供的辅助量通常更高。尽管如此，最大的偏离发生在 MT 算法中。参与者在安全性、舒适度、效果和辅助量方面并没有明显倾向于一种方法而不是另一种方法。
与手动调整相比，自动调整具有以下优势：更快地调整辅助、更低的辅助水平、单独调整每个子任务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导致更好的临床结果。
