Automatic versus manual tuning of robot-assisted gait training in people with neurological disorders
Automatic versus manual tuning of robot-assisted gait training in people with neurological disorders
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DOI：
10.1186/s12984-019-0630-9
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发表时间：
2020-01-28
影响因子：
5.1
通讯作者：
van Asseldonk, Edwin H. F.
中科院分区：
文献类型：
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作者：
Fricke, Simone S.;Bayon, Cristina;van Asseldonk, Edwin H. F.
背景 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，并且训练参数的调整可能非常耗时。因此，已经开发了各种自动调整辅助的算法。然而，这些算法所应用的辅助尚未与手动调整的辅助进行直接比较。在本研究中，我们重点关注基于子任务的辅助，并将自动调整 (AT) 机器人辅助与手动调整 (MT) 机器人辅助进行比较。方法 10 名神经系统疾病患者（其中 6 名中风，4 名脊髓损伤）在 AT 和 MT 辅助下在 LOPES II 步态训练器中行走。在这两种情况下，针对步行的各种子任务分别调整辅助（在本研究中定义为控制：重心转移、脚外侧放置、后肢和前肢角度、预定位、站立期间的稳定性、脚间隙）。对于 MT 方法，机器人辅助由经验丰富的治疗师进行调整，对于 AT 方法，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、辅助水平以及与参考轨迹的偏差。此外，参与者还评估了 AT 和 MT 方法的安全性、舒适性、效果和辅助量。结果 AT 算法比 MT 方法更快地达到稳定的辅助水平。我们发现这两种方法为每个子任务提供的帮助存在很大差异。 MT 方法的援助金额通常高于 AT 方法。尽管如此，MT 算法仍发现与参考轨迹的最大偏差。在安全性、舒适度、效果和帮助量方面，参与者并没有明显地偏好其中一种方法。结论 与手动调优相比，自动调优具有辅助调优速度快、辅助级别低、每个子任务单独调优、所有子任务性能良好等优点。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结果。
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.