Department of Medical Engineering, University of South Florida, Tampa, 33620, USA.
Department of Mechanical Engineering, University of South Florida, Tampa, 33620, USA.
J Neuroeng Rehabil. 2022 Jun 30;19(1):65. doi: 10.1186/s12984-022-01044-0.
Split-belt treadmill training has been used to assist with gait rehabilitation following stroke. This method modifies a patient's step length asymmetry by adjusting left and right tread speeds individually during training. However, current split-belt training approaches pay little attention to the individuality of patients by applying set tread speed ratios (e.g., 2:1 or 3:1). This generalization results in unpredictable step length adjustments between the legs. To customize the training, this study explores the capabilities of a live feedback system that modulates split-belt tread speeds based on real-time step length asymmetry.
Fourteen healthy individuals participated in two 1.5-h gait training sessions scheduled 1 week apart. They were asked to walk on the Computer Assisted Rehabilitation Environment (CAREN) split-belt treadmill system with a boot on one foot to impose asymmetrical gait patterns. Each training session consisted of a 3-min baseline, 10-min baseline with boot, 10-min feedback with boot (6% asymmetry exaggeration in the first session and personalized in the second), 5-min post feedback with boot, and 3-min post feedback without boot. A proportional-integral (PI) controller was used to maintain a specified step-length asymmetry by changing the tread speed ratios during the 10-min feedback period. After the first session, a linear model between baseline asymmetry exaggeration and post-intervention asymmetry improvement was utilized to develop a relationship between target exaggeration and target post-intervention asymmetry. In the second session, this model predicted a necessary target asymmetry exaggeration to replace the original 6%. This prediction was intended to result in a highly symmetric post-intervention step length.
Eleven out of 14 participants (78.6%) developed a successful relationship between asymmetry exaggeration and decreased asymmetry in the post-intervention period of the first session. Seven out of the 11 participants (63.6%) in this successful correlation group had second session post-intervention asymmetries of < 3.5%.
The use of a PI controller to modulate split-belt tread speeds demonstrated itself to be a viable method for individualizing split-belt treadmill training.
分带跑步机训练已被用于辅助中风后的步态康复。这种方法通过在训练过程中分别调整左右履带速度来改变患者的步长不对称。然而，目前的分带训练方法通过应用固定的履带速度比（例如 2:1 或 3:1）对患者的个体差异关注甚少。这种概括导致双腿之间的步长调整不可预测。为了实现个性化训练，本研究探索了一种基于实时步长不对称性来调节分带履带速度的实时反馈系统的能力。
14 名健康个体参加了两次间隔一周的 1.5 小时步态训练。他们被要求穿着一只脚的靴子在 CAREN 分带跑步机系统上行走，以形成不对称的步态模式。每次训练包括 3 分钟基线、10 分钟基线加靴子、10 分钟带靴子的反馈（第一次反馈中夸大 6%的不对称性，第二次反馈中个性化）、5 分钟带靴子的反馈后、3 分钟不带靴子的反馈后。在 10 分钟的反馈期间，使用比例积分（PI）控制器通过改变履带速度比来维持指定的步长不对称性。在第一次训练后，利用基线不对称性夸大与干预后不对称性改善之间的线性模型，建立目标夸大与干预后目标不对称性之间的关系。在第二次训练中，该模型预测了必要的目标不对称性夸大以取代原始的 6%。这一预测旨在产生一个高度对称的干预后步长。
在第一次训练的干预后阶段，14 名参与者中有 11 名（78.6%）成功建立了不对称性夸大与不对称性降低之间的关系。在这个成功相关组的 11 名参与者中有 7 名（63.6%）在第二次训练中的干预后不对称性<3.5%。
使用 PI 控制器来调节分带履带速度被证明是一种可行的方法，可以实现分带跑步机训练的个性化。
