Learning Post-Stroke Gait Training Strategies by Modeling Patient-Therapist Interaction
Learning Post-Stroke Gait Training Strategies by Modeling Patient-Therapist Interaction
复制标题
通过模拟患者与治疗师的互动来学习中风后步态训练策略
DOI：
10.1109/tnsre.2023.3253795
复制
发表时间：
2023
影响因子：
4.9
通讯作者：
Zhang, Wenlong
中科院分区：
文献类型：
--
作者：
Rezayat Sorkhabadi, Seyed Mostafa;Smith, Mason;Khodmbashi, Roozbeh;Lopez, Rachel;Raasch, Melissa;Maruyama, Trent;Kwasnica, Christina;Zhang, Wenlong
为了安全有效地进行机器人辅助步态训练，必须结合物理治疗师的知识和专业知识。为了实现这一目标，我们直接从物理治疗师的示范中学习手动步态辅助中风康复。使用可穿戴传感系统测量患者的下肢运动学和治疗师对患者腿部施加的辅助力，该系统包括定制的力传感阵列。然后，收集到的数据用于描述治疗师针对患者步态中发现的独特步态行为的策略。初步分析表明，膝关节伸展和体重转移是塑造治疗师辅助策略的最重要特征。然后将这些关键特征整合到虚拟阻抗模型中，以预测治疗师的辅助扭矩。该模型得益于目标导向的吸引子和代表性特征，这些特征允许对治疗师的援助策略进行直观的表征和估计。由此产生的模型能够准确地捕捉到治疗师在整个训练过程中的高水平行为（r2 = 0.92, RMSE = 0.23Nm），同时还能解释一些包含在个别跨步中的更细微的行为（r2 = 0.53, RMSE = 0.61Nm）。这项工作提供了一种控制可穿戴机器人的新方法，直接将物理治疗师的决策过程编码为安全的人机交互框架，用于步态康复。
For safe and effective robot-aided gait training, it is essential to incorporate the knowledge and expertise of physical therapists. Toward this goal, we directly learn from physical therapists’ demonstrations of manual gait assistance in stroke rehabilitation. Lower-limb kinematics of patients and assistive force applied by therapists to the patient’s leg are measured using a wearable sensing system which includes a custom-made force sensing array. The collected data is then used to characterize a therapist’s strategies in response to unique gait behaviors found within a patient’s gait. Preliminary analysis shows that knee extension and weight-shifting are the most important features that shape a therapist’s assistance strategies. These key features are then integrated into a virtual impedance model to predict the therapist’s assistive torque. This model benefits from a goal-directed attractor and representative features that allow intuitive characterization and estimation of a therapist’s assistance strategies. The resulting model is able to accurately capture high-level therapist behaviors over the course of a full training session (r2 = 0.92, RMSE = 0.23Nm) while still explaining some of the more nuanced behaviors contained in individual strides (r2 = 0.53, RMSE = 0.61Nm). This work provides a new approach to control wearable robotics in the sense of directly encoding the decision-making process of physical therapists into a safe human-robot interaction framework for gait rehabilitation.
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DOI：
10.1682/jrrd.2005.06.0103
发表时间：
2006-08-01
影响因子：
--
作者：
Hogan, Neville;Krebs, Hermano I.;Volpe, Bruce T.
通讯作者：
Volpe, Bruce T.
DOI：
10.1115/1.4033949
发表时间：
2016-11-01
影响因子：
1.7
作者：
Zhang, Wenlong;Tomizuka, Masayoshi;Byl, Nancy
通讯作者：
Byl, Nancy
影响因子：
2.2
作者：
Andersson, Pontus;Franzen, Erika
通讯作者：
Franzen, Erika
影响因子：
5.1
作者：
Vaughan-Graham, Julie;Brooks, Dina;Patterson, Kara
通讯作者：
Patterson, Kara
影响因子：
5.1
作者：
Bryan GM;Franks PW;Song S;Reyes R;O'Donovan MP;Gregorczyk KN;Collins SH
通讯作者：
Collins SH