Abstract
Post-stroke rehabilitation is often necessary for patients to regain proper walking gait. However, the typical therapy process can be exhausting and physically demanding for therapists, potentially reducing therapy intensity, duration, and consistency over time. We propose a Patient-Therapist Force Field (PTFF) to visualize therapist responses to patient kinematics and a Synthetic Therapist (ST) machine learning model to support the therapist in dyadic robot-mediated physical interaction therapy. The first encodes patient and therapist stride kinematics into a shared low-dimensional latent manifold using a Variational Autoencoder (VAE) and models their interaction through a Gaussian Mixture Model (GMM), which learns a probabilistic vector field mapping patient latent states to therapist responses. This representation visualizes patient-therapist interaction dynamics to inform therapy strategies and robot controller design. The latter is implemented as a Long Short-Term Memory (LSTM) network trained on patient-therapist interaction data to predict therapist-applied joint torques from patient kinematics. Trained and validated using leave-one-out cross-validation across eight post-stroke patients, the model was integrated into a ROS-based exoskeleton controller to generate real-time torque assistance based on predicted therapist responses. Offline results and preliminary testing indicate the potential of their use as an alternative approach to post-stroke exoskeleton therapy. The PTFF provides understanding of the therapist's actions while the ST frees the human therapist from the exoskeleton, allowing them to continuously monitor the patient's nuanced condition.
Abstract (translated)
中风后的康复通常需要患者重新获得正常的行走步态。然而，传统的治疗过程可能对理疗师来说既疲惫又体力消耗大，这可能会随着时间的推移减少治疗强度、时长和一致性。为此，我们提出了一种患者-理疗师力场（PTFF）可视化方法来展示理疗师对患者运动学特征的响应，并开发了一个合成理疗师（ST）机器学习模型以支持机器人介导物理互动疗法中的人机协作。前者通过变分自动编码器（VAE）将患者的步态和理疗师的动作映射到共享低维潜在流形，然后使用高斯混合模型（GMM）建模患者与理疗师之间的交互过程，并学习一种概率向量场以从患者的状态预测理疗师的反应。这种表示法可以可视化患者-理疗师互动的动力学情况，从而帮助制定治疗策略和机器人控制器的设计。后者则采用长短期记忆网络（LSTM），利用患者-理疗师互动数据来根据患者的运动学特征预测理疗师所施加的关节扭矩。 该模型通过八名中风后病人的留一交叉验证法进行训练和验证，并被集成到基于ROS的外骨骼控制器中，以根据预测出的理疗师反应生成实时力矩辅助。离线结果和初步测试表明了这种方法在替代中风后外骨骼疗法中的潜力。PTFF能够揭示理疗师的行为模式，而ST模型则使人类理疗师从外骨骼设备解脱出来，使其可以持续监测患者的细微状况变化。
URL
https://arxiv.org/abs/2603.02458
