Ranzani Raffaele, Chiriatti Giorgia, Schwarz Anne, Devittori Giada, Gassert Roger, Lambercy Olivier
Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
Department of Industrial Engineering and Mathematical Science, Polytechnic University of Marche, Ancona, Italy.
Front Robot AI. 2023 Feb 6;10:1093124. doi: 10.3389/frobt.2023.1093124. eCollection 2023.
Robot-assisted neurorehabilitation is becoming an established method to complement conventional therapy after stroke and provide intensive therapy regimes in unsupervised settings (e.g., home rehabilitation). Intensive therapies may temporarily contribute to increasing muscle tone and spasticity, especially in stroke patients presenting tone alterations. If sustained without supervision, such an increase in muscle tone could have negative effects (e.g., functional disability, pain). We propose an online perturbation-based method that monitors finger muscle tone during unsupervised robot-assisted hand therapy exercises. We used the ReHandyBot, a novel 2 degrees of freedom (DOF) haptic device to perform robot-assisted therapy exercises training hand grasping (i.e., flexion-extension of the fingers) and forearm pronosupination. The tone estimation method consisted of fast (150 ms) and slow (250 ms) 20 mm ramp-and-hold perturbations on the grasping DOF, which were applied during the exercises to stretch the finger flexors. The perturbation-induced peak force at the finger pads was used to compute tone. In this work, we evaluated the method performance in a stiffness identification experiment with springs (0.97 and 1.57 N/mm), which simulated the stiffness of a human hand, and in a pilot study with subjects with increased muscle tone after stroke and unimpaired, which performed one active sensorimotor exercise embedding the tone monitoring method. The method accurately estimates forces with root mean square percentage errors of 3.8% and 11.3% for the soft and stiff spring, respectively. In the pilot study, six chronic ischemic stroke patients [141.8 (56.7) months after stroke, 64.3 (9.5) years old, expressed as mean (std)] and ten unimpaired subjects [59.9 (6.1) years old] were tested without adverse events. The average reaction force at the level of the fingertip during slow and fast perturbations in the exercise were respectively 10.7 (5.6) N and 13.7 (5.6) N for the patients and 5.8 (4.2) N and 6.8 (5.1) N for the unimpaired subjects. The proposed method estimates reaction forces of physical springs accurately, and captures online increased reaction forces in persons with stroke compared to unimpaired subjects within unsupervised human-robot interactions. In the future, the identified range of muscle tone increase after stroke could be used to customize therapy for each subject and maintain safety during intensive robot-assisted rehabilitation.
机器人辅助神经康复正成为一种既定方法，用于补充中风后的传统治疗，并在无人监督的环境（如家庭康复）中提供强化治疗方案。强化治疗可能会暂时导致肌张力增加和痉挛，特别是在出现肌张力改变的中风患者中。如果在无人监督的情况下持续进行，这种肌张力的增加可能会产生负面影响（如功能障碍、疼痛）。我们提出了一种基于在线扰动的方法，该方法可在无人监督的机器人辅助手部治疗练习过程中监测手指肌张力。我们使用了ReHandyBot，这是一种新型的2自由度触觉设备，用于进行机器人辅助治疗练习，训练手部抓握（即手指屈伸）和前臂旋前旋后。肌张力估计方法包括在抓握自由度上进行快速（150毫秒）和慢速（250毫秒）的20毫米斜坡保持扰动，这些扰动在练习过程中用于拉伸手指屈肌。手指垫处的扰动诱发峰值力用于计算肌张力。在这项工作中，我们在一个使用弹簧（0.97和1.57牛/毫米）模拟人手刚度的刚度识别实验中，以及在一项针对中风后肌张力增加且未受损的受试者的初步研究中评估了该方法的性能，该初步研究进行了一项嵌入肌张力监测方法的主动感觉运动练习。该方法分别以3.8%和11.3%的均方根百分比误差准确估计了软弹簧和硬弹簧的力。在初步研究中，对6名慢性缺血性中风患者[中风后141.8（56.7）个月，64.3（9.5）岁，以平均值（标准差）表示]和10名未受损受试者[59.9（6.1）岁]进行了测试，未发生不良事件。练习过程中慢速和快速扰动时指尖水平的平均反作用力，患者分别为10.7（5.6）牛和13.7（5.6）牛，未受损受试者分别为5.8（4.2）牛和6.8（5.1）牛。所提出的方法能够准确估计物理弹簧的反作用力，并在无人监督的人机交互中捕捉中风患者与未受损受试者相比在线增加的反作用力。未来，中风后确定的肌张力增加范围可用于为每个受试者定制治疗方案，并在强化机器人辅助康复过程中确保安全。
