Investigation of Exo-neuro-musculo-skeleton with Neural-network based Evaluation for Ankle-foot Rehabilitation after Stroke

Stroke is one of the primary causes of adult hemiplegia globally. Conventional motor recovery of the hemiparetic limb necessitates repeated and intensive training for stroke survivors. However, the current rehabilitation service for motor restoration after discharge from the hospital is insufficient, particularly for the ambulation ability. Although 60%–80% of stroke survivors can walk independently, most of them exhibit long-term gait disturbances, including high gait asymmetry, lower walking speed, inability to walk far, and being more likely to fall, which affect their mobility and integration into the community. Thus, more effective, and readily accessible rehabilitation services or methods are required to enhance the ambulation ability of chronic stroke survivors to improve their life quality. On the other hand, the evaluation of the training effects during neurorehabilitation is also a crucial issue, which is commonly conducted by a blinded assessor (e.g., professional physiotherapists). Clinical assessment is hard to obtain owing to the shorthanded situation in the current healthcare system, e.g., professional therapists. The surface electromyography (sEMG) signals driven quantitative and objective evaluations have been used to track the training effects, e.g., the co-contraction index (CI) of muscle pairs and activation level of individual muscle. However, these quantitative metrics are not available online and cannot be robustly correlated to clinical scores. The objectives of this study were: (1) development of a data-driven model involving sEMG for facilitating an objective and automated metric of training effects for poststroke rehabilitation assisted by robots, (2) development of an exo-neuro-musculo-skeleton ankle-foot system with balance sensing feedback (ENMS-BF) for motor recovery of the paralyzed lower extremity after stroke, and (3) investigation of the assistive capability and rehabilitation effects of the proposed ENMS-BF on chronic stroke survivors, with both face-to-face individual training and remote self-help paired training. This study was implemented in three sections as follows:

In the first part, we constructed a backpropagation neural network (BPNN) model with the sEMG signals as the driven data, which matched the mapping relationship between the sEMG characteristics and commonly utilized clinical scales, i.e., the Modified Ashworth Scale (MAS) and the Fugl–Meyer Assessment (FMA). Twenty-nine individuals with chronic stroke completed a robot-assisted upper limb rehabilitation program, with the sEMG signals collected before and after the 20-session intervention. There were significant correlations (P<0.001) between the manually assessed and mapped FMA and MAS scores, within the labelled data captured before and after the intervention. The results showed that the proposed sEMG-driven model based on BPNN enables the automated tracking of motor recovery for chronic stroke survivors and demonstrated the potential to be applied in automated assessment post-stroke.

In the second section, we developed a novel ENMS-BF driven by plantar pressures to assist gait training by dynamic correction of foot drop and foot inversion. The ENMS-BF can be worn unilaterally onto the paretic lower limb with a weight of 0.47 kg. It consists of a soft-and-rigid musculoskeletal combination, i.e., musculoskeleton, two-channel neuromuscular electrical stimulation (NMES), and a tactile vibrator. The properties of pressure-to-torque transmission of the musculoskeleton were measured quantitatively. The results showed that the ENMS-BF could effectively correct foot drop and foot inversion in the hemiparetic gait pattern.

In the third section, the feasibility and rehabilitative effects of the ENMS-BF-assisted gait training after stroke were evaluated. Twelve stroke survivors participated in the individual gait training with close supervision. Then, another 12 individuals with chronic stroke were recruited in self-help paired training based on a cyber physical social system (CPSS) for remote social links. The results indicated that the ENMS-BF assisted gait training was feasible and effective in improving the motor function, gait pattern, and plantar pressure of the paralyzed lower limb in both groups. The developed ENMS-BF combining with CPSS could effectively facilitate self-help gait training with remote management and peer support.

In conclusion, the developed sEMG-driven model based on BPNN could facilitate the automated assessment of motor function recovery post-stroke. The developed ENMS-BF could assist in ankle dorsiflexion and self-correction of foot inversion during gait training. The ENMS-BF-assisted individual gait training was effective for improvements of lower limb motor function, gait pattern, and plantar balance in the paralyzed limb post-stroke. Based on the CPSS, the ENMS-BF-assisted paired training could support and facilitate self-help rehabilitation with professional management and social links with peers remotely.

中风是全球成年人偏瘫的主要原因之一。偏瘫肢体的传统康复需要对中风幸存者进行反复和密集的训练。然而，目前的出院后运动恢复康复服务不足，特别是在行走能力方面。尽管60%至80%的中风幸存者可以独立行走，但他们中的大多数长期存在步态障碍，包括高度步态不对称、较低的步行速度、不能远距离行走和更易跌倒等，这影响了他们的流动性和融入社区。因此，需要更有效、易于获得的康复服务或方法，以增强慢性中风幸存者的步行能力，提高他们的生活质量。另一方面，神经康复训练期间的评估也是一个关键问题，通常由盲评人（如专业物理治疗师）进行。由于目前医疗保健系统的人手不足，例如专业治疗师，因此很难进行临床评估。表面肌电图（sEMG）信号驱动的定量和客观评估已被用于跟踪训练效果，例如肌肉对的共同收缩指数（CI）和单个肌肉的激活水平。然而，这些定量指标不可在线获取，并且不能与临床评分稳健地相关。本研究的目标是：（1）开发一种基于sEMG的数据驱动模型，以促进机器人协助下的中风后康复训练效果的客观和自动化评估指标；（2）开发一种具有平衡感知反馈的外骨骼-神经-肌肉-骨骼脚踝系统（ENMS-BF），用于中风后瘫痪下肢的运动恢复；（3）研究所提出的ENMS-BF对慢性中风幸存者的辅助能力和康复效果，包括面对面的个体训练和远程自助配对训练。本研究分为三个部分：

在第一部分中，我们构建了一个反向传播神经网络（BPNN）模型，以sEMG信号为驱动数据，匹配了sEMG特征和常用的临床评分量表，即修改后的Ashworth量表（MAS）和Fugl-Meyer评估（FMA）之间的映射关系。29名慢性中风患者完成了机器人辅助上肢康复计划，在20次干预前后收集了sEMG信号。手动评定和映射FMA和MAS评分与标记数据之间存在显著相关性（P＜0.001）。结果表明，所提出的基于BPNN的sEMG驱动模型能够自动跟踪慢性中风幸存者的运动恢复，并具有在中风后进行自动化评估的潜力。

在第二部分中，我们开发了一种新型的ENMS-BF，通过动态矫正足下垂和足内翻来协助步态训练，其由足底压力驱动。ENMS-BF可以单侧佩戴在瘫痪的下肢上，重量为0.47公斤。它由软硬肌骨组合，即肌肉骨骼系统、双通道神经肌肉电刺激（NMES）和触觉振动器组成。我们定量测量了肌骨系统的压力转矩传递特性。结果表明，ENMS-BF可以有效地矫正偏瘫步态模式中的足下垂和足内翻。

在第三部分中，我们评估了ENMS-BF辅助步态训练的可行性和康复效果。12名中风幸存者参加了密切监督的个体步态训练。然后，另外12名慢性中风患者被招募进行基于网络物理社交系统（CPSS）的远程自助配对训练。结果表明，ENMS-BF辅助步态训练在两组中都可以改善下肢运动功能、步态模式和足底平衡。所开发的结合CPSS的ENMS-BF有效地促进了远程管理和同伴鼓励下的自助步态训练。

总之，所开发的基于sEMG的BPNN驱动模型可以促进中风后运动功能恢复的自动化评估。所开发的ENMS-BF可以协助踝部背屈，并在步态训练中进行自我矫正足内翻。ENMS-BF辅助的个体步态训练对于改善中风后瘫痪肢体下肢的运动功能、步态模式和足底平衡是有效的。基于CPSS，ENMS-BF辅助的配对训练可以支持和促进具有远程管理和同伴鼓励的自助康复。

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