Patarini Francesca, Tamburella Federica, Pichiorri Floriana, Mohebban Shiva, Bigioni Alessandra, Ranieri Andrea, Di Tommaso Francesco, Tagliamonte Nevio Luigi, Serratore Giada, Lorusso Matteo, Ciaramidaro Angela, Cincotti Febo, Scivoletto Giorgio, Mattia Donatella, Toppi Jlenia
Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto, 25, 00185, Rome, Italy.
Neuroelectrical Imaging and Brain Computer Interface Lab, IRCCS Fondazione Santa Lucia, Rome, Italy.
J Neuroeng Rehabil. 2024 Dec 3;21(1):211. doi: 10.1186/s12984-024-01504-9.
Treadmill based Robotic-Assisted Gait Training (t-RAGT) provides for automated locomotor training to help the patient achieve a physiological gait pattern, reducing the physical effort required by therapist. By introducing the robot as a third agent to the traditional one-to-one physiotherapist-patient (Pht-Pt) relationship, the therapist might not be fully aware of the patient's motor performance. This gap has been bridged by the integration in rehabilitation robots of a visual FeedBack (FB) that informs about patient's performance. Despite the recognized importance of FB in t-RAGT, the optimal role of the therapist in the complex patient-robot interaction is still unclear. This study aimed to describe whether the type of FB combined with different modalities of Pht's interaction toward Pt would affect the patients' visual attention and emotional engagement during t-RAGT.
Ten individuals with incomplete Spinal Cord Injury (C or D ASIA Impairment Scale level) were assessed using eye-tracking (ET) and high-density EEG during seven t-RAGT sessions with Lokomat where (i) three types of visual FB (chart, emoticon and game) and (ii) three levels of Pht-Pt interaction (low, medium and high) were randomly combined. ET metrics (fixations and saccades) were extracted for each of the three defined areas of interest (AoI) (monitor, Pht and surrounding) and compared among the different experimental conditions (FB, Pht-Pt interaction level). The EEG spectral activations in theta and alpha bands were reconstructed for each FB type applying Welch periodogram to data localised in the whole grey matter volume using sLORETA.
We found an effect of FB type factor on all the ET metrics computed in the three AoIs while the factor Pht-Pt interaction level also combined with FB type showed an effect only on the ET metrics calculated in Pht and surrounding AoIs. Neural activation in brain regions crucial for social cognition resulted for high Pht-Pt interaction level, while activation of the insula was found during low interaction, independently on the FB used.
The type of FB and the way in which Pht supports the patients both have a strong impact on patients' engagement and should be considered in the design of a t-RAGT-based rehabilitation session.
基于跑步机的机器人辅助步态训练（t-RAGT）提供自动运动训练，以帮助患者实现生理步态模式，减少治疗师所需的体力劳动。通过将机器人引入传统的一对一物理治疗师-患者（Pht-Pt）关系中作为第三方，治疗师可能无法完全了解患者的运动表现。康复机器人中集成的视觉反馈（FB）弥补了这一差距，该反馈可告知患者的表现。尽管FB在t-RAGT中的重要性已得到认可，但在复杂的患者-机器人交互中治疗师的最佳角色仍不清楚。本研究旨在描述与Pht对Pt的不同交互方式相结合的FB类型是否会影响t-RAGT期间患者的视觉注意力和情感投入。
在使用Lokomat进行的七次t-RAGT训练中，对十名不完全脊髓损伤（C或D级亚洲损伤量表水平）的个体进行眼动追踪（ET）和高密度脑电图评估，其中（i）三种类型的视觉FB（图表、表情符号和游戏）和（ii）三种Pht-Pt交互水平（低、中、高）被随机组合。针对三个定义的感兴趣区域（AoI）（显示器、Pht和周围环境）中的每一个提取ET指标（注视和扫视），并在不同的实验条件（FB、Pht-Pt交互水平）之间进行比较。使用sLORETA将Welch周期图应用于全灰质体积中的数据，为每种FB类型重建theta和alpha波段的脑电图频谱激活。
我们发现FB类型因素对在三个AoI中计算的所有ET指标都有影响，而Pht-Pt交互水平因素与FB类型相结合时，仅对在Pht和周围AoI中计算的ET指标有影响。对于高Pht-Pt交互水平，在对社会认知至关重要的脑区中出现神经激活，而在低交互期间发现岛叶激活，与所使用的FB无关。
FB的类型以及Pht支持患者的方式都对患者的参与度有很大影响，在基于t-RAGT的康复训练设计中应予以考虑。
