I highly doubt this will get you 100% recovered in the limited time you will get to use it. So, DEMAND YOUR COMPETENT? DOCTOR HAVE EXACT PROTOCOLS AFTER THAT TO GET 100% RECOVERED! If your doctor can't do that; you don't have a functioning stroke doctor! RUN AWAY!
And if your doctor hasn't already used these earlier ones, why the hell not?
On the role of visual feedback and physiotherapist-patient interaction in robot-assisted gait training: an eye-tracking and HD-EEG study
Journal of NeuroEngineering and Rehabilitation volume 21, Article number: 211 (2024)
Abstract
Background
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.
Methods
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.
Results
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.
Conclusions
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.
Background
Over the last decade, robot-based rehabilitation has been increasingly accepted in clinical applications as an adjunct conventional therapy, assisting the physiotherapist in increasing the intensity and repeatability of rehabilitation sessions [1,2,3,4]. Commercially available robotic-assisted gait training (RAGT) devices are generally divided into stationary systems and overground systems [5]. The latter are robotic devices that allow patients to practise walking on a hard surface, whereas the former take advantages from the body weight support (BWS) [1] and can be divided into treadmill-based gait trainers with exoskeleton (t-RAGT) and end-effector gait trainers. The t-RAGT is a device in which the movement of the leg is generated by the exoskeleton worn by the patient. The end-effector gait trainer is a device with two independently moving footplates to which the patient’s feet are attached [5]. The movement of the plates induces the stance and swing phases of the patient’s gait. Of these two devices the t-RAGT is one the most widely used in rehabilitation centres [6] and there is evidence of its effectiveness [7] when used in conjunction with the conventional gait rehabilitation for central nervous system conditions [2, 8, 9] such as the Spinal Cord Injury (SCI) [2, 10].
t-RAGT offers several advantages such as task-oriented movements, precisely controllable assistance, the ability to modulate the weight-bearing effect on the lower limbs and objective and quantifiable measures of patient’s performance based on kinematic, kinetic and spatio-temporal data [11, 12]. t-RAGT provides automated locomotor training using a BWS system and actuators located on limbs that provide adjustable Guidance Assistance (GA) to assist the patient’s lower limb joints in performing a physiological gait pattern while reducing the physical effort of the physiotherapist [13]. In addition, a reproducible, rhythmic and physiological limb movement provides the optimal afferent input which is necessary to stimulate the spinal neural circuits to activate the lower limb muscles that cannot be moved voluntarily [14].
The addition of a robot as a third party to the one-to-one Physiotherapist-Patient (Pht-Pt) relationship has modified the classic dynamics between the therapist and the patient, moving from a dyadic to a triadic interaction paradigm. In addition, the lack of direct physical contact during rehabilitation poses a challenge for the therapists in providing feedback to patients on their performance [15], potentially compromising their involvement during therapy. However, this gap has typically been bridged by providing both the patient and the physiotherapist with visual feedback (FB) during therapy, informing them of the characteristics of the patient's motor pattern. The therapist is then able to use the FB information to provide the patient with enriched instructions, suggestions and corrections to reinforce what the patient is already receiving through the FB itself. This approach contributes to improve the effectiveness of the patients’ motor training by promoting motor learning strategies, improving the human–robot interaction and increasing motivation and engagement [16, 17]. In fact, FB (e.g., visual, auditory, haptic) is widely recognised as a key component for robotic neurorehabilitation, due to its ability to enhance a plasticity-dependent mechanism that improves motor learning and performance, especially in the context of t-RAGT [18]. FB also promotes active patient’s participation, which is indeed crucial for successful rehabilitation outcomes and needs to be facilitated and tailored to patient-specific requirements [19, 20]. Although the importance of FB in t-RAGT is well recognised, it is still unclear what the effective role of the therapist should be in the complex patient-robot interaction. In fact, the patient can be left to decode the information contained in the FB without any verbal support from the therapist, or the therapist can provide continuous real-time encouragement for the correct interpretation of the FB through a positive social relationship characterised by mutual respect, therapist’s empathy and patient’s trust [21].
To date, patient’s experience and participation in the rehabilitation process has been assessed using questionnaires that examine the perceived quality of care and services received [19], or exploring aspects of motivation, agency and satisfaction [22,23,24]. Patients report that training with the visual FB increases their motivation [25] and they express a desire to incorporate the FB into their training programme as they perceive that it accurately reflects their activity [23,24,25]. To improve the understanding of the dynamics that characterise patients’ engagement in terms of visual attention and cognitive processing during t-RAGT we propose to introduce objective measurements alongside the subjective information derived from questionnaires. Indeed, objective measurements can provide quantifiable data that improve reproducibility and reduce the bias and subjectivity inherent in questionnaire-based assessment. These measurements could be derived from bio-signals such as Eye-Tracking (ET) and Electroencephalography (EEG). Indeed, ET has proven to be a valuable tool for studying human behaviour by measuring and monitoring eye movements, which determine the stimuli on which the gaze falls [26]. ET metrics, derived from fixational and saccadic eye movements, are used to define observer engagement and determine how visual attention is distributed across the visual field. This is based on the eye-mind hypothesis, according to which the fixation of the eyes corresponds to the engagement of the mind [27]. The EEG records the electrical activity generated by neurons from electrodes placed on the scalp [28]. Advanced analysis of EEG signals has contributed to a better understanding of various cognitive processes (e.g. visual perception, attention, emotion), providing real-time data on brain function with excellent temporal resolution. The neural mechanisms underlying empathic interaction and mentalising have been investigated using EEG [29,30,31,32,33], demonstrating the relevance of specific brain structures such as the anterior cingulate cortex (ACC), superior frontal regions, parietal and somatosensory regions, the temporal-parietal junction and the posterior cingulate. EEG has also been used to assess social behaviour during key interactions in clinical settings [34] such as eye-gaze [35], cooperative decision making [31], verbal and non-verbal communication [36].
In this context, we conducted a feasibility study to investigate and quantify, using multimodal EEG and ET recordings, how the combination of different types of visual FB and different levels of Pht-Pt interaction would affect the patients’ engagement during t-RAGT.
Specifically, we hypothesise that a combination of the type of visual FB and of the way in which the therapist supports the patient during a t-RAGT rehabilitation session:
- i.
would influence the patient’s visual attention by differently directing the patient’s gaze towards the monitor where the FB is provided, the therapist silhouette, or other potential sources of distraction (environment);
- ii.
would elicit cognitive processes involving brain areas associated with social interaction when the physiotherapist’s support is more relevant.
To this end, ten individuals with SCI performed seven t-RAGT sessions using the Lokomat Pro (Hocoma AG, Switzerland). During these sessions, two experimental conditions were implemented and randomly combined: (i) three types of visual FBs available on the Lokomat and (ii) three levels of Pht-Pt interaction. ET and EEG data were acquired simultaneously during the sessions and used to assess the patients’ attentional allocation and engagement during the rehabilitation intervention. By monitoring eye movements and brain activity, we were able to determine where the patient’s attention was focused, and which brain regions were active during the different experimental manipulations.
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