The future role of robots in neuro-rehabilitation

"What should a rehabilitation hospital be like?" Priorities and expectations of people with spinal cord injury in Türkiye

STUDY DESIGN

Survey study OBJECTIVES: To understand the priorities and expectations of individuals with disabilities caused by spinal cord injuries(SCI) who require long-term inpatient rehabilitation at a rehabilitation hospital.

SETTING

Başakşehir Çam and Sakura City Hospital, İstanbul, Türkiye METHODS: This cross-sectional clinical study included individuals over the age of 18 with SCI who had previously been hospitalized in a rehabilitation hospital. The 18-question survey, titled "What should a rehabilitation hospital be like according to persons with spinal cord injuries?" was administered to individuals hospitalized in the inpatient service of Çam Sakura City Hospital. It was also disseminated to people with SCI through social media. The participants' demographic data was recorded.

RESULTS

The survey was completed by 120 participants, comprising 70 males and 50 females. The mean age was 37.47 ± 11.63 years. The time since the SCI was less than one year for 20 individuals and more than one year for 100 individuals. The results showed that robotic rehabilitation and psychological support were the most requested rehabilitation domains, while interest in sexual rehabilitation was less than that in other rehabilitation domains. Furthermore, in the correlation analysis, elderly participants indicated that there should be more specialized services and outpatient clinics exclusive to the SCI.

CONCLUSIONS

The study revealed a striking trend - participants expressed a strong desire for SCI-specific rehabilitation units and robotic rehabilitation. Additionally, the significance and necessity of sexual rehabilitation should be conveyed to people with SCI.

Avoidance of specific calibration sessions in motor intention recognition for exoskeleton-supported rehabilitation through transfer learning on EEG data

Exoskeleton-based support for patients requires the learning of individual machine-learning models to recognize movement intentions of patients based on the electroencephalogram (EEG). A major issue in EEG-based movement intention recognition is the long calibration time required to train a model. In this paper, we propose a transfer learning approach that eliminates the need for a calibration session. This approach is validated on healthy subjects in this study. We will use the proposed approach in our future rehabilitation application, where the movement intention of the affected arm of a patient can be inferred from the EEG data recorded during bilateral arm movements enabled by the exoskeleton mirroring arm movements from the unaffected to the affected arm. For the initial evaluation, we compared two trained models for predicting unilateral and bilateral movement intentions without applying a classifier transfer. For the main evaluation, we predicted unilateral movement intentions without a calibration session by transferring the classifier trained on data from bilateral movement intentions. Our results showed that the classification performance for the transfer case was comparable to that in the non-transfer case, even with only 4 or 8 EEG channels. Our results contribute to robotic rehabilitation by eliminating the need for a calibration session, since EEG data for training is recorded during the rehabilitation session, and only a small number of EEG channels are required for model training.

Neurocritical Care for Patients With Ischemic Stroke

OBJECTIVE

Management of stroke due to large vessel occlusion (LVO) has undergone unprecedented change in the past decade. Effective treatment with thrombectomy has galvanized the field and led to advancements in all aspects of care. This article provides a comprehensive examination of neurologic intensive care unit (ICU) management of patients with stroke due to LVO. The role of the neurocritical care team in stroke systems of care and the importance of prompt diagnosis, initiation of treatment, and continued monitoring of patients with stroke due to LVO is highlighted.

LATEST DEVELOPMENTS

The management of complications commonly associated with stroke due to LVO, including malignant cerebral edema and respiratory failure, are addressed, stressing the importance of early identification and aggressive treatment in mitigating negative effects on patients' prognoses. In the realm of medical management, this article discusses various medical therapies, including antithrombotic therapy, blood pressure management, and glucose control, outlining evidence-based strategies for optimizing patient outcomes. It further emphasizes the importance of a multidisciplinary approach to provide a comprehensive care model. Lastly, the critical aspect of family communication and prognostication in the neurologic ICU is addressed.

ESSENTIAL POINTS

This article emphasizes the multidimensional aspects of neurocritical care in treating patients with stroke due to LVO.

Proposal of neural network model for neurocognitive rehabilitation and its comparison with fuzzy expert system model

This article focuses on the development of algorithms for a smart neurorehabilitation system, whose core is made up of artificial neural networks. The authors of the article have proposed a completely unique transfer of ACE-R results to the CHC model. This unique approach allows for the saturation of the CHC model domains according to modified ACE-R factor analysis. The outputs of the proposed algorithm thus enable the automatic creation of a personalized and optimized neurorehabilitation plan for individual patients to train their cognitive functions. A set of tasks in 6 levels of difficulty (level 1 to level 6) was designed for each of the nine CHC model domains. For each patient, the results of the ACE-R screening helped deter-mine the specific CHC domains to be rehabilitated, as well as the initial gaming level for rehabilitation in each domain. The proposed artificial neural network algorithm was adapted to real data from 703 patients. Experimental outputs were compared to the outputs of the initially designed fuzzy expert system, which was trained on the same real data, and all outputs from both systems were statistically evaluated against expert conclusions that were available. It is evident from the conducted experimental study that the smart neurorehabilitation system using artificial neural networks achieved significantly better results than the neurorehabilitation system whose core is a fuzzy expert system. Both algorithms are implemented into a comprehensive neurorehabilitation portal (Eddie), which was supported by a research project from the Technology Agency of the Czech Republic.

Effect of transcranial direct current stimulation in combination with robotic therapy in upper limb impairments in people with stroke: a systematic review

Background

Stroke is a devastating condition, which not only affects patients’ activity, but also is a primary reason for the psychosocial impact on them, their caregivers, and the healthcare system. Transcranial direct current stimulation (tDCS) modulates cortical activity, encouraging neuro-modulation and motor recovery in stroke rehabilitation. Robotic therapy (RT) provides repetitive, high-intensity, interactive, task-specific intervention and can measure changes while providing feedback to people with stroke.

Objectives

This study aimed to evaluate and summarize the scientific literature systematically to investigate the combined effect of tDCS and RT in patients with stroke.

Methods

Four databases (MEDLINE, Web of Science, ScienceDirect, & PEDro) were searched for clinical trials investigating the effect of RT and tDCS in stroke patients with upper limb impairment. PEDro scale was used for the quality assessment of included studies.

Results

The search yielded 208 articles. A total of 213 patients with stroke who had upper limb impairment were studied. In the majority of the trials, RT combined with tDCS lead to positive improvement in various measures of upper limb function and spasticity.

Conclusions

RT along with tDCS is an effective mode of rehabilitation, although no additional effects of tDCS plus RT in comparison with RT alone were reported. Large, robust studies are needed, so that health care providers and researchers can make better decisions about merging tDCS and RT in stroke rehabilitation settings in the future.

Robot-assisted therapy for upper limb paresis after stroke: Use of robotic algorithms in advanced practice

BACKGROUND

Rehabilitation of stroke-related upper limb paresis is a major public health issue.

OBJECTIVE

Robotic systems have been developed to facilitate neurorehabilitation by providing key elements required to stimulate brain plasticity and motor recovery, namely repetitive, intensive, adaptative training with feedback. Although the positive effect of robot-assisted therapy on motor impairments has been well demonstrated, the effect on functional capacity is less certain.

METHOD

This narrative review outlines the principles of robot-assisted therapy for the rehabilitation of post-stroke upper limb paresis.

RESULTS

A paradigm is proposed to promote not only recovery of impairment but also function.

CONCLUSION

Further studies that would integrate some principles of the paradigm described in this paper are needed.

A Technology-Based Intervention to Support Older Adults in Living Independently: Protocol for a Cross-National Feasibility Pilot

Innovative technologies can support older adults with or without disabilities, allowing them to live independently in their environment whilst monitoring their health and safety conditions and thereby reducing the significant burden on caregivers, whether family or professional. This paper discusses the design of a study protocol to evaluate the acceptance, usability, and efficiency of the SAVE system, a custom-developed information technology-based elderly care system. The study will involve older adults (aged 65 or older), professional and lay caregivers, and care service decision-makers representing all types of users in a care service scenario. The SAVE environmental sensors, smartwatches, smartphones, and Web service application will be evaluated in people's homes situated in Romania, Italy, and Hungary with a total of 165 users of the three types (cares, elderly, and admin). The study design follows the mixed method approach, using standardized tests and questionnaires with open-ended questions and logging all the data for evaluation. The trial is registered to the platform ClinicalTrials.gov with the registration number NCT05626556. This protocol not only guides the participating countries but can be a feasibility protocol suitable for evaluating the usability and quality of similar systems.

Is robot-assisted gait training intensity a determinant of functional recovery early after stroke? A pragmatic observational study of clinical care

Gait rehabilitation is a critical factor in functional recovery after a stroke. The aim of this pragmatic observational study was to identify the optimal dose and timing of robot-assisted gait training (RAGT) that can lead to a favourable outcome in a sample of subacute stroke survivors. Subacute patients with stroke who underwent a RAGT within a multidisciplinary rehabilitation program were enrolled. A set of clinical (i.e. age, type of stroke and time since stroke) and rehabilitation stay outcomes (length of stay and RAGT number of sessions) were recorded to evaluate their impact on functional outcome measures by functional independence measure (FIM) or functional ambulation category (FAC). We included 236 patients (62.73 ± 11.82 year old); 38.44% were females, and 59.32% were ischaemic stroke patients. Patients that received at least 14 RAGT sessions, had 15.83% more chance to be responders compared to those that receive less sessions (P = 0.006). Similarly, younger patients (≤60 years) were more prone to be responders (+15.1%). Lastly, an early rehabilitation (<6 weeks) was found to be more efficient (+21.09%) in determining responsiveness (P < 0.001). Becoming newly independent for gait, that refers to a FAC score ≥4, was related with age and RAGT sessions (P = 0.001). In conclusion, a younger age (≤60 years), an early rehabilitation (<6 weeks since stroke) and a higher RAGT dose (at least 14 sessions) were related to a favourable outcome in patients with subacute stroke.

Emerging Roles for Social Robots in Rehabilitation

Insights from social and cognitive neuroscience should inform the design of socially assistive robots for neurorehabilitation as novel roles emerge for them in human-human interactions.

Citation Network Study on the Use of New Technologies in Neurorehabilitation

New technologies in neurorehabilitation is a wide concept that intends to find solutions for individual and collective needs through technical systems. Analysis through citation networks is used to search scientific literature related to a specific topic. On the one hand, the main countries, institutions, and authors researching this topic have been identified, as well as their evolution over time. On the other hand, the links between the authors, the countries, and the topics under research have been analyzed. The publications analysis was performed through the Web of Science database using the search terms "new technolog*," "neurorehabilitation," "physical therapy*," and "occupational therapy*." The selected interval of publication was from 1992 to December 2020. The results were analyzed using CitNetExplorer software. After a Web of Science search, a total of 454 publications and 135 citation networks were found, 1992 being the first year of publication. An exponential increase was detected from the year 2009. The largest number was detected in 2020. The main areas are rehabilitation and neurosciences and neurology. The most cited article was from Perry et al. in 2007, with a citation index of 460. The analysis of the top 20 most cited articles shows that most approach the use of robotic devices and brain-computer interface systems. In conclusion, the main theme was found to be the use of robotic devices to address neuromuscular rehabilitation goals and brain-computer interfaces and their applications in neurorehabilitation.

Peak Activation Shifts in the Sensorimotor Cortex of Chronic Stroke Patients Following Robot-assisted Rehabilitation Therapy

Background:

Ischemic stroke is the most common cause of complex chronic disability and the third leading cause of death worldwide. In recovering stroke patients, peak activation within the ipsilesional primary motor cortex (M1) during the performance of a simple motor task has been shown to exhibit an anterior shift in many studies and a posterior shift in other studies.

Objective:

We investigated this discrepancy in chronic stroke patients who completed a robot-assisted rehabilitation therapy program.

Methods:

Eight chronic stroke patients with an intact M1 and 13 Healthy Control (HC) volunteers underwent 300 functional magnetic resonance imaging (fMRI) scans while performing a grip task at different force levels with a robotic device. The patients were trained with the same robotic device over a 10-week intervention period and their progress was evaluated serially with the Fugl-Meyer and Modified Ashworth scales. Repeated measure analyses were used to assess group differences in locations of peak activity in the sensorimotor cortex (SM) and the relationship of such changes with scores on the Fugl-Meyer Upper Extremity (FM UE) scale.

Results:

Patients moving their stroke-affected hand had proportionally more peak activations in the primary motor area and fewer peak activations in the somatosensory cortex than the healthy controls (P=0.009). They also showed an anterior shift of peak activity on average of 5.3-mm (P<0.001). The shift correlated negatively with FM UE scores (P=0.002).

Conclusion:

A stroke rehabilitation grip task with a robotic device was confirmed to be feasible during fMRI scanning and thus amenable to be used to assess plastic changes in neurological motor activity. Location of peak activity in the SM is a promising clinical neuroimaging index for the evaluation and monitoring of chronic stroke patients.

Decoding of Ankle Joint Movements in Stroke Patients Using Surface Electromyography

Stroke is a cerebrovascular disease (CVD), which results in hemiplegia, paralysis, or death. Conventionally, a stroke patient requires prolonged sessions with physical therapists for the recovery of motor function. Various home-based rehabilitative devices are also available for upper limbs and require minimal or no assistance from a physiotherapist. However, there is no clinically proven device available for functional recovery of a lower limb. In this study, we explored the potential use of surface electromyography (sEMG) as a controlling mechanism for the development of a home-based lower limb rehabilitative device for stroke patients. In this experiment, three channels of sEMG were used to record data from 11 stroke patients while performing ankle joint movements. The movements were then decoded from the sEMG data and their correlation with the level of motor impairment was investigated. The impairment level was quantified using the Fugl-Meyer Assessment (FMA) scale. During the analysis, Hudgins time-domain features were extracted and classified using linear discriminant analysis (LDA) and artificial neural network (ANN). On average, 63.86% ± 4.3% and 67.1% ± 7.9% of the movements were accurately classified in an offline analysis by LDA and ANN, respectively. We found that in both classifiers, some motions outperformed others (p < 0.001 for LDA and p = 0.014 for ANN). The Spearman correlation (ρ) was calculated between the FMA scores and classification accuracies. The results indicate that there is a moderately positive correlation (ρ = 0.75 for LDA and ρ = 0.55 for ANN) between the two of them. The findings of this study suggest that a home-based EMG system can be developed to provide customized therapy for the improvement of functional lower limb motion in stroke patients.

Improving motor function after chronic stroke by interactive gaming with a redesigned MR-compatible hand training device

New rehabilitation strategies enabled by technological developments are challenging the prevailing concept of there being a limited window for functional recovery after stroke. In this study, we examined the utility of a robot-assisted therapy used in combination with a serious game as a rehabilitation and motor assessment tool in patients with chronic stroke. We evaluated 928 game rounds from 386 training sessions of 8 patients who had suffered an ischemic stroke affecting middle cerebral artery territory that incurred at least 6 months prior. Motor function was assessed with clinical motor scales, including the Fugl-Meyer upper extremity (FM UE) scale, Action Research Arm Test, Modified Ashworth scale and the Box and Blocks test. Robotic device output measures (mean force, force-position correlation) and serious game score elements (collisions, rewards and total score) were calculated. A total of 2 patients exhibited a marginal improvement after a 10-week training protocol according to the FM UE scale and an additional patient exhibited a significant improvement according to Box and Blocks test. Motor scales showed strong associations of robotic device parameters and game metrics with clinical motor scale scores, with the strongest correlations observed for the mean force (0.677<Ρ<0.869), followed by the number of collisions (-0.670<Ρ<-0.585). Linear regression analysis showed that these indices were independent predictors of motor scale scores. In conclusion, a robotic device linked to a serious game can be used by patients with chronic stroke and induce at least some clinical improvements in motor performance. Robotic device output parameters and game score elements associate strongly with clinical motor scales and have the potential to be used as predictors in models of rehabilitation progress.

Robot Assisted Ankle Neuro-Rehabilitation: State of the art and Future Challenges

Introduction: Robot-assisted neuro-rehabilitation is gaining acceptability among the physical therapy community. The ankle is one of the most complicated anatomical joints in the human body and neurologic injuries such as stroke often result in ankle and foot disabilities. Areas covered: Robotic solutions for the ankle joint physical therapy have extensively been researched. Significant research has been conducted on the mechanism design, actuation as well as control of these ankle rehabilitation robots. Also, the experimental evaluations of these robots have been conducted with healthy and neurologically impaired subjects. This paper presents a comprehensive review of the recent developments in the field of robot-assisted ankle rehabilitation. Mechanism design, actuation, and various types of control strategies are discussed. Also, the experimental evaluations of these ankle rehabilitation robots are discussed in the context of the evaluation of robotic hardware with healthy subjects as well as motor function outcomes with neurologically impaired subjects. Expert opinion: Significant progress in the mechanism design, control, and experimental evaluations of the ankle rehabilitation robots have been reported. However, more sensing and reference trajectory generation methods need to be developed as well as more objective quantitive evaluations that need to be conducted for establishing the clinical significance of these robots.

Dynamic Stability and Trunk Control Improvements Following Robotic Balance and Core Stability Training in Chronic Stroke Survivors: A Pilot Study

Stroke survivors show greater postural oscillations and altered muscular activation compared to healthy controls. This results in difficulties in walking and standing, and in an increased risk of falls. A proper control of the trunk is related to a stable walk and to a lower falling risk; to this extent, rehabilitative protocols are currently working on core stability. The main objective of this work was to evaluate the effectiveness of trunk and balance training performed with a new robotic device designed for evaluation and training of balance and core stability, in improving the recovery of chronic stroke patients compared with a traditional physical therapy program. Thirty chronic stroke patients, randomly divided in two groups, either underwent a traditional rehabilitative protocol, or a robot-based program. Each patient was assessed before and after the rehabilitation and at 3-months follow-up with clinical and robot-based evaluation exercises focused on static and dynamic balance and trunk control. Results from clinical scores showed an improvement in both groups in balance and trunk control. Robot-based indices analysis indicated that the experimental group showed greater improvements in proprioceptive control, reactive balance and postural control in unstable conditions, compared to the control group, showing an improved trunk control with reduced compensatory strategies at the end of the training. Moreover, the experimental group had an increased retention of the benefits obtained with training at 3 months follow up. These results support the idea that such robotic device is a promising tool for stroke rehabilitation.

Perspectives and Challenges in Robotic Neurorehabilitation

The development of robotic devices for rehabilitation is a fast-growing field. Nowadays, thanks to novel technologies that have improved robots’ capabilities and offered more cost-effective solutions, robotic devices are increasingly being employed during clinical practice, with the goal of boosting patients’ recovery. Robotic rehabilitation is also widely used in the context of neurological disorders, where it is often provided in a variety of different fashions, depending on the specific function to be restored. Indeed, the effect of robot-aided neurorehabilitation can be maximized when used in combination with a proper training regimen (based on motor control paradigms) or with non-invasive brain machine interfaces. Therapy-induced changes in neural activity and behavioral performance, which may suggest underlying changes in neural plasticity, can be quantified by multimodal assessments of both sensorimotor performance and brain/muscular activity pre/post or during intervention. Here, we provide an overview of the most common robotic devices for upper and lower limb rehabilitation and we describe the aforementioned neurorehabilitation scenarios. We also review assessment techniques for the evaluation of robotic therapy. Additional exploitation of these research areas will highlight the crucial contribution of rehabilitation robotics for promoting recovery and answering questions about reorganization of brain functions in response to disease.