The future role of robots in neuro-rehabilitation

Fazekas, Gábor; Tavaszi, Ibolya

doi:10.1080/14737175.2019.1617700

Cited by 25 publications

(15 citation statements)

References 16 publications

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“…The low-level implementation of these assistive training modalities is more challenging in exoskeletons than in end-effector devices. In particular, with the goal to exploit exoskeletons to also improve inter-joint coordination in the neurological population [72], several innovative control schemes have been designed. Starting from the common control schemes implemented in end-effector devices and exoskeletons, there is closed-loop feedback control with feedforward components.…”

Section: Shoulder; Elbow; Wristmentioning

confidence: 99%

Perspectives and Challenges in Robotic Neurorehabilitation

et al. 2019

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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.In the last decades, innovative robotic technologies have been developed in order to effectively help clinicians during the neurorehabilitation process. The term "robotic technology" in this application domain refers to any mechatronic device with a certain degree of intelligence that can physically intervene on the behavior of the patient, optimizing and speeding up his/her sensorimotor recovery. The two key capabilities of these robots are: (1) Assessing the human sensorimotor function; and (2) re-training the human brain in order to improve the patient's quality of life. However, most of the studies in this field have been focused more on the development of the devices, whereas less effort was made on maximizing their efficacy for promoting recovery. The main challenge consists of designing effective training modalities, supported by appropriate control strategies. Thus, each robotic device supports a pre-defined training modality depending on the low-level control strategy implemented and also on the residual abilities of each patient. Usually, most of the rehabilitation devices implement a passive training modality (robot-driven, position control strategy), where the robot imposes the trajectories, and an active training modality (patient-driven), where the robot modulates its trajectory in response to the subject's intention to move [7,8]. However, among all the different training modalities, the most relevant is the assistive one. Assistive controllers help participants to move their impaired limbs according to the desired postures during grasping, reaching, or walki...

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Section: Shoulder; Elbow; Wristmentioning

confidence: 99%

Perspectives and Challenges in Robotic Neurorehabilitation

et al. 2019

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“…New rehabilitation strategies enabled by new technological developments have been shown to yield performance gains in chronic stroke patients (6). Serious games (7,8) and robotic devices (9) have emerged as promising rehabilitation tools for the improvement of motor function and quality of life.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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“…Notably, a recently developed robot-aided rehabilitation device can be employed for intensive training that complements and improves upon conventional therapy methods [ 4 ]. Robotic devices have the advantage of guiding simple, repetitive movements with consistency [ 5 - 7 ]. Furthermore, they can be combined with games that keep patients engaged while they are challenged physically [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

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

Astrakas

Ottensmeyer

et al. 2021

TONIJ

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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.

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The future role of robots in neuro-rehabilitation

Cited by 25 publications

References 16 publications

Perspectives and Challenges in Robotic Neurorehabilitation

Perspectives and Challenges in Robotic Neurorehabilitation

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

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

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