Explore the vast range of titles available.

Immersive Virtual Reality (iVR) interventions have emerged as effective complements to conventional rehabilitation, with advantages in terms of flexible parametrization, extended data recording and patient engagement. Home-rehabilitation is another impactful, yet unexplored potential. A key requirement is ensuring that patients execute tasks with proper motor coordination and postural awareness: to this end, tactile sensory feedback can be introduced to enhance both immersion and motor guidance. We investigate here the efficacy and the effects on motor coordination of tactile guidance provided within an iVR rehabilitation serious game. A wearable haptic armband, delivering directional continuous and vibrotactile feedback, has been developed to guide grasping and pronosupination tasks. We evaluated the interaction on motor control and task execution using objective performance metrics, biomechanical responses, and subjective assessments, in a group of 12 healthy subjects, and a preliminary feasibility study with two participants with stroke. Findings show that haptic guidance in iVR statistically significantly enhances movement precision, reduces variability, and induces adaptive changes in muscle coordination. Feedback from clinical tests further indicates preliminary indications of usability and acceptability of the developed technology as part of a rehabilitation program. These results underscore the promise of haptic-enriched iVR systems for advancing both clinical and home-based motor rehabilitation.

Upper limb recovery is one of the main goals of post-stroke rehabilitation due to its importance for autonomy in Activities of Daily Living (ADL). Although the efficacy of upper limb Robot-assisted Therapy (RT) is well established in literature, the impact of the initial status of the patient on the effects of RT is still understudied. This paper aims to identify whether demographic, clinical and motor characteristics of stroke patients may influence the ability to independently perform ADL after RT. A retrospective study was conducted on sixty stroke patients who conducted planar upper limb goal-directed tasks with the InMotion 2.0 robot. The RT was administered 5 days/week for 4 weeks and each session lasted 45 minutes. The primary outcome measure was the Modified Barthel Index (BI), dichotomized into favourable (BI ≥75) and unfavourable (BI<75) outcomes. The potential predictors were the demographic and clinical records, and the following clinical assessment scores: Modified Ashworth Scale-Shoulder (MAS-S); Modified Ashworth Scale-Elbow (MAS-E); Fugl-Meyer Assessment Upper Extremity (FMA-UE); upper limb section of the Motricity Index (MIul); total passive Range Of Motion (pROM); and Box and Block Test (BBT). Statistical analysis showed that the BBT, FMA-UE and MIul scores were significant predictors of a favourable outcome in ADL. The cut-off scores of the independent variables were calculated (FMA-UE = 32; MIul = 48; BBT = 3) with respect to the dichotomic BI outcome. Their robustness was assessed with the Fragility Index (FMA-UE = 2; MIul = 3; BBT = 7), showing that BBT is the most robust predictor of favourable BI outcome. Moreover, subjects with all predictors higher than the cut-off scores had higher probability to increase their independence in ADL at the end of the therapy. Demographic records, spasticity and pROM were not identified as predictors. Stroke patients with greater manual dexterity and less impairment appear to have a higher probability of achieving clinically significant ADL outcomes after upper limb RT. The obtained results can help to optimise the management of RT treatment planning. Further studies on a larger number of patients with a long-term follow up are recommended in order to evaluate other potential predictors and to validate the results.

Background and purpose There is little evidence available on the use of robot-assisted therapy in subacute stroke patients. A randomized controlled trial was carried out to evaluate the short-time efficacy of intensive robot-assisted therapy compared to usual physical therapy performed in the early phase after stroke onset. Methods Fifty-three subacute stroke patients at their first-ever stroke were enrolled 30 ± 7 days after the acute event and randomized into two groups, both exposed to standard therapy. Additional 30 sessions of robot-assisted therapy were provided to the Experimental Group. Additional 30 sessions of usual therapy were provided to the Control Group. The following impairment evaluations were performed at the beginning (T0), after 15 sessions (T1), and at the end of the treatment (T2): Fugl-Meyer Assessment Scale (FM), Modified Ashworth Scale-Shoulder (MAS-S), Modified Ashworth Scale-Elbow (MAS-E), Total Passive Range of Motion-Shoulder/Elbow (pROM), and Motricity Index (MI). Results Evidence of significant improvements in MAS-S (p = 0.004), MAS-E (p = 0.018) and pROM (p < 0.0001) was found in the Experimental Group. Significant improvement was demonstrated in both Experimental and Control Group in FM (EG: p < 0.0001, CG: p < 0.0001) and MI (EG: p < 0.0001, CG: p < 0.0001), with an higher improvement in the Experimental Group. Conclusions Robot-assisted upper limb rehabilitation treatment can contribute to increasing motor recovery in subacute stroke patients. Focusing on the early phase of stroke recovery has a high potential impact in clinical practice.

Spasticity is a typical motor disorder in patients affected by stroke. Typically post-stroke rehabilitation consists of repetition of mobilization exercises on impaired limbs, aimed to reduce muscle hypertonia and mitigate spastic reflexes. It is currently strongly debated if the treatment's effectiveness improves with the timeliness of its adoption; in particular, starting intensive rehabilitation as close as possible to the stroke event may counteract the growth and postpone the onset of spasticity. In this paper we present a phase-II clinical validation of a robotic exoskeleton in treating subacute post-stroke patients. Seventeen post-stroke patients participated in 10 daily rehabilitation sessions using the NEUROExos Elbow Module exoskeleton, each one lasting 45 min: the exercises consisted of of the elbow, to detect excessive user's resistance to the movement. We investigated the by reporting possible adverse events, such as mechanical, electrical or software failures of the device or injuries or pain experienced by the patient. As regards the , the Modified Ashworth Scale, was identified as primary outcome measure and the NEEM metrics describing elbow joint resistance to passive extension (i.e., maximum extension torque and zero-torque angle) as secondary outcomes. During the entire duration of the treatments no failures or adverse events for the patients were reported. No statistically significant differences were found in the Modified Ashworth Scale scores, between pre-treatment and post-treatment and between post-treatment and follow-up sessions, indicating the absence of spasticity increase throughout (14 days) and after (3-4 months follow-up) the treatment. Exoskeleton metrics confirmed the absence of significant difference in between pre- and post-treatment data, whereas intra-session data highlighted significant differences in the secondary outcomes, toward a decrease of the subject's joint resistance. The results show that our robotic exoskeleton can be safely used for prolonged sessions in post-stroke and suggest that intensive early rehabilitation treatment may prevent the occurrence of spasticity at a later stage. Moreover, the NEEM metrics were found to be reliable compared to the Modified Ashworth Scale and sensitive to revealing intra-session changes of elbow resistance to passive extension, in agreement with clinical evidences.

Background. Upper limb robot-assisted therapy (RT) provides intensive, repetitive, and task-specific treatment, and its efficacy for stroke survivors is well established in literature. Biomechanical data from robotic devices has been widely employed for patient’s assessment, but rarely it has been analysed for tracking patient progress during RT. The goal of this retrospective study is to analyse built-in kinematic data registered by a planar end-effector robot for assessing the time course of motor recovery and patient’s workspace exploration skills. A comparison of subjects having mild and severe motor impairment has been also conducted. For that purpose, kinematic data recorded by a planar end-effector robot have been processed for investigating how motor performance in executing point-to-point trajectories with different directions changes during RT. Methods. Observational retrospective study of 68 subacute stroke patients who conducted 20 daily sessions of upper limb RT with the InMotion 2.0 (Bionik Laboratories, USA): planar point-to-point reaching tasks with an “assist as needed” strategy. The following kinematic parameters (KPs) were computed for each subject and for each point-to-point trajectory executed during RT: movement accuracy, movement speed, number of peak speed, and task completion time. The Wilcoxon signed-rank tests were used with clinical outcomes. the Friedman test and post hoc Conover’s test (Bonferroni’s correction) were applied to KPs. A secondary data analysis has been conducted by comparing patients having different severities of motor impairment. The level of significance was set at p value < 0.05. Results. At the RT onset, the movements were less accurate and smoothed, and showed higher times of execution than those executed at the end of treatment. The analysis of the time course of KPs highlighted that RT seems to improve the motor function mainly in the first sessions of treatment: most KPs show significant intersession differences during the first 5/10 sessions. Afterwards, no further significant variations occurred. The ability to perform movements away from the body and from the hemiparetic side remains more challenging. The results obtained from the data stratification show significant differences between subjects with mild and severe motor impairment. Conclusion. Significant improvements in motor performance were registered during the time course of upper limb RT in subacute stroke patients. The outcomes depend on movement direction and motor impairment and pave the way to optimize healthcare resources and to design patient-tailored rehabilitative protocols.

Background: The literature on upper limb robot-assisted therapy showed that robot-measured metrics can simultaneously predict registered clinical outcomes. However, only a limited number of studies correlated pre-treatment kinematics with discharge motor recovery. Given the importance of predicting rehabilitation outcomes for optimizing physical therapy, a predictive model for motor recovery that incorporates multidirectional indicators of a patient’s upper limb abilities is needed. Objective: The aim of this study was to develop a predictive model for rehabilitation outcome at discharge (i.e., muscle strength assessed by the Motricity Index of the affected upper limb) based on multidirectional 2D robot-measured kinematics. Methods: Re-analysis of data from 66 subjects with subacute stroke who underwent upper limb robot-assisted therapy with an end-effector robot was performed. Two least squares error multiple linear regression models for outcome prediction were developed and differ in terms of validation procedure: the Split Sample Validation (SSV) model and the Leave-One-Out Cross-Validation (LOOCV) model. In both models, the outputs were the discharge Motricity Index of the affected upper limb and its sub-items assessing elbow flexion and shoulder abduction, while the inputs were the admission robot-measured metrics. Results: The extracted robot-measured features explained the 54% and 71% of the variance in clinical scores at discharge in the SSV and LOOCV validation procedures respectively. Normalized errors ranged from 22% to 35% in the SSV models and from 20% to 24% in the LOOCV models. In all models, the movement path error of the trajectories characterized by elbow flexion and shoulder extension was the significant predictor, and all correlations were significant. Conclusion: This study highlights that motor patterns assessed with multidirectional 2D robot-measured metrics are able to predict clinical evalutation of upper limb muscle strength and may be useful for clinicians to assess, manage, and program a more specific and appropriate rehabilitation in subacute stroke patients.

Background After a stroke, patients show significant modifications of neural control of movement, such as abnormal muscle co-activation, and reduced selectivity and modulation of muscle activity. Nonetheless, results reported in literature do not allow to unequivocally explain whether and, in case, how a cerebrovascular accident affects muscle synergies underlying the control of the upper limb. These discrepancies suggest that a complete understanding of the modular re-organization of muscle activity due to a stroke is still lacking. This pilot study aimed at investigating the effects of the conjunction between the natural ongoing of the pathology and the intense robot-mediated treatment on muscle synergies of the paretic upper limb of subacute post-stroke patients. Methods Six subacute patients, homogenous with respect to the age and the time elapsed from the trauma, and ten healthy age-matched subjects were enrolled. The protocol consisted in achieving planar movement of the upper limb while handling the end-effector of a robotic platform. Patients underwent 6 weeks long treatment while clinical scores, kinematics of the end-effector and muscle activity were recorded. Then we verified whether muscle coordination underlying the motor task was significantly affected by the cerebrovascular accident and how muscle synergies were modified along the treatment. Results Results show that although muscle synergies in subacute stroke patients were qualitatively comparable to those of healthy subjects, those underlying the movement of the shoulder can reflect the functional deficit induced by the pathology. Moreover, the improvement of motor performance due to the treatment was achieved in conjunction with slight modifications of muscle synergies. In this regard, modifications of muscle synergies appeared to be influenced by the different recovering mechanisms across patients presumably due to the heterogeneity of lesions, sides and location of the accident. Conclusions The results support the hypothesis that muscle synergies reflect the injury of the cerebrovascular accident and could document the effects of the functional recovery due to a suitable and customized treatment. Therefore, they open up new possibilities for the development of more effective neuro-rehabilitation protocols.

Despite this, there exist significant dissimilarities and discrepancies with regard to patient eligibility criteria, the effectiveness of treatments based on these devices, and their economic impact, organizational issues and outcome evaluation (Geroin et al., 2018; Lo et al., 2019). The CC method was considered one of the most appropriate ways to tackle the question of rehabilitation assisted by robotic and electromechanical devices for persons with neurological disabilities, a complex problem whose implications extend far beyond the clinical context. The Cicerone CC is expected to provide relevant insights and recommendations on the use of robotic and electromechanical devices for neurorehabilitation, which currently represent promising and challenging technologies potentially able to help reduce impairment, enhance activities of daily living and, ultimately, improve the quality of life of persons with disabilities.

Background Common scales for clinical evaluation of post-stroke upper-limb motor recovery are often complemented with kinematic parameters extracted from movement trajectories. However, there is no a general consensus on which parameters to use. Moreover, the selected variables may be redundant and highly correlated or, conversely, may incompletely sample the kinematic information from the trajectories. Here we sought to identify a set of clinically useful variables for an exhaustive but yet economical kinematic characterization of upper limb movements performed by post-stroke hemiparetic subjects. Methods For this purpose, we pursued a top-down model-driven approach, seeking which kinematic parameters were pivotal for a computational model to generate trajectories of point-to-point planar movements similar to those made by post-stroke subjects at different levels of impairment. Results The set of kinematic variables used in the model allowed for the generation of trajectories significantly similar to those of either sub-acute or chronic post-stroke patients at different time points during the therapy. Simulated trajectories also correctly reproduced many kinematic features of real movements, as assessed by an extensive set of kinematic metrics computed on both real and simulated curves. When inspected for redundancy, we found that variations in the variables used in the model were explained by three different underlying and unobserved factors related to movement efficiency, speed, and accuracy, possibly revealing different working mechanisms of recovery. Conclusion This study identified a set of measures capable of extensively characterizing the kinematics of upper limb movements performed by post-stroke subjects and of tracking changes of different motor improvement aspects throughout the rehabilitation process.