Effects of the Alternate Combination of "Error-Enhancing" and "Active Assistive" Robot-Mediated Treatments on Stroke Patients

Error Enhancement for Upper Limb Rehabilitation in the Chronic Phase after Stroke: A 5-Day Pre-Post Intervention Study

A large proportion of chronic stroke survivors still struggle with upper limb (UL) problems in daily activities, typically reaching tasks. During three-dimensional reaching movements, the deXtreme robot offers error enhancement forces. Error enhancement aims to improve the quality of movement. We investigated clinical and patient-reported outcomes and assessed the quality of movement before and after a 5 h error enhancement training with the deXtreme robot. This pilot study had a pre-post intervention design, recruiting 22 patients (mean age: 57 years, mean days post-stroke: 1571, male/female: 12/10) in the chronic phase post-stroke with UL motor impairments. Patients received 1 h robot treatment for five days and were assessed at baseline and after training, collecting (1) clinical, (2) patient-reported, and (3) kinematic (KINARM, BKIN Technologies Ltd., Kingston, ON, Canada) outcome measures. Our analysis revealed significant improvements (median improvement (Q1-Q3)) in (1) UL Fugl-Meyer assessment (1.0 (0.8-3.0), p < 0.001) and action research arm test (2.0 (0.8-2.0), p < 0.001); (2) motor activity log, amount of use (0.1 (0.0-0.3), p < 0.001) and quality of use (0.1 (0.1-0.5), p < 0.001) subscale; (3) KINARM-evaluated position sense (-0.45 (-0.81-0.09), p = 0.030) after training. These findings provide insight into clinical self-reported and kinematic improvements in UL functioning after five hours of error enhancement UL training.

A unified scheme for the benchmarking of upper limb functions in neurological disorders

BACKGROUND

In neurorehabilitation, we are witnessing a growing awareness of the importance of standardized quantitative assessment of limb functions. Detailed assessments of the sensorimotor deficits following neurological disorders are crucial. So far, this assessment has relied mainly on clinical scales, which showed several drawbacks. Different technologies could provide more objective and repeatable measurements. However, the current literature lacks practical guidelines for this purpose. Nowadays, the integration of available metrics, protocols, and algorithms into one harmonized benchmarking ecosystem for clinical and research practice is necessary.

METHODS

This work presents a benchmarking framework for upper limb capacity. The scheme resulted from a multidisciplinary and iterative discussion among several partners with previous experience in benchmarking methodology, robotics, and clinical neurorehabilitation. We merged previous knowledge in benchmarking methodologies for human locomotion and direct clinical and engineering experience in upper limb rehabilitation. The scheme was designed to enable an instrumented evaluation of arm capacity and to assess the effectiveness of rehabilitative interventions with high reproducibility and resolution. It includes four elements: (1) a taxonomy for motor skills and abilities, (2) a list of performance indicators, (3) a list of required sensor modalities, and (4) a set of reproducible experimental protocols.

RESULTS

We proposed six motor primitives as building blocks of most upper-limb daily-life activities and combined them into a set of functional motor skills. We identified the main aspects to be considered during clinical evaluation, and grouped them into ten motor abilities categories. For each ability, we proposed a set of performance indicators to quantify the proposed ability on a quantitative and high-resolution scale. Finally, we defined the procedures to be followed to perform the benchmarking assessment in a reproducible and reliable way, including the definition of the kinematic models and the target muscles.

CONCLUSIONS

This work represents the first unified scheme for the benchmarking of upper limb capacity. To reach a consensus, this scheme should be validated with real experiments across clinical conditions and motor skills. This validation phase is expected to create a shared database of human performance, necessary to have realistic comparisons of treatments and drive the development of new personalized technologies.

Haptic Training: Which Types Facilitate (re)Learning of Which Motor Task and for Whom? Answers by a Review

The use of robots has attracted researchers to design numerous haptic training methods to support motor learning. However, investigations of new methods yielded inconclusive results regarding their effectiveness to enhance learning due to the diversity of tasks, haptic designs, participants' skill level, and study protocols. In this review, we developed a taxonomy to identify generalizable findings out of publications on haptic training. In the taxonomy, we grouped the results of studies on healthy learners based on participants' skill level and tasks' characteristics. Our inspection of included studies revealed that: i) Performance-enhancing haptic methods were beneficial for novices, ii) Training with haptics was as effective as training with other feedback modalities, and iii) Performance-enhancing and performance-degrading haptic methods were useful for the learning of temporal and spatial aspects, respectively. We also observed that these findings are in line with results from robot-aided neurorehabilitation studies on patients. Our review suggests that haptic training can be effective to foster learning, especially when the information cannot be provided with other feedback modalities. We believe the findings from the taxonomy constitute a general guide, which can assist researchers when designing studies to investigate the effectiveness of haptics on learning different tasks.

Advanced Neurotechnologies for the Restoration of Motor Function

Stroke is one of the leading causes of long-term disability. Advanced technological solutions ("neurotechnologies") exploiting robotic systems and electrodes that stimulate the nervous system can increase the efficacy of stroke rehabilitation. Recent studies on these approaches have shown promising results. However, a paradigm shift in the development of new approaches must be made to significantly improve the clinical outcomes of neurotechnologies compared with those of traditional therapies. An "evolutionary" change can occur only by understanding in great detail the basic mechanisms of natural stroke recovery and technology-assisted neurorehabilitation. In this review, we first describe the results achieved by existing neurotechnologies and highlight their current limitations. In parallel, we summarize the data available on the mechanisms of recovery from electrophysiological, behavioral, and anatomical studies in humans and rodent models. Finally, we propose new approaches for the effective use of neurotechnologies in stroke survivors, as well as in people with other neurological disorders.

Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke

BACKGROUND

Electromechanical and robot-assisted arm training devices are used in rehabilitation, and may help to improve arm function after stroke.

OBJECTIVES

To assess the effectiveness of electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength in people after stroke. We also assessed the acceptability and safety of the therapy.

SEARCH METHODS

We searched the Cochrane Stroke Group's Trials Register (last searched January 2018), the Cochrane Central Register of Controlled Trials (CENTRAL) (the Cochrane Library 2018, Issue 1), MEDLINE (1950 to January 2018), Embase (1980 to January 2018), CINAHL (1982 to January 2018), AMED (1985 to January 2018), SPORTDiscus (1949 to January 2018), PEDro (searched February 2018), Compendex (1972 to January 2018), and Inspec (1969 to January 2018). We also handsearched relevant conference proceedings, searched trials and research registers, checked reference lists, and contacted trialists, experts, and researchers in our field, as well as manufacturers of commercial devices.

SELECTION CRITERIA

Randomised controlled trials comparing electromechanical and robot-assisted arm training for recovery of arm function with other rehabilitation or placebo interventions, or no treatment, for people after stroke.

DATA COLLECTION AND ANALYSIS

Two review authors independently selected trials for inclusion, assessed trial quality and risk of bias, used the GRADE approach to assess the quality of the body of evidence, and extracted data. We contacted trialists for additional information. We analysed the results as standardised mean differences (SMDs) for continuous variables and risk differences (RDs) for dichotomous variables.

MAIN RESULTS

We included 45 trials (involving 1619 participants) in this update of our review. Electromechanical and robot-assisted arm training improved activities of daily living scores (SMD 0.31, 95% confidence interval (CI) 0.09 to 0.52, P = 0.0005; I² = 59%; 24 studies, 957 participants, high-quality evidence), arm function (SMD 0.32, 95% CI 0.18 to 0.46, P < 0.0001, I² = 36%, 41 studies, 1452 participants, high-quality evidence), and arm muscle strength (SMD 0.46, 95% CI 0.16 to 0.77, P = 0.003, I² = 76%, 23 studies, 826 participants, high-quality evidence). Electromechanical and robot-assisted arm training did not increase the risk of participant dropout (RD 0.00, 95% CI -0.02 to 0.02, P = 0.93, I² = 0%, 45 studies, 1619 participants, high-quality evidence), and adverse events were rare.

AUTHORS' CONCLUSIONS

People who receive electromechanical and robot-assisted arm training after stroke might improve their activities of daily living, arm function, and arm muscle strength. However, the results must be interpreted with caution although the quality of the evidence was high, because there were variations between the trials in: the intensity, duration, and amount of training; type of treatment; participant characteristics; and measurements used.

Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke

BACKGROUND

Electromechanical and robot-assisted arm training devices are used in rehabilitation, and may help to improve arm function after stroke.

OBJECTIVES

To assess the effectiveness of electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength in people after stroke. We also assessed the acceptability and safety of the therapy.

SEARCH METHODS

We searched the Cochrane Stroke Group's Trials Register (last searched February 2015), the Cochrane Central Register of Controlled Trials (CENTRAL) (the Cochrane Library 2015, Issue 3), MEDLINE (1950 to March 2015), EMBASE (1980 to March 2015), CINAHL (1982 to March 2015), AMED (1985 to March 2015), SPORTDiscus (1949 to March 2015), PEDro (searched April 2015), Compendex (1972 to March 2015), and Inspec (1969 to March 2015). We also handsearched relevant conference proceedings, searched trials and research registers, checked reference lists, and contacted trialists, experts, and researchers in our field, as well as manufacturers of commercial devices.

SELECTION CRITERIA

Randomised controlled trials comparing electromechanical and robot-assisted arm training for recovery of arm function with other rehabilitation or placebo interventions, or no treatment, for people after stroke.

DATA COLLECTION AND ANALYSIS

Two review authors independently selected trials for inclusion, assessed trial quality and risk of bias, and extracted data. We contacted trialists for additional information. We analysed the results as standardised mean differences (SMDs) for continuous variables and risk differences (RDs) for dichotomous variables.

MAIN RESULTS

We included 34 trials (involving 1160 participants) in this update of our review. Electromechanical and robot-assisted arm training improved activities of daily living scores (SMD 0.37, 95% confidence interval (CI) 0.11 to 0.64, P = 0.005, I² = 62%), arm function (SMD 0.35, 95% CI 0.18 to 0.51, P < 0.0001, I² = 36%), and arm muscle strength (SMD 0.36, 95% CI 0.01 to 0.70, P = 0.04, I² = 72%), but the quality of the evidence was low to very low. Electromechanical and robot-assisted arm training did not increase the risk of participant drop-out (RD 0.00, 95% CI -0.02 to 0.03, P = 0.84, I² = 0%) with moderate-quality evidence, and adverse events were rare.

AUTHORS' CONCLUSIONS

People who receive electromechanical and robot-assisted arm and hand training after stroke might improve their activities of daily living, arm and hand function, and arm and hand muscle strength. However, the results must be interpreted with caution because the quality of the evidence was low to very low, and there were variations between the trials in the intensity, duration, and amount of training; type of treatment; and participant characteristics.

Optimizing learning of a locomotor task: amplifying errors as needed

Research on motor learning has emphasized that errors drive motor adaptation. Thereby, several researchers have proposed robotic training strategies that amplify movement errors rather than decrease them. In this study, the effect of different robotic training strategies that amplify errors on learning a complex locomotor task was investigated. The experiment was conducted with a one degree-of freedom robotic stepper (MARCOS). Subjects were requested to actively coordinate their legs in a desired gait-like pattern in order to track a Lissajous figure presented on a visual display. Learning with three different training strategies was evaluated: (i) No perturbation: the robot follows the subjects' movement without applying any perturbation, (ii) Error amplification: existing errors were amplified with repulsive forces proportional to errors, (iii) Noise disturbance: errors were evoked with a randomly-varying force disturbance. Results showed that training without perturbations was especially suitable for a subset of initially less-skilled subjects, while error amplification seemed to benefit more skilled subjects. Training with error amplification, however, limited transfer of learning. Random disturbing forces benefited learning and promoted transfer in all subjects, probably because it increased attention. These results suggest that learning a locomotor task can be optimized when errors are randomly evoked or amplified based on subjects' initial skill level.