Boosting robot-assisted rehabilitation of stroke hemiparesis by individualized selection of upper limb movements – a pilot study

Abstract

BackgroundIntensive robot-assisted training of the upper limb after stroke can reduce motor impairment, even at the chronic stage. However, the effectiveness of practice for recovery depends on the selection of the practised movements. We hypothesized that rehabilitation can be optimized by selecting the movements to be practiced based on the trainee’s performance profile.MethodsWe present a novel principle (‘steepest gradients’) for performance-based selection of movements. The principle is based on mapping motor performance across a workspace and then selecting movements located at regions of the steepest transition between better and worse performance.To assess the benefit of this principle we compared the effect of 15 sessions of robot-assisted reaching training on upper-limb motor impairment, between two groups of people who have moderate-to-severe chronic upper-limb hemiparesis due to stroke. The test group (N = 7) received steepest gradients-based training, iteratively selected according to the steepest gradients principle with weekly remapping, whereas the control group (N = 9) received a standard “centre-out” reaching training. Training intensity was identical.ResultsBoth groups showed improvement in Fugl-Meyer upper-extremity scores (the primary outcome measure). Moreover, the test group showed significantly greater improvement (twofold) compared to control. The score remained elevated, on average, for at least 4 weeks although the additional benefit of the steepest-gradients -based training diminished relative to control.ConclusionsThis study provides a proof of concept for the superior benefit of performance-based selection of practiced movements in reducing upper-limb motor impairment due to stroke. This added benefit was most evident in the short term, suggesting that performance-based steepest-gradients training may be effective in increasing the rate of initial phase of practice-based recovery; we discuss how long-term retention may also be improved.Trial registrationISRCTN, ISRCTN65226825, registered 12 June 2018 - Retrospectively registered,Electronic supplementary materialThe online version of this article (10.1186/s12984-019-0513-0) contains supplementary material, which is available to authorized users.

Motor improvement estimation and task adaptation for personalized robot-aided therapy: a feasibility study

“…As a matter of fact, motor learning is known to be maximized when the difficulty level of the training task matches the patient’s level of ability [15]. Recent advances in the field of personalized robotic rehabilitation have therefore focused on the design of customized training protocols, including individualized selection of upper limb movements [16]. Different measures have been used to assess the patient’s “status” during training (i.e., motor performance, engagement, etc.)…”

BCI training effects on chronic stroke correlate with functional reorganization in motor-related regions: A concurrent EEG and fMRI study

“…Similar with other studies [37,38], EEG signals from C3 and C4 channels 8 were used for BCI control and meanwhile, all channels were used to generate the 9 real-time topography of the brain dynamic potential for surveillance. 10 Alpha suppression reflects an event-related desynchronization (ERD) of the EEG 11 caused by an increase in neural activity [39], which has been widely utilized in BCI 12 training field and has promising outcome [40,41]. The α rhythm is mainly found 13 over the vertex (near location of Cz) or laterally across the precentral motor cortex, 14 normally at C3 or C4 electrode depending on which hand or arm movement is being 15 performed.…”

“…5 The EEG data were acquired simultaneously with the fMRI using Neuroscan All recording impedances were kept below 5 kΩ. The reference channel was located 11 at the point between Cz and CPz; AFz electrode was treated as ground. Signals…”

“…7 Finally, we applied a surface Laplace filter with the spherical spline method [61] to 8 increase the topographical selectivity, eliminate the volume conduction and high- 9 light the high-spatial-frequency components while attenuating low ones [62]. For 10 group-level analysis convenience, all EEG for those patients with left-hemisphere 11 lesion was left-right flipped before signal processing procedure. 12 Effective Connectivity analysis 13 Based on the fMRI results, three regions of interest (ROIs) were determined for fur- 14 ther effective connectivity analysis, i.e.…”

“…where Ω represents the ROI. GP DC mn indicates the GPDC value calculated be-10 tween EEG signals from electrode m (in ROI i ) and electrode n (in ROI j ) respec- 11 tively with the direction from electrode n to electrode m. N i means the number 12 of representative electrodes in ROI i . Given the direction in a pair of ROIs, ROI i 13 and ROI j can be considered as sink region and source region of information flow 14 respectively.…”