Anusha Venkatakrishnan

The H2 robotic exoskeleton for gait rehabilitation after stroke: early findings from a clinical study

AbstractBackgroundStroke significantly affects thousands of individuals annually, leading to considerable physical impairment and functional disability. Gait is one of the most important activities of daily living affected in stroke survivors. Recent technological developments in powered robotics exoskeletons can create powerful adjunctive tools for rehabilitation and potentially accelerate functional recovery. Here, we present the development and evaluation of a novel lower limb robotic exoskeleton, namely H2 (Technaid S.L., Spain), for gait rehabilitation in stroke survivors.MethodsH2 has six actuated joints and is designed to allow intensive overground gait training. An assistive gait control algorithm was developed to create a force field along a desired trajectory, only applying torque when patients deviate from the prescribed movement pattern. The device was evaluated in 3 hemiparetic stroke patients across 4 weeks of training per individual (approximately 12 sessions). The study was approved by the Institutional Review Board at the University of Houston. The main objective of this initial pre-clinical study was to evaluate the safety and usability of the exoskeleton. A Likert scale was used to measure patient’s perception about the easy of use of the device.ResultsThree stroke patients completed the study. The training was well tolerated and no adverse events occurred. Early findings demonstrate that H2 appears to be safe and easy to use in the participants of this study. The overground training environment employed as a means to enhance active patient engagement proved to be challenging and exciting for patients. These results are promising and encourage future rehabilitation training with a larger cohort of patients.ConclusionsThe developed exoskeleton enables longitudinal overground training of walking in hemiparetic patients after stroke. The system is robust and safe when applied to assist a stroke patient performing an overground walking task. Such device opens the opportunity to study means to optimize a rehabilitation treatment that can be customized for individuals. Trial registration: This study was registered at ClinicalTrials.gov (https://clinicaltrials.gov/show/NCT02114450).

The H2 robotic exoskeleton for gait rehabilitation after stroke

AbstractBackgroundStroke significantly affects thousands of individuals annually, leading to considerable physical impairment and functional disability. Gait is one of the most important activities of daily living affected in stroke survivors. Recent technological developments in powered robotics exoskeletons can create powerful adjunctive tools for rehabilitation and potentially accelerate functional recovery. Here, we present the development and evaluation of a novel lower limb robotic exoskeleton, namely H2 (Technaid S.L., Spain), for gait rehabilitation in stroke survivors.MethodsH2 has six actuated joints and is designed to allow intensive overground gait training. An assistive gait control algorithm was developed to create a force field along a desired trajectory, only applying torque when patients deviate from the prescribed movement pattern. The device was evaluated in 3 hemiparetic stroke patients across 4 weeks of training per individual (approximately 12 sessions). The study was approved by the Institutional Review Board at the University of Houston. The main objective of this initial pre-clinical study was to evaluate the safety and usability of the exoskeleton. A Likert scale was used to measure patient’s perception about the easy of use of the device.ResultsThree stroke patients completed the study. The training was well tolerated and no adverse events occurred. Early findings demonstrate that H2 appears to be safe and easy to use in the participants of this study. The overground training environment employed as a means to enhance active patient engagement proved to be challenging and exciting for patients. These results are promising and encourage future rehabilitation training with a larger cohort of patients.ConclusionsThe developed exoskeleton enables longitudinal overground training of walking in hemiparetic patients after stroke. The system is robust and safe when applied to assist a stroke patient performing an overground walking task. Such device opens the opportunity to study means to optimize a rehabilitation treatment that can be customized for individuals. Trial registration: This study was registered at ClinicalTrials.gov (https://clinicaltrials.gov/show/NCT02114450).

Combining transcranial direct current stimulation and neuroimaging: novel insights in understanding neuroplasticity.

In recent years, noninvasive brain stimulation techniques like transcranial direct current stimulation (tDCS) have gained immense popularity owing to their effects on modulating cortical activity and consequently motor and cognitive performance. However, the neurophysiology underlying such neuroplastic changes is less understood. This article critically evaluates the contemporary approach of combined tDCS and neuroimaging as a means to provide novel insights in understanding the neurophysiological and neuroplastic processes modulated by this brain stimulation technique. We end by briefly suggesting further lines of inquiry.

Applications of Brain–Machine Interface Systems in Stroke Recovery and Rehabilitation

Stroke is a leading cause of disability, significantly impacting the quality of life (QOL) in survivors, and rehabilitation remains the mainstay of treatment in these patients. Recent engineering and technological advances such as brain–machine interfaces (BMI) and robotic rehabilitative devices are promising to enhance stroke neurorehabilitation, to accelerate functional recovery and improve QOL. This review discusses the recent applications of BMI and robotic-assisted rehabilitation in stroke patients. We present the framework for integrated BMI and robotic-assisted therapies, and discuss their potential therapeutic, assistive and diagnostic functions in stroke rehabilitation. Finally, we conclude with an outlook on the potential challenges and future directions of these neurotechnologies, and their impact on clinical rehabilitation.

Design and optimization of an EEG-based brain machine interface (BMI) to an upper-limb exoskeleton for stroke survivors

This study demonstrates the feasibility of detecting motor intent from brain activity of chronic stroke patients using an asynchronous electroencephalography (EEG)-based brain machine interface (BMI). Intent was inferred from movement related cortical potentials (MRCPs) measured over an optimized set of EEG electrodes. Successful intent detection triggered the motion of an upper-limb exoskeleton (MAHI Exo-II), to guide movement and to encourage active user participation by providing instantaneous sensory feedback. Several BMI design features were optimized to increase system performance in the presence of single-trial variability of MRCPs in the injured brain: (1) an adaptive time window was used for extracting features during BMI calibration; (2) training data from two consecutive days were pooled for BMI calibration to increase robustness to handle the day-to-day variations typical of EEG, and (3) BMI predictions were gated by residual electromyography (EMG) activity from the impaired arm, to reduce the number of false positives. This patient-specific BMI calibration approach can accommodate a broad spectrum of stroke patients with diverse motor capabilities. Following BMI optimization on day 3, testing of the closed-loop BMI-MAHI exoskeleton, on 4th and 5th days of the study, showed consistent BMI performance with overall mean true positive rate (TPR) = 62.7 ± 21.4% on day 4 and 67.1 ± 14.6% on day 5. The overall false positive rate (FPR) across subjects was 27.74 ± 37.46% on day 4 and 27.5 ± 35.64% on day 5; however for two subjects who had residual motor function and could benefit from the EMG-gated BMI, the mean FPR was quite low (< 10%). On average, motor intent was detected −367 ± 328 ms before movement onset during closed-loop operation. These findings provide evidence that closed-loop EEG-based BMI for stroke patients can be designed and optimized to perform well across multiple days without system recalibration.

Parkinson’s disease differentially affects adaptation to gradual as compared to sudden visuomotor distortions

Patients with Parkinsons disease (PD) have difficulties in movement adaptation to optimize performance in novel environmental contexts such as altered screen cursor-hand relationships. Prior studies have shown that the time course of the distortion differentially affects visuomotor adaptation to screen cursor rotations, suggesting separate mechanisms for gradual and sudden adaptation. Moreover, studies in human and non-human primates suggest that adaptation to sudden kinematic distortions may engage the basal ganglia, whereas adaptation to gradual kinematic distortions involves cerebellar structures. In the present studies, participants were patients with PD, who performed center-out pointing movements, using either a digitizer tablet and pen or a computer trackball, under normal or rotated screen cursor feedback conditions. The initial study tested patients with PD using a cross-over experimental design for adaptation to gradual as compared with sudden rotated hand-screen cursor relationships and revealed significant after-effects for the gradual adaptation task only. Consistent with these results, findings from a follow-up experiment using a trackball that required only small finger movements showed that patients with PD adapt better to gradual as against sudden perturbations, when compared to age-matched healthy controls. We conclude that Parkinsons disease affects adaptation to sudden visuomotor distortions but spares adaptation to gradual distortions.

Real-time strap pressure sensor system for powered exoskeletons.

Assistive and rehabilitative powered exoskeletons for spinal cord injury (SCI) and stroke subjects have recently reached the clinic. Proper tension and joint alignment are critical to ensuring safety. Challenges still exist in adjustment and fitting, with most current systems depending on personnel experience for appropriate individual fastening. Paraplegia and tetraplegia patients using these devices have impaired sensation and cannot signal if straps are uncomfortable or painful. Excessive pressure and blood-flow restriction can lead to skin ulcers, necrotic tissue and infections. Tension must be just enough to prevent slipping and maintain posture. Research in pressure dynamics is extensive for wheelchairs and mattresses, but little research has been done on exoskeleton straps. We present a system to monitor pressure exerted by physical human-machine interfaces and provide data about levels of skin/body pressure in fastening straps. The system consists of sensing arrays, signal processing hardware with wireless transmission, and an interactive GUI. For validation, a lower-body powered exoskeleton carrying the full weight of users was used. Experimental trials were conducted with one SCI and one able-bodied subject. The system can help prevent skin injuries related to excessive pressure in mobility-impaired patients using powered exoskeletons, supporting functionality, independence and better overall quality of life.

An integrated neuro-robotic interface for stroke rehabilitation using the NASA X1 powered lower limb exoskeleton.

Stroke remains a leading cause of disability, limiting independent ambulation in survivors, and consequently affecting quality of life (QOL). Recent technological advances in neural interfacing with robotic rehabilitation devices are promising in the context of gait rehabilitation. Here, the X1, NASAs powered robotic lower limb exoskeleton, is introduced as a potential diagnostic, assistive, and therapeutic tool for stroke rehabilitation. Additionally, the feasibility of decoding lower limb joint kinematics and kinetics during walking with the X1 from scalp electroencephalographic (EEG) signals - the first step towards the development of a brain-machine interface (BMI) system to the X1 exoskeleton - is demonstrated.

Independent component analysis of resting brain activity reveals transient modulation of local cortical processing by transcranial direct current stimulation

Neuroplasticity induced by transcranial direct current stimulation (tDCS) contributes to motor learning although the underlying mechanisms are incompletely understood. Here, we investigated the effects of tDCS on resting brain dynamics recorded by whole-head magnetoencephalography (MEG) pre- and up to 35 minutes post-tDCS or sham over the left primary motor cortex (M1) in healthy adults. Owing to superior temporal and spatial resolution of MEG, we sought to apply a robust, blind and data-driven analytic approach such as independent component analysis (ICA) and statistical clustering to these data to investigate potential neuroplastic effects of tDCS during resting state conditions. We found decreased alpha and increased gamma band power that outlasted the real tDCS stimulation period in a fronto-parietal motor network relative to sham. However, this method could not find differences between anodal and cathodal polarities of tDCS. These results suggest that tDCS over M1 modulates resting brain dynamics in a fronto-parietal motor network (that includes the stimulated location), indicative of within-network enhanced localized cortical processing.

Detecting movement intent from scalp EEG in a novel upper limb robotic rehabilitation system for stroke

Stroke can be a source of significant upper extremity dysfunction and affect the quality of life (QoL) in survivors. In this context, novel rehabilitation approaches employing robotic rehabilitation devices combined with brain-machine interfaces can greatly help in expediting functional recovery in these individuals by actively engaging the user during therapy. However, optimal training conditions and parameters for these novel therapeutic systems are still unknown. Here, we present preliminary findings demonstrating successful movement intent detection from scalp electroencephalography (EEG) during robotic rehabilitation using the MAHI Exo-II in an individual with hemiparesis following stroke. These findings have strong clinical implications for the development of closed-loop brain-machine interfaces to robotic rehabilitation systems.