Effects of Slit Edge Notches on Mechanical Properties of 3D-Printed PA12 Nylon Kirigami Specimens.

Kirigami structures, a Japanese paper-cutting art form, has been widely adopted in engineering design, including robotics, biomedicine, energy harvesting, and sensing. This study investigated the effects of slit edge notches on the mechanical properties, particularly the tensile stiffness, of 3D-printed PA12 nylon kirigami specimens. Thirty-five samples were designed with various notch sizes and shapes and printed using a commercial 3D printer with multi-jet fusion (MJF) technique. Finite element analysis (FEA) was employed to determine the mechanical properties of the samples computationally. The results showed that the stiffness of the kirigami samples is positively correlated with the number of edges in the notch shape and quadratically negatively correlated with the notch area of the samples. The mathematical relationship between the stretching tensile stiffness of the samples and their notch area was established and explained from an energy perspective. The relationship established in this study can help fine-tune the stiffness of kirigami-inspired structures without altering the primary parameters of kirigami samples. With the rapid fabrication method (e.g., 3D printing technique), the kirigami samples with suitable mechanical properties can be potentially applied to planar springs for hinge structures or energy-absorbing/harvesting structures. These findings will provide valuable insights into the development and optimization of kirigami-inspired structures for various applications in the future.

Impact of anodal high-definition transcranial direct current stimulation of medial prefrontal cortex on stroop task performance and its electrophysiological correlates. A pilot study.

Medial prefrontal cortex (MPC) has been associated with a wide range of cognitive functions; however, its specific role in interference control is not fully understood. The current study investigates the role of MPC in interference control by externally stimulating it with an electric current and studying associated behavioral and neurophysiological markers. Participants randomly assigned to experimental and sham groups were administered with a high-definition transcranial direct current stimulation (HD-tDCS) of 2 mA for 15 min. They performed a classic color-word Stroop task before, during, and immediately after the stimulation, while electroencephalography (EEG) was acquired throughout the experiment. A decrease in reaction time (RT) for incongruent and neutral trials of the Stroop task was observed in the experimental group compared to the sham group with a significant reduction in the Stroop Effect after stimulation; however, no significant change was observed in the amplitude and latency of N200, P200, and N450 event related potentials. Furthermore, the resting state complexity of the neural signals in the medial frontal region was decreased in the experimental group with a decrease in theta frequency band during the Stroop task. We conclude that the stimulation of MPC increases its efficiency in resolving the conflict by reducing theta power during the Stroop task, which is also reflected in the reduced complexity in the resting state EEG. (ClinicalTrials.gov Identifier: NCT04318522).

Interface Engineering of Flexible Piezoresistive Sensors via Near-Field Electrospinning Processed Spacer Layers.

The interface contact between the active material and its neighboring metal electrodes dominates the sensing response of mainstream high-sensitivity piezoresistive pressure sensors. However, the properties of such interface are often difficult to control and preserve owing to the limited strategies to precisely engineer the surface structure and mechanical property of the active material. Here, a top-down fabrication method to create a grid-like polyurethane fiber-based spacer layer at the interface between a piezoresistive layer and its contact electrodes is proposed. The tuning of the period and thickness of the spacer layer is conveniently achieved by a programmable near-field electrospinning process, and the influence of the spacer structure on the sensing performance is systematically investigated. The sensor with the optimized spacer layer shows a widened sensing range (230 kPa) while maintaining a high sensitivity (1.91 kPa-1 ). Furthermore, the output current fluctuation of the sensors during a 74 000-cycle test is drastically reduced from 14.28% (without a spacer) to 3.63% (with a spacer), demonstrating greatly enhanced long-term reliability. The new near-field electrospinning-based strategy is capable of tuning sensor responses without changing the active material, providing a universal and scalable path to engineer the performances of contact-dominant sensors.

Cortico-muscular interaction to monitor the effects of neuromuscular electrical stimulation pedaling training in chronic stroke.

Neuromuscular electrical stimulation (NMES) has been widely utilized in post-stroke motor restoration. However, its impact on the closed-loop sensorimotor control process remains largely unclear. This is the first study to investigate the directional changes in cortico-muscular interactions after repetitive rehabilitation training by measuring the noninvasive electroencephalogram (EEG) and electromyography (EMG) signals. In this study, 10 subjects with chronic stroke received 20 sessions of NMES-pedaling interventions, and each training session included three 10-min NMES-driven pedaling trials. In addition, pre- and post-intervention assessments of lower limb isometric contraction were conducted before and after the whole NMES-pedaling interventions. The EEG (128 channels) and EMG (3 bilateral lower limb sensors) signals were collected during the isometric contraction tasks for the paretic and non-paretic lower limbs. Both the cortico-muscular coherence (CMC) and generalized partial directed coherence (GPDC) values were analyzed between eight selected EEG channels in the central primary motor cortex and EMG channels. The results revealed significant clinical improvements. Additionally, rehabilitation training facilitated cortico-muscular interaction of the ipsilesional brain and paretic lower limbs (p = 0.004). Moreover, both the descending and ascending cortico-muscular pathways were altered after NMES-training (p = 0.001, p < 0.001). Therefore, the results implied potential applications of EEG-EMG in understanding neuromuscular changes during the post-stroke motor rehabilitation process.

Effects of a Soft Robotic Hand for Hand Rehabilitation in Chronic Stroke Survivors.

OBJECTIVES: Soft robotic hands are proposed for stroke rehabilitation in terms of their high compliance and low inherent stiffness. We investigated the clinical efficacy of a soft robotic hand that could actively flex and extend the fingers in chronic stroke subjects with different levels of spasticity. METHODS: Sixteen chronic stroke subjects were recruited into this single-group study. Subjects underwent 20 sessions of 1-hour EMG-driven soft robotic hand training. Training effect was evaluated by the pre-training and post-training assessments with the clinical scores: Action Research Arm Test(ARAT), Fugl-Meyer Assessment for Upper Extremity(FMA-UE), Box-and-Block test(BBT), Modified Ashworth Scale(MAS), and maximum voluntary grip strength. RESULTS: For all the recruited subjects (n = 16), significant improvement of upper limb function was generally observed in ARAT (increased mean=2.44, P = 0.032), FMA-UE (increased mean=3.31, P = 0.003), BBT (increased mean=1.81, P = 0.024), and maximum voluntary grip strength (increased mean=2.14 kg, P < 0.001). No significant change was observed in terms of spasticity with the MAS (decreased mean=0.11, P = 0.423). Further analysis showed subjects with mild or no finger flexor spasticity (MAS<2, n = 9) at pre-training had significant improvement of upper limb function after 20 sessions of training. However, for subjects with moderate and severe finger flexor spasticity (MAS=2,3, n = 7) at pre-training, no significant change in clinical scores was shown and only maximum voluntary grip strength had significant increase. CONCLUSION: EMG-driven rehabilitation training using the soft robotic hand with flexion and extension could be effective for the functional recovery of upper limb in chronic stroke subjects with mild or no spasticity.

A hybrid network for automatic hepatocellular carcinoma segmentation in H&E-stained whole slide images.

Hepatocellular carcinoma (HCC), as the most common type of primary malignant liver cancer, has become a leading cause of cancer deaths in recent years. Accurate segmentation of HCC lesions is critical for tumor load assessment, surgery planning, and postoperative examination. As the appearance of HCC lesions varies greatly across patients, traditional manual segmentation is a very tedious and time-consuming process, the accuracy of which is also difficult to ensure. Therefore, a fully automated and reliable HCC segmentation system is in high demand. In this work, we present a novel hybrid neural network based on multi-task learning and ensemble learning techniques for accurate HCC segmentation of hematoxylin and eosin (H&E)-stained whole slide images (WSIs). First, three task-specific branches are integrated to enlarge the feature space, based on which the network is able to learn more general features and thus reduce the risk of overfitting. Second, an ensemble learning scheme is leveraged to perform feature aggregation, in which selective kernel modules (SKMs) and spatial and channel-wise squeeze-and-excitation modules (scSEMs) are adopted for capturing the features from different spaces and scales. Our proposed method achieves state-of-the-art performance on three publicly available datasets, with segmentation accuracies of 0.797, 0.923, and 0.765 in the PAIP, CRAG, and UHCMC&CWRU datasets, respectively, which demonstrates its effectiveness in addressing the HCC segmentation problem. To the best of our knowledge, this is also the first work on the pixel-wise HCC segmentation of H&E-stained WSIs.

Changes in electroencephalography complexity and functional magnetic resonance imaging connectivity following robotic hand training in chronic stroke.

Introduction: In recent years, robotic training has been utilized for recovery of motor control in patients with motor deficits. Along with clinical assessment, electrical patterns in the brain have emerged as a marker for studying changes in the brain associated with brain injury and rehabilitation. These changes mainly involve an imbalance between the two hemispheres. We aimed to study the effect of brain computer interface (BCI)-based robotic hand training on stroke subjects using clinical assessment, electroencephalographic (EEG) complexity analysis, and functional magnetic resonance imaging (fMRI) connectivity analysis. Method: Resting-state simultaneous EEG-fMRI was conducted on 14 stroke subjects before and after training who underwent 20 sessions robot hand training. Fractal dimension (FD) analysis was used to assess neuronal impairment and functional recovery using the EEG data, and fMRI connectivity analysis was performed to assess changes in the connectivity of brain networks. Results: FD results indicated a significant asymmetric difference between the ipsilesional and contralesional hemispheres before training, which was reduced after robotic hand training. Moreover, a positive correlation between interhemispheric asymmetry change for central brain region and change in Fugl Meyer Assessment (FMA) scores for upper limb was observed. Connectivity results showed a significant difference between pre-training interhemispheric connectivity and post-training interhemispheric connectivity. Moreover, the change in connectivity correlated with the change in FMA scores. Results also indicated a correlation between the increase in connectivity for motor regions and decrease in FD interhemispheric asymmetry for central brain region covering the motor area. Conclusion: In conclusion, robotic hand training significantly facilitated stroke motor recovery, and FD, along with connectivity analysis can detect neuroplasticity changes.

Postural Stability in Obese Preoperative Bariatric Patients Using Static and Dynamic Evaluation.

INTRODUCTION: Globally, 300 million adults have clinical obesity. Heightened adiposity and inadequate musculature secondary to obesity alter bipedal stance and gait, diminish musculoskeletal tissue quality, and compromise neuromuscular feedback; these physiological changes alter stability and increase injury risk from falls. Studies in the field focus on obese patients across a broad range of body mass indices (BMI >30 kg/m2) but without isolating the most morbidly obese subset (BMI ≥40 kg/m2). We investigated the impact of obesity in perturbing postural stability in morbidly obese subjects elected for bariatric intervention, harboring a higher-spectrum BMI. SUBJECTS AND METHODS: Traditional force plate measurements and stabilograms are gold standards employed when measuring center of pressure (COP) and postural sway. To quantify the extent of postural instability in subjects with obesity before bariatric surgery, we assessed 17 obese subjects with an average BMI of 40 kg/m2 in contrast to 13 nonobese subjects with an average BMI of 30 kg/m2. COP and postural sway were measured from static and dynamic tasks. Involuntary movements were measured when patients performed static stances, with eyes either opened or closed. Two additional voluntary movements were measured when subjects performed dynamic, upper torso tasks with eyes opened. RESULTS: Mean body weight was 85% (p < 0.001) greater in obese than nonobese subjects. Following static balance assessments, we observed greater sway displacement in the anteroposterior (AP) direction in obese subjects with eyes open (87%, p < 0.002) and eyes closed (76%, p = 0.04) versus nonobese subjects. Obese subjects also exhibited a higher COP velocity in static tests when subjects' eyes were open (47%, p = 0.04). Dynamic tests demonstrated no differences between groups in sway displacement in either direction; however, COP velocity in the mediolateral (ML) direction was reduced (31%, p < 0.02) in obese subjects while voluntarily swaying in the AP direction, but increased in the same cohort when swaying in the ML direction (40%, p < 0.04). DISCUSSION AND CONCLUSION: Importantly, these data highlight obesity's contribution towards increased postural instability. Obese subjects exhibited greater COP displacement at higher AP velocities versus nonobese subjects, suggesting that clinically obese individuals show greater instability than nonobese subjects. Identifying factors contributory to instability could encourage patient-specific physical therapies and presurgical measures to mitigate instability and monitor postsurgical balance improvements.

Soft Rehabilitation Actuator With Integrated Post-stroke Finger Spasticity Evaluation.

Strokes cause severe impairment of hand function because of the spasticity in the affected upper extremities. Proper spasticity evaluation is critical to facilitate neural plasticity for rehabilitation after stroke. However, existing methods for measuring spasticity, e.g. Modified Ashworth Scale (MAS), highly depends on clinicians' experiences, which are subjective and lacks quantitative details. Here, we introduce the first rehabilitation actuator that objectively reflects the condition of post-stroke finger spasticity. The actuator is 3D printed with soft materials. By considering the finger and the actuator together, the spasticity, i.e. stiffness, in finger is obtained from the pressure-angle relationship. The method is validated by simulations using finite element analysis (FEA) and experiments on mannequin fingers. Furthermore, it is examined on four stroke subjects and four healthy subjects. Results show the finger stiffness increases significantly from healthy subjects to stroke subjects, particularly those with high MAS score. For patients with the same MAS score, stiffness variation can be a few times. With this soft actuator, a hand rehabilitation robot that may tell the therapeutic progress during the rehabilitation training is readily available.

Anodal Transcranial Direct Current Stimulation of Anterior Cingulate Cortex Modulates Subcortical Brain Regions Resulting in Cognitive Enhancement.

Transcranial direct current stimulation (tDCS) has been widely utilized in research settings and modulates brain activity. The application of anodal tDCS on the prefrontal cortex has indicated improvement in cognitive functioning. The cingulate cortex, situated in the medial aspect of the prefrontal cortex, has been identified as a core region performing cognitive functions. Most of the previous studies investigating the impact of stimulation on the prefrontal cortex stimulated the dorsolateral prefrontal cortex (DLPFC), however, the impact of stimulation on cingulate has not been explored. The current study investigates the effect of stimulation on the resting-state functional connectivity of the anterior cingulate cortex with other regions of the brain and changes in behavioral results in a color-word Stroop task, which has repeatedly elicited activation in different regions of the cingulate. Twenty subjects were randomly assigned to the experimental and sham group, and their medial prefrontal area was stimulated using MRI compatible tDCS. Resting-state functional magnetic resonance imaging (rs-fMRI) and cognitive Stroop task were monitored before, during, and after the stimulation. Neuroimaging results indicated a significant decrease in resting-state functional connectivity in the experimental group during and after stimulation as compared to before stimulation in two clusters including right insular cortex, right central operculum cortex, right frontal operculum cortex and right planum polare with the left anterior cingulate cortex (L-ACC) selected as the seed. The behavioral results indicated a significant decrease in reaction time (RT) following stimulation in the experimental group compared to the sham group. Moreover, the change in functional connectivity in subcortical regions with L-ACC as the seed and change in RT was positively correlated. The results demonstrated that ACC has a close functional relationship with the subcortical regions, and stimulation of ACC can modulate these connections, which subsequently improves behavioral performance, thus, providing another potential target of stimulation for cognitive enhancement. Clinical Trial Registration: ClinicalTrials.gov Identifier: NCT04318522.

Verification of Finger Joint Stiffness Estimation Method With Soft Robotic Actuator.

Stroke has been the leading cause of disability due to the induced spasticity in the upper extremity. The constant flexion of spastic fingers following stroke has not been well described. Accurate measurements for joint stiffness help clinicians have a better access to the level of impairment after stroke. Previously, we conducted a method for quantifying the passive finger joint stiffness based on the pressure-angle relationship between the spastic fingers and the soft-elastic composite actuator (SECA). However, it lacks a ground-truth to demonstrate the compatibility between the SECA-facilitated stiffness estimation and standard joint stiffness quantification procedure. In this study, we compare the passive metacarpophalangeal (MCP) joint stiffness measured using the SECA with the results from our designed standalone mechatronics device, which measures the passive metacarpophalangeal joint torque and angle during passive finger rotation. Results obtained from the fitting model that concludes the stiffness characteristic are further compared with the results obtained from SECA-Finger model, as well as the clinical score of Modified Ashworth Scale (MAS) for grading spasticity. These findings suggest the possibility of passive MCP joint stiffness quantification using the soft robotic actuator during the performance of different tasks in hand rehabilitation.

What Kind of Brain Structural Connectivity Remodeling Can Relate to Residual Motor Function After Stroke?

Recent findings showed that brain networks far away from a lesion could be altered to adapt changes after stroke. This study examined 13 chronic stroke patients with moderate to severe motor impairment and 13 age-comparable healthy controls using diffusion tensor imaging to investigate the stroke impact on the reorganization of structural connectivity. Each subject's brain was segmented into 68 cortical and 12 subcortical regions of interest (ROIs), and connectivity measures including fractional anisotropy (FA), regional FA (rFA), connection weight (CW) and connection strength (CS) were adopted to compare two subject groups. Correlations between these measures and clinical scores of motor functions (Action Research Arm Test and Fugl-Meyer Assessment for upper extremity) were done. Network-based statistic (NBS) was conducted to identify the connectivity differences between patients and controls from the perspective of whole-brain network. The results showed that both rFAs and CSs demonstrated significant differences between patients and controls in the ipsilesional sensory-motor areas and subcortical network, and bilateral attention and default mode networks. Significant positive correlations were found between the paretic motor functions and the rFAs/CSs of the contralesional medial orbitofrontal cortex (mOFC) and rostral anterior cingulate cortex (rACC), and remained significant even after removing the effect of the ipsilesional corticospinal tract. Additionally, all the connections linked with the contralesional mOFC and rACC showed significantly higher FA/CW values in the stroke patients compared to the healthy controls from the NBS results. These findings indicated that these contralesional prefrontal areas exhibited stronger connections after stroke and strongly related to the residual motor function of the stroke patients.

Pathway-specific modulatory effects of neuromuscular electrical stimulation during pedaling in chronic stroke survivors.

BACKGROUND: Neuromuscular electrical stimulation (NMES) is extensively used in stroke motor rehabilitation. How it promotes motor recovery remains only partially understood. NMES could change muscular properties, produce altered sensory inputs, and modulate fluctuations of cortical activities; but the potential contribution from cortico-muscular couplings during NMES synchronized with dynamic movement has rarely been discussed. METHOD: We investigated cortico-muscular interactions during passive, active, and NMES rhythmic pedaling in healthy subjects and chronic stroke survivors. EEG (128 channels), EMG (4 unilateral lower limb muscles) and movement parameters were measured during 3 sessions of constant-speed pedaling. Sensory-level NMES (20 mA) was applied to the muscles, and cyclic stimulation patterns were synchronized with the EMG during pedaling cycles. Adaptive mixture independent component analysis was utilized to determine the movement-related electro-cortical sources and the source dipole clusters. A directed cortico-muscular coupling analysis was conducted between representative source clusters and the EMGs using generalized partial directed coherence (GPDC). The bidirectional GPDC was compared across muscles and pedaling sessions for post-stroke and healthy subjects. RESULTS: Directed cortico-muscular coupling of NMES cycling was more similar to that of active pedaling than to that of passive pedaling for the tested muscles. For healthy subjects, sensory-level NMES could modulate GPDC of both ascending and descending pathways. Whereas for stroke survivors, NMES could modulate GPDC of only the ascending pathways. CONCLUSIONS: By clarifying how NMES influences neuromuscular control during pedaling in healthy and post-stroke subjects, our results indicate the potential limitation of sensory-level NMES in promoting sensorimotor recovery in chronic stroke survivors.

Cortico-Muscular Coherence Modulated by High-Definition Transcranial Direct Current Stimulation in People With Chronic Stroke.

High-definition transcranial direct current stimulation (HD-tDCS) is a potential neuromodulation apparatus for stroke rehabilitation. However, its modulatory effects in stroke subjects is still not well understood. In this paper, the offline modulatory effects of HD-tDCS on the ipsilesional primary motor cortex were investigated by performing wrist isometric contraction tasks before and after HD-tDCS in eleven unilateral chronic stroke subjects using a synchronized HD-tDCS and electroencephalogram/electromyography measurement system. This paper is a randomized, single blinded, and sham-controlled crossover study. Each subject randomly received three HD-tDCS (anode, cathode, and sham) with at least one-week washout period. Online feedback-guided medium-level wrist isometric contraction tasks were conducted for the affected upper limbs before stimulation and 10, 30, and 50 min after the end of 10-min 1-mA HD-tDCS. The characteristics of corticomuscular coherence (CMC), cortical oscillation power spectral density, and power spectral entropy were analyzed during tasks and compared across all sessions and stimulation conditions. Anode HD-tDCS induced significant CMC changes in stroke subjects, while cathode and sham stimulation did not induce significant CMC changes. The largest neuromodulation effects were observed at 10 min immediately after anodal HD-tDCS.

Myoelectric Pattern Recognition for Controlling a Robotic Hand: A Feasibility Study in Stroke.

OBJECTIVE: Myoelectric pattern recognition has been successfully applied as a human-machine interface to control robotic devices such as prostheses and exoskeletons, significantly improving the dexterity of myoelectric control. This study investigates the feasibility of applying myoelectric pattern recognition for controlling a robotic hand in stroke patients. METHODS: Myoelectric pattern recognition of six hand motion patterns was performed using forearm electromyogram signals in paretic side of eight stroke subjects. Both the random cross validation (RCV) and the chronological handout validation (CHV) were applied to assess the offline myoelectric pattern recognition performance. Experiments on real-time myoelectric pattern recognition control of an exoskeleton robotic hand were also performed. RESULTS: An average classification accuracy of 84.1% (the mean value from two different classifiers) and individual subject differences were observed in the offline myoelectric pattern recognition analysis using the RCV, while the accuracy decreased to 65.7% when the CHV was used. The stroke subjects achieved an average accuracy of 61.3 ± 20.9% for controlling the robotic hand. However, our study did not reveal a clear correlation between the real-time control accuracy and the offline myoelectric pattern recognition performance, or any specific characteristics of the stroke subjects. CONCLUSION: The findings suggest that it is feasible to apply myoelectric pattern recognition to control the robotic hand in some but not all of the stroke patients. Each stroke subject should be individually online tested for the feasibility of applying myoelectric pattern recognition control for robot-assisted rehabilitation.

Low Gamma Band Cortico-muscular Coherence Inter-Hemisphere Difference following Chronic Stroke.

Brain oscillation and motor control process would change due to chronic stroke. Inter-hemisphere brain activation patterns may relate to motor related recovery. This study employed cortico-muscular coherence to explore cortical motor control process during wrist isometric contraction experiments of both affected and unaffected hands from chronic stroke subjects. Eleven chronic stroke subjects with moderate hand function involved in the experiments and each subject took three visits. Multitaper coherence estimation with bias-correction was performed to acquire cortico-muscular coherence, neuronal coherence source Localization was conducted to determine typical scalp motivation area during isometric contraction. Non-parametric permutation based multiple frequency bin statistics was utilized to compare the difference between two sides. The results demonstrated significant typical low gamma band inter-hemisphere disparity in cortico-muscular coherence between two sides after chronic stroke. The spatial topographical pattern and source Localization outcomes also supported these findings.

Real-time Electromyography-driven Functional Electrical Stimulation Cycling System for Chronic Stroke Rehabilitation.

Stroke-induced lower extremity dysfunction has become a severe medical problem nowadays and effective rehabilitation methods are in great demand. In this work, a new real-time Electromyography-driven Functional Electrical Stimulation (FES) cycling system was developed to help chronic stroke patients with lower limb rehabilitation training. To evaluate the feasibility and effectiveness of this system, 3 chronic stroke subjects were recruited and each received 20 training sessions where real-time Electromyography (EMG) was used to interact with the cycling system. During the training, two typical metrics, averaged Area Under Torque (AUT) and maximal EMG amplitude, were adopted to measure the muscle strength changes of hamstring (HS). The training results showed that the two measurements of HS both significantly increased, especially the maximal EMG amplitude in the last trial was twice as much as that in the first trial, indicating paretic limb strength increment and functional recovery, which suggested that our system is effective and helpful in the stroke rehabilitation.

Differentiated Effects of Robot Hand Training With and Without Neural Guidance on Neuroplasticity Patterns in Chronic Stroke.

Robot-assisted training combined with neural guided strategy has been increasingly applied to stroke rehabilitation. However, the induced neuroplasticity is seldom characterized. It is still uncertain whether this kind of guidance could enhance the long-term training effect for stroke motor recovery. This study was conducted to explore the clinical improvement and the neurological changes after 20-session guided or non-guided robot hand training using two measures: changes in brain discriminant ability between motor-imagery and resting states revealed from electroencephalography (EEG) signals and changes in brain network variability revealed from resting-state functional magnetic resonance imaging (fMRI) data in 24 chronic stroke subjects. The subjects were randomly assigned to receive either combined action observation (AO) with EEG-guided robot-hand training (RobotEEG_AO, n = 13) or robot-hand training without AO and EEG guidance (Robotnon-EEG_Text, n = 11). The robot hand in RobotEEG_AO group was activated only when significant mu suppression (8-12 Hz) was detected from subjects' EEG signals in ipsilesional hemisphere, while the robot hand in Robotnon-EEG_Text group was randomly activated regardless of their EEG signals. Paretic upper-limb motor functions were evaluated at three time-points: before, immediately after and 6 months after the interventions. Only RobotEEG_AO group showed a long-term significant improvement in their upper-limb motor functions while no significant and long-lasting training effect on the paretic motor functions was shown in Robotnon-EEG_Text group. Significant neuroplasticity changes were only observed in RobotEEG_AO group as well. The brain discriminant ability based on the ipsilesional EEG signals significantly improved after intervention. For brain network variability, the whole brain was first divided into six functional subnetworks, and significant increase in the temporal variability was found in four out of the six subnetworks, including sensory-motor areas, attention network, auditory network, and default mode network after intervention. Our results revealed the differences in the long-term training effect and the neuroplasticity changes following the two interventional strategies: with and without neural guidance. The findings might imply that sustainable motor function improvement could be achieved through proper neural guidance, which might provide insights into strategies for effective stroke rehabilitation. Furthermore, neuroplasticity could be promoted more profoundly by the intervention with proper neurofeedback, and might be shaped in relation to better motor skill acquisition.

Randomized controlled trial of robot-assisted gait training with dorsiflexion assistance on chronic stroke patients wearing ankle-foot-orthosis.

BACKGROUND: Robot-assisted ankle-foot-orthosis (AFO) can provide immediate powered ankle assistance in post-stroke gait training. Our research team has developed a novel lightweight portable robot-assisted AFO which is capable of detecting walking intentions using sensor feedback of wearer's gait pattern. This study aims to investigate the therapeutic effects of robot-assisted gait training with ankle dorsiflexion assistance. METHODS: This was a double-blinded randomized controlled trial. Nineteen chronic stroke patients with motor impairment at ankle participated in 20-session robot-assisted gait training for about five weeks, with 30-min over-ground walking and stair ambulation practices. Robot-assisted AFO either provided active powered ankle assistance during swing phase in Robotic Group (n = 9), or torque impedance at ankle joint as passive AFO in Sham Group (n = 10). Functional assessments were performed before and after the 20-session gait training with 3-month Follow-up. Primary outcome measure was gait independency assessed by Functional Ambulatory Category (FAC). Secondary outcome measures were clinical scores including Fugl-Meyer Assessment (FMA), Modified Ashworth Scale (MAS), Berg Balance Scale (BBS), Timed 10-Meter Walk Test (10MWT), Six-minute Walk Test (SMWT), supplemented by gait analysis. All outcome measures were performed in unassisted gait after patients had taken off the robot-assisted AFO. Repeated-measures analysis of covariance was conducted to test the group differences referenced to clinical scores before training. RESULTS: After 20-session robot-assisted gait training with ankle dorsiflexion assistance, the active ankle assistance in Robotic Group induced changes in gait pattern with improved gait independency (all patients FAC ≥ 5 post-training and 3-month follow-up), motor recovery, walking speed, and greater confidence in affected side loading response (vertical ground reaction force + 1.49 N/kg, peak braking force + 0.24 N/kg) with heel strike instead of flat foot touch-down at initial contact (foot tilting + 1.91°). Sham Group reported reduction in affected leg range of motion (ankle dorsiflexion - 2.36° and knee flexion - 8.48°) during swing. CONCLUSIONS: Robot-assisted gait training with ankle dorsiflexion assistance could improve gait independency and help stroke patients developing confidence in weight acceptance, but future development of robot-assisted AFO should consider more lightweight and custom-fit design. TRIAL REGISTRATION: ClinicalTrials.gov NCT02471248 . Registered 15 June 2015 retrospectively registered.

Design of an exoskeleton ankle robot for robot-assisted gait training of stroke patients.

Lower Limb Exoskeleton robot that can facilitate stair walking is a big challenge, most systems could only provide level ground walking. In this study, a lightweight (0.5kg at ankle, 0.5kg at waist for control box) and autonomous exoskeleton Ankle Robot was proposed to provide power assistance for gait training of chronic stroke patients and it can facilitate three walking conditions in real-time: (1) level walking, (2) stair ascending, and (3) stair descending. Chronic stroke patients (n=3) with drop foot gait deficit and moderate motor impairment were recruited to evaluate the system under different walking conditions (Functional Ambulatory Category: FAC=4.7±0.5 and Fugl-Meyer Assessment for lower-extremity: FMA-LE=13.7±2.9). The system consisted of a specially designed carbon fiber AFO, servomotor, gear transmission system, IMU and force sensors, and control box. The IMU sensors embedded in the shank measured acceleration and angular velocity to identify distinct features in leg tilting angle and leg angular velocity between the three walking conditions. The results showed the powered ankle dorsiflexion assistance could reduce dropped foot of the stroke patients in swing phase and provide better gait pattern. A demo of the ankle robot will be conducted in the conference.