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.

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.

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.

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.

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.

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.

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.

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 (Robot_{EEG_AO}, n = 13) or robot-hand training without AO and EEG guidance (Robot_{non−EEG_Text}, n = 11). The robot hand in Robot_{EEG_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 Robot_{non−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 Robot_{EEG_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 Robot_{non−EEG_Text} group. Significant neuroplasticity changes were only observed in Robot_{EEG_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.

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.

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.

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.

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.

Advanced Myoelectric Control for Robotic Hand-Assisted Training: Outcome from a Stroke Patient

A hand exoskeleton driven by myoelectric pattern recognition was designed for stroke rehabilitation. It detects and recognizes the user’s motion intent based on electromyography (EMG) signals, and then helps the user to accomplish hand motions in real time. The hand exoskeleton can perform six kinds of motions, including the whole hand closing/opening, tripod pinch/opening, and the “gun” sign/opening. A 52-year-old woman, 8 months after stroke, made 20× 2-h visits over 10 weeks to participate in robot-assisted hand training. Though she was unable to move her fingers on her right hand before the training, EMG activities could be detected on her right forearm. In each visit, she took 4× 10-min robot-assisted training sessions, in which she repeated the aforementioned six motion patterns assisted by our intent-driven hand exoskeleton. After the training, her grip force increased from 1.5 to 2.7 kg, her pinch force increased from 1.5 to 2.5 kg, her score of Box and Block test increased from 3 to 7, her score of Fugl–Meyer (Part C) increased from 0 to 7, and her hand function increased from Stage 1 to Stage 2 in Chedoke–McMaster assessment. The results demonstrate the feasibility of robot-assisted training driven by myoelectric pattern recognition after stroke.

Evaluation of the Microsoft Kinect as a clinical assessment tool of body sway

Total body center of mass (TBCM) is a useful kinematic measurement of body sway. However, expensive equipment and high technical requirement limit the use of motion capture systems in large-scale clinical settings. Center of pressure (CP) measurement obtained from force plates cannot accurately represent TBCM during large body sway movement. Microsoft Kinect is a rapidly developing, inexpensive, and portable posturographic device, which provides objective and quantitative measurement of TBCM sway. The purpose of this study was to evaluate Kinect as a clinical assessment tool for TBCM sway measurement. The performance of the Kinect system was compared with a Vicon motion capture system and a force plate. Ten healthy male subjects performed four upright quiet standing tasks: (1) eyes open (EOn), (2) eyes closed (ECn), (3) eyes open standing on foam (EOf), and (4) eyes closed standing on foam (ECf). Our results revealed that the Kinect system produced highly correlated measurement of TBCM sway (mean RMSE=4.38 mm; mean CORR=0.94 in Kinect-Vicon comparison), as well as comparable intra-session reliability to Vicon. However, the Kinect device consistently overestimated the 95% CL of sway by about 3mm. This offset could be due to the limited accuracy, resolution, and sensitivity of the Kinect sensors. The Kinect device was more accurate in the medial-lateral than in the anterior-posterior direction, and performed better than the force plate in more challenging balance tasks, such as (ECf) with larger TBCM sway. Overall, Kinect is a cost-effective alternative to a motion capture and force plate system for clinical assessment of TBCM sway.

The effects of post-stroke upper-limb training with an electromyography (EMG)-driven hand robot

Loss of hand function and finger dexterity are main disabilities in the upper limb after stroke. An electromyography (EMG)-driven hand robot had been developed for post-stroke rehabilitation training. The effectiveness of the hand robot assisted whole upper limb training was investigated on persons with chronic stroke (n=10) in this work. All subjects attended a 20-session training (3-5times/week) by using the hand robot to practice object grasp/release and arm transportation tasks. Significant motor improvements were observed in the Fugl-Meyer hand/wrist and shoulder/elbow scores (p<0.05), and also in the Action Research Arm Test and Wolf Motor Function Test (p<0.05). Significant reduction in spasticity of the fingers as was measured by the Modified Ashworth Score (p<0.05). The training improved the muscle co-ordination between the antagonist muscle pair (flexor digitorum (FD) and extensor digitorum (ED)), associated with a significant reduction in the ED EMG level (p<0.05) and a significant decrease of ED and FD co-contraction during the training (p<0.05); the excessive muscle activities in the biceps brachii were also reduced significantly after the training (p<0.05).

Neural correlates of motor impairment during motor imagery and motor execution in sub-cortical stroke

OBJECTIVE

This study aims at identifying the neural substrates for motor execution (ME) and motor imagery (MI) in patients after stroke and their correlations with functional outcomes.

METHODS

10 chronic stroke patients with left sub-cortical lesions and 10 unimpaired subjects were recruited. Their cortical processes were studied when they were asked to perform ME and MI unimanually using their unaffected and affected wrists during fMRI.

RESULTS

From correlation results, the supplementary motor area (SMA), its activation volume and congruence in functional neuroanatomy associated with ME and MI using affected wrist positively correlated with motor performance. During ME of the affected wrist, the precuneus, its activation volume and congruence in functional neuroanatomy between patient and unimpaired groups showed a negative correlation, while, in non-primary motor areas, the hemispheric balance of premotor cortex and the congruence in functional neuroanatomy of contralesional inferior parietal lobule between patient and unimpaired groups showed a positive correlation with motor performance.

CONCLUSIONS

The non-primary motor-related areas were revealed to play a critical role in determining motor outcomes after left sub-cortical stroke, which was demonstrated in the stroke patients. In particular, SMA might be the key neural substrate associated with motor recovery.

Coordinated upper limb training assisted with an electromyography (EMG)-driven hand robot after stroke

An electromyography (EMG)-driven hand robot had been developed for post-stroke rehabilitation training. The effectiveness of the hand robot assisted whole upper limb training on muscular coordination was investigated on persons with chronic stroke (n=10) in this work. All subjects attended a 20-session training (3-5 times/week) by using the hand robot to practice object grasp/release and arm transportation tasks. Improvements were found in the muscle co-ordination between the antagonist muscle pair (flexor digitorum and extensor digitorum) as measured by muscle co-contractions in EMG signals; and also in the reduction of excessive muscle activities in the biceps brachii. Reduced spasticity in the fingers was also observed as measured by the Modified Ashworth Score.

Post-stroke wrist rehabilitation assisted with an intention-driven functional electrical stimulation (FES)-robot system

In this work, a novel FES-robot system was developed for wrist rehabilitation training after stroke. The FES-robot system could be continuously controlled by electromyography (EMG) from the residual wrist muscles to facilitate wrist flexion and extension tracking tasks on a horizontal plane by providing assistance from both FES and robot parts. The system performance with five different assistive combinations from the FES and robot parts was evaluated by subjects with chronic stroke (n=5). The results suggested that the assistance from the robot part mainly improved the movement accuracy in the tracking tasks; and the assistance from the FES part mainly suppressed the excessive muscular activities from the elbow joint. The best combination was when the assistances from FES and robot was 1:1, and the results showed better wrist tracking performance with less muscle co-contraction from the elbow joint.

An EMG-driven exoskeleton hand robotic training device on chronic stroke subjects: task training system for stroke rehabilitation

An exoskeleton hand robotic training device is specially designed for persons after stroke to provide training on their impaired hand by using an exoskeleton robotic hand which is actively driven by their own muscle signals. It detects the stroke person's intention using his/her surface electromyography (EMG) signals from the hemiplegic side and assists in hand opening or hand closing functional tasks. The robotic system is made up of an embedded controller and a robotic hand module which can be adjusted to fit for different finger length. Eight chronic stroke subjects had been recruited to evaluate the effects of this device. The preliminary results showed significant improvement in hand functions (ARAT) and upper limb functions (FMA) after 20 sessions of robot-assisted hand functions task training. With the use of this light and portable robotic device, stroke patients can now practice more easily for the opening and closing of their hands at their own will, and handle functional daily living tasks at ease. A video is included together with this paper to give a demonstration of the hand robotic system on chronic stroke subjects and it will be presented in the conference.

The effects of voluntary, involuntary, and forced exercises on motor recovery in a stroke rat model

Stroke rehabilitation with different exercise paradigms has been investigated, but a comparison study on motor recovery after voluntary, involuntary, and forced exercises is limited. The current study used a rat brain ischemia model to investigate the effects of voluntary wheel running, involuntary muscle movement caused by functional electrical stimulation (FES), and forced treadmill exercise on motor recovery and brain BDNF changes. The results showed that voluntary exercise is the most effective intervention in upregulating the hippocampal BDNF level, and facilitating motor recovery after brain ischemia.

The effects of electromechanical wrist robot assistive system with neuromuscular electrical stimulation for stroke rehabilitation

An electromyography (EMG)-driven electromechanical robot system integrated with neuromuscular electrical stimulation (NMES) was developed for wrist training after stroke. The performance of the system in assisting wrist flexion/extension tracking was evaluated on five chronic stroke subjects, when the system provided five different schemes with or without NMES and robot assistance. The tracking performances were measured by range of motion (ROM) of the wrist and root mean squared error (RMSE). The performance is better when both NMES and robot assisted in the tracking than those with either NMES or robot only (P<0.05). The muscle co-contractions in the upper limb measured by EMG were reduced when NMES provided assistance (P<0.05). All subjects also attended a 20-session wrist training for evaluating the training effects (3-5 times/week). The results showed improvements on the voluntary motor functions in the hand, wrist and elbow functions after the training, as indicated by the clinical scores of Fugl-Meyer Assessment, Action Research Arm Test, Wolf Motor Function Test; and also showed reduced spasticity in the wrist and the elbow as measured by the Modified Ashworth Score of each subject. After the training, the co-contractions were reduced between the flexor carpi radialis and extensor carpi radialis, and between the biceps brachii and triceps brachii. Assistance from the robot helped improve the movement accuracy; and the NMES helped increase the muscle activation for the wrist joint and suppress the excessive muscular activities from the elbow joint. The NMES-robot assisted wrist training could improve the hand, wrist, and elbow functions.

Effectiveness of functional electrical stimulation (FES)-robot assisted wrist training on persons after stroke

A functional electrical stimulation (FES)-robot system controlled by subjects' motor intention was developed in our previous study. The effectiveness of the FES-robot on wrist training was investigated in this work. Five hemiplegic subjects with chronic stroke were recruited for an FES-robot assisted wrist training with 20 sessions. After the training, motor improvements were found in the wrist and fingers, represented by significant increase (P < 0.05) in clinical scores of the Fugl-Meyer Assessment (FMA), the Action Research Arm Test (ARAT), and the Modified Ashworth Score (MAS). Muscle coordination in the upper limb was also improved during the training as assessed by electromyography. The increased ARAT scores suggested improved upper limb motor functions, especially in the hand and fingers, compared to no improvement in previous study with only interactive robot-assisted wrist training without FES.

The effects of voluntary, involuntary, and forced exercises on brain-derived neurotrophic factor and motor function recovery: a rat brain ischemia model

Background

Stroke rehabilitation with different exercise paradigms has been investigated, but which one is more effective in facilitating motor recovery and up-regulating brain neurotrophic factor (BDNF) after brain ischemia would be interesting to clinicians and patients. Voluntary exercise, forced exercise, and involuntary muscle movement caused by functional electrical stimulation (FES) have been individually demonstrated effective as stroke rehabilitation intervention. The aim of this study was to investigate the effects of these three common interventions on brain BDNF changes and motor recovery levels using a rat ischemic stroke model.

Methodology/Principal Findings

One hundred and seventeen Sprague-Dawley rats were randomly distributed into four groups: Control (Con), Voluntary exercise of wheel running (V-Ex), Forced exercise of treadmill running (F-Ex), and Involuntary exercise of FES (I-Ex) with implanted electrodes placed in two hind limb muscles on the affected side to mimic gait-like walking pattern during stimulation. Ischemic stroke was induced in all rats with the middle cerebral artery occlusion/reperfusion model and fifty-seven rats had motor deficits after stroke. Twenty-four hours after reperfusion, rats were arranged to their intervention programs. De Ryck's behavioral test was conducted daily during the 7-day intervention as an evaluation tool of motor recovery. Serum corticosterone concentration and BDNF levels in the hippocampus, striatum, and cortex were measured after the rats were sacrificed. V-Ex had significantly better motor recovery in the behavioral test. V-Ex also had significantly higher hippocampal BDNF concentration than F-Ex and Con. F-Ex had significantly higher serum corticosterone level than other groups.

Conclusion/Significance

Voluntary exercise is the most effective intervention in upregulating the hippocampal BDNF level, and facilitating motor recovery. Rats that exercised voluntarily also showed less corticosterone stress response than other groups. The results also suggested that the forced exercise group was the least preferred intervention with high stress, low brain BDNF levels and less motor recovery.

Performance of common spatial pattern under a smaller set of EEG electrodes in brain-computer interface on chronic stroke patients: a multi-session dataset study

Brain-computer interface (BCI) uses non-muscular channel of the nervous system for communication. Common Spatial Pattern (CSP) is a popular spatial filtering method used to reduce the effect of volume conduction on EEG signals. It is thought that CSP requires a large number of electrodes to be effective. Using a 20-session dataset of motor imagery BCI usage by 5 stroke patients, we demonstrated that after channel selection, CSP can still maintain a high accuracy with low number of electrodes using a newly proposed channel selection method called CSP-rank (higher than 90% with 8 electrodes). The results showed that using only the first session for channel selection, a high accuracy can be maintained in subsequent sessions. CSP-rank has been compared to the popular support vector machine recursive feature elimination (SVM-RFE). The results showed that the CSP-rank required less electrodes to maintain accuracy higher than 90% (a minimum of 8 compared to 12 of SVM-RFE) and it attained a higher maximum accuracy (91.7% compared with 90.7% of SVM-RFE). This could support clinicians to apply more BCI in routine rehabilitation.

Combined functional electrical stimulation (FES) and robotic system for wrist rehabiliation after stroke

Functional electrical stimulation (FES) and rehabilitation robots are techniques used to assist in post-stroke rehabilitation. However, FES and rehabilitation robots are still separate systems currently; and their combined training effects on persons after experiencing a stroke have not been well studied yet. In this work, a new combined FES-robot system driven by user's voluntary intention was developed for wrist joint training after stroke. The performance of the FES-robot assisted wrist tracking was evaluated on five subjects with chronic stroke. With simultaneous assistance from both the FES and robot parts of the system, the motion accuracy was improved and excessive activation in elbow flexor was reduced during wrist tracking.

An intention driven hand functions task training robotic system

A novel design of a hand functions task training robotic system was developed for the stroke rehabilitation. It detects the intention of hand opening or hand closing from the stroke person using the electromyography (EMG) signals measured from the hemiplegic side. This training system consists of an embedded controller and a robotic hand module. Each hand robot has 5 individual finger assemblies capable to drive 2 degrees of freedom (DOFs) of each finger at the same time. Powered by the linear actuator, the finger assembly achieves 55 degree range of motion (ROM) at the metacarpophalangeal (MCP) joint and 65 degree range of motion (ROM) at the proximal interphalangeal (PIP) joint. Each finger assembly can also be adjusted to fit for different finger length. With this task training system, stroke subject can open and close their impaired hand using their own intention to carry out some of the daily living tasks.

Incorporating ultrasound-measured musculotendon parameters to subject-specific EMG-driven model to simulate voluntary elbow flexion for persons after stroke

BACKGROUND

This study was to extend previous neuromusculoskeletal modeling efforts through combining the in vivo ultrasound-measured musculotendon parameters on persons after stroke.

METHOD

A subject-specific neuromusculoskeletal model of the elbow was developed to predict the individual muscle force during dynamic movement and then validated by joint trajectory. The model combined a geometrical model and a Hill-type musculotendon model, and used subject-specific musculotendon parameters as inputs. EMG signals and joint angle were recorded from healthy control subjects (n=4) and persons after stroke (n=4) during voluntary elbow flexion in a vertical plane. Ultrasonography was employed to measure the muscle optimal length and pennation angle of each prime elbow flexor (biceps brachii, brachialis, brachioradialis) and extensor (three heads of triceps brachii). Maximum isometric muscle stresses of the flexor and extensor muscle group were calibrated by minimizing the root mean square difference between the predicted and measured maximum isometric torque-angle curves. These parameters were then inputted into the neuromusculoskeletal model to predict the individual muscle force using the input of EMG signals directly without any trajectory fitting procedure involved.

FINDINGS

The results showed that the prediction of voluntary flexion in the hemiparetic group using subject-specific parameters data was better than that using cadaveric data extracted from the literature.

INTERPRETATION

The results demonstrated the feasibility of using EMG-driven neuromusculoskeletal modeling with direct ultrasound measurement for the prediction of voluntary elbow movement for both subjects without impairment and persons after stroke.

Quantitative evaluation of motor functional recovery process in chronic stroke patients during robot-assisted wrist training

This study was to investigate the motor functional recovery process in chronic stroke during robot-assisted wrist training. Fifteen subjects with chronic upper extremity paresis after stroke attended a 20-session wrist tracking training using an interactive rehabilitation robot. Electromyographic (EMG) parameters, i.e., EMG activation levels of four muscles: biceps brachii (BIC), triceps brachii (TRI, lateral head), flexor carpiradialis (FCR), and extensor carpiradialis (ECR) and their co-contraction indexes (CI) were used to monitor the neuromuscular changes during the training course. The EMG activation levels of the FCR (11.1% of decrease from the initial), BIC (17.1% of decrease from the initial), and ECR (29.4% of decrease from the initial) muscles decreased significantly during the training (P<0.05). Such decrease was associated with decreased Modified Ashworth Scores for both the wrist and elbow joints (P<0.05). Significant decrease (P<0.05) was also found in CIs of muscle pairs, BIC&TRI (21% of decrease from the initial), FCR&BIC (11.3% of decrease from the initial), ECR&BIC (49.3% of decrease from the initial). The decreased CIs related to the BIC muscle were mainly caused by the reduction in the BIC EMG activation level, suggesting a better isolation of the wrist movements from the elbow motions. The decreased CI of ECR& FCR in the later training sessions (P<0.05) was due to the reduced co-contraction phase of the antagonist muscle pair in the tracking tasks. Significant improvements (P<0.05) were also found in motor outcomes related to the shoulder/elbow and wrist/hand scores assessed by the Fugl-Meyer assessment before and after the training. According to the evolution of the EMG parameters along the training course, further motor improvements could be obtained by providing more training sessions, since the decreases of the EMG parameters did not reach a steady state before the end of the training. The results in this study provided an objective and quantitative EMG measure to describe the motor recovery process during poststroke robot-assisted wrist for the further understanding on the neuromuscular mechanism associated with the recovery.

The therapeutic effects of myoelectrically controlled robotic system for persons after stroke--a pilot study

In this study, a myoelectrically controlled robotic system with one degree of freedom was developed to assist elbow training in the horizontal plane for patients after stroke. The system could provide assistive extension torque which was proportional to the amplitude of the subject's processed and normalized electromyograhpic (EMG) signal from triceps. The system also provided different resistive torques during movement, which were based on the maximum isometric voluntary extension (MIVE) and flexion (MIVF) torques. A study investigated its effect after 20-session of training for four weeks on the functional improvement of the affected arm in 3 subjects after stroke. Outcome measurements on the muscle strength at the elbow joint showed that there were increases in the MIVE and MIVF torques of the affected arms of all the subjects after the four-week rehabilitation training. The subjects could also reach a more extended position without the assistance of the robotic system than that before the four-week training.

Coactivations of elbow and shoulder muscles in hemiplegic persons with chronic stroke during robot-assisted training

The motor recovery procedure of chronic stroke during robot-assisted training has not been well studied previously. In this work, we analyzed the variations in the coactivating patterns of elbow and shoulder muscles (biceps, triceps lateral, anterior deltoid, and posterior deltoid) in hemiplegic persons with chronic stroke (n=4) during a 20-session's interactive robot-assisted treatment. Significant decreases in muscle cocontractions (P<0.05) for all muscle pairs started from the 8th session of the training. Improvements were also observed in motor scores of Fugl-Meyer and modified Ashworth scale after the treatment. The results suggested an increased dexterity and selective control on individual muscles for both elbow and shoulder joints in a designed task after the robot-assisted training.

Firing properties of motor units during fatigue in subjects after stroke

The purpose of this work was to investigate the electromyographic (EMG) fatigue representations in muscles of subjects after stroke at the level of motor unit, based on the analysis of mean power frequency (MPF) in the power density spectrum (PDS) for intramuscular EMG and our previous modeling and experiment studies on the neuromuscular transmission failure (NTF). NTF due to the local muscular fatigue had been captured in motor unit signals from healthy subjects during a submaximal fatigue contraction previously. In this study, the EMG signals for the biceps brachii muscles were collected by needle electrodes from the affected and unaffected arms of six hemiplegic subjects after stroke, and from the dominated arm of six healthy subjects during a full maximum voluntary contraction (MVC) and a subsequent 20% MVC. The MPF of EMG trials detected intramuscularly during the full and 20% MVCs, and the parameters of motor unit action potential trains (MUAPTs) during 20% MVC were analyzed in three groups: the normal (from healthy subjects), unaffected (from subjects after stroke), and affected (from subjects after stroke). It was found that during the full MVC the MPFs of the normal and unaffected groups decreased more than the affected when monitored by a moving time window of 2 s. The comparison on the overall MPF during the full MVC for these three groups over the whole time course of the EMG signal (18 s) were: the affected overall MPF was higher than the unaffected (P < 0.05); and the unaffected overall MPF was larger than the normal (P < 0.05). However, no significant decrease in MPF was found for these three groups during 20% MVC. The NTF was captured in most MUAPTs in the groups of the normal and unaffected rather than in the affected group, symbolized by the lowered rates of change (RCs) of firing rate (FR) (P < 0.05), more MUAPTs with positive RCs of maximum oscillation (MO) in MUAPT power density spectra (P < 0.05), and the significant higher RCs of minimum inter-pulse interval (MINI) (P < 0.05) in the normal and unaffected compared to the affected group. Enhanced neural drives to the motor units of the unaffected and affected groups were observed during 20% MVC, which possibly came from the bilateral neural inputs due to the disinhibition of the ipsilateral projections in subjects after stroke. For identifying the fatigue associated with NTF, the motor unit firing parameters, FR, MINI, and MO, were more sensitive than the MPF. The results obtained in this work provided a further understanding on the EMG of the fatigue processes in paretic and non-paretic muscles during voluntary contractions.

Using recurrent artificial neural network model to estimate voluntary elbow torque in dynamic situations

Muscle modelling is an important component of body segmental motion analysis. Although many studies had focused on static conditions, the relationship between electromyographic (EMG) signals and joint torque under voluntary dynamic situations has not been well investigated. The aim of this study was to investigate the performance of a recurrent artificial neural network (RANN) under voluntary dynamic situations for torque estimation of the elbow complex. EMG signals together with kinematic data, which included angle and angular velocity, were used as the inputs to estimate the expected torque during movement. Moreover, the roles of angle and angular velocity in the accuracy of prediction were investigated, and two models were compared. One model used EMG and joint kinematic inputs and the other model used only EMG inputs without kinematic data. Six healthy subjects were recruited, and two average angular velocities (60 degrees s(-1) and 90 degrees s(-1)) with three different loads (0 kg, 1 kg, 2 kg) in the hand position were selected to train and test the RANN between 90 degrees elbow flexion and full elbow extension (0 degrees). After training, the root mean squared error (RMSE) between expected torque and predicted torque of the model, with EMG and joint kinematic inputs in the training data set and the test data set, were 0.17 +/- 0.03 Nm and 0.35 +/- 0.06 Nm, respectively. The RMSE values between expected torque and predicted torque of the model, with only EMG inputs in the training data set and the test set, were 0.57 +/- 0.07 Nm and 0.73 +/- 0.11 Nm, respectively. The results showed that EMG signals together with kinematic data gave significantly better performance in the joint torque prediction; joint angle and angular velocity provided important information in the estimation of joint torque in voluntary dynamic movement.

The mechanomyography of persons after stroke during isometric voluntary contractions

This study was to investigate the properties of mechanomyography (MMG), or muscle sound, of the paretic muscle in the affected side of hemiplegic subjects after stroke during isometric voluntary contractions, in comparison with those from the muscle in the unaffected side of the hemiplegic subjects and from the healthy muscle of unimpaired subjects. MMG and electromyography (EMG) signals were recorded simultaneously from the biceps brachii muscles of the dominant arm of unimpaired subjects (n=5) and the unaffected and affected arms of subjects after stroke (n=8), when performing a fatiguing maximal voluntary contraction (MVC) associated with the decrease in elbow flexion torque, and then submaximal elbow flexions at 20%, 40%, 60% and 80% MVCs. The root mean squared (RMS) values, the mean power frequencies (MPF, in the power density spectrum, PDS) of the EMG and MMG, and the high frequency rate (HF-rate, the ratio of the power above 15Hz in the MMG PDS) were used for the analysis. The MMG RMS decreased more slowly during the MVC in the affected muscle compared to the healthy and unaffected muscles. A transient increase could be observed in the MMG MPFs from the unaffected and healthy muscles during the MVC, associated with the decrease in their simultaneous EMG MPFs due to the muscular fatigue. No significant variation could be seen in the EMG and MMG MPFs in the affected muscles during the MVC. The values in the MPF and HF-rate of MMG from the affected muscles were significantly lower than those from the healthy and unaffected muscles (P<0.05) at the high contraction level (80% MVC). Both the MMG and EMG RMS values in the healthy and unaffected groups were found to be significantly higher than the affected group (P<0.05) at 60% and 80% MVCs. These observations were related to an atrophy of the fast-twitch fibers and a reduction of the neural input in the affected muscles of the hemiplegic subjects. The results in this study suggested MMG could be used as a complementary to EMG for the analysis on muscular characteristics in subjects after stroke.

Neurochemical effects of exercise and neuromuscular electrical stimulation on brain after stroke: a microdialysis study using rat model

Treadmill exercise and neuromuscular electrical stimulation are common clinical approaches for stroke rehabilitation. Both animal and clinical studies have shown the functional improvements after these interventions. However, the neurochemical effects on the ischemic brain had not been well studied. This study aimed at evaluating the effects of treadmill exercise and neuromuscular electrical stimulation (NMES), and studying their effects during a 2-week training, on the levels of common neurotransmitters (aspartate, glutamate, taurine and gamma-aminobutyric acid (GABA)) in the hippocampus following transient focal cerebral ischemia. Either treadmill exercise or neuromuscular electrical stimulation was prescribed to the rats 24 h after cerebral ischemia whereas Control group remained in cages for 2 weeks. Microdialysis technique was used to collect dialysates from ipsilesional hippocampus in vivo. It was found that the glutamate level was increased significantly during treadmill exercise and then returned to baseline level. Both interventions did not trigger significant effects on aspartate and glutamate basal levels during the 2 weeks. The relatively high taurine level in Control groups may suggest that the interventions might suppress the taurine release in hippocampus. GABA and aspartate levels did not showed significant changes over the 2 weeks in all groups. These results provide insights to explain the neurochemical effects on the ischemic injured brain during the course of rehabilitation.

Oscillations in the power spectra of motor unit signals caused by refractoriness variations

The refractory period of a motor unit is an important mechanism that regulates the motor unit firing, and its variation has been found in many physiological cases. In this study, a new observation that an increase in the motor unit refractoriness results in an enhancement of oscillations, or ripple effects, in the motor unit output power density spectra (PDS) has been identified and studied. The effects of the refractoriness variation on the PDS of motor unit firing were investigated on three levels: theoretical modeling, simulation and electromyographic (EMG) experimentation on human subjects. Both theoretical modeling and simulation showed the enhanced oscillations, ripple effects, in MUAPT PDS, given the increase in the refractoriness. It was also found that the extent of the increment in output PDS oscillation could be related to the motor unit size and the mean firing rate of the stimulation. A needle EMG experiment on biceps brachii muscles of five healthy human subjects was carried out during isometric contraction at 20% maximum voluntary contraction (MVC) for 20 s with a fatigue effort proceeded by MVC. The increased oscillations in the PDS of the real MUAPTs were observed with the rising of the motor unit refractoriness due to fatigue. The study gives new information for EMG spectra interpretation, and also provides a potential method for accessing neuromuscular transmission failure (NTF) due to fatigue during voluntary contraction.

Development of a circuit for functional electrical stimulation

This paper examines the various design of a multiple-purpose portable functional electrical stimulator which is used in surface stimulation of paralyzed muscle of patients with stroke and results in limb activation. The functionality, circuit performance and reliability of the circuits will be examined. Analysis, design, and experimental results are presented.

Command control for functional electrical stimulation hand grasp systems using miniature accelerometers and gyroscopes

Recent commercially available miniature sensors have the potential to improve the functions of functional electrical stimulation (FES) systems in terms of control, reliability and robustness. A new control approach using a miniature gyroscope and an accelerometer was studied. These sensors were used to detect the linear acceleration and angular velocity of residual voluntary movements on upper limbs and were small and easy to put on. Five healthy subjects and three cervical spinal cord injured subjects were recruited to evaluate this controller. Sensors were placed on four locations: the shoulder, upper arm, wrist and hand. A quick forward-and-backward movement was employed to produce a distinctive waveform that was different from general movements. A detection algorithm was developed to generate a command signal by identifying this distinctive waveform through the detection of peaks and valleys in the sensor's signals. This command signal was used to control different FES hand grasp patterns. With a specificity of 0.9, the sensors had a success rate of 85-100% on healthy subjects and 82-97% on spinal cord injured subjects. In terms of sensor placement, the gyroscope was better as a control source than the accelerometer for wrist and hand positions, but the reverse was true for the shoulder.

[Direct determination of selenium and bismuth in antimony and antimony compound by hydride generation-atomic fluorescence spectrometry]

The interference of Sb in determination of Se and Bi in antimony powder and antimony compound has been studied in different acidity, it is found that the interference of antimony has been reduced apparently in high acidity. Simultaneously, according to the difference of hydride generation reaction between Sb5+ and Sb3+, a sensitive and rapid method has been developed and used to determine Se and Bi in real samples. The recovery of the method is 95%-105% for practical samples. Detection limits are 0.00004 x 10(-2) (content) for Se and 0.0001 x 10(-2) mg.L-1 (content) for Bi respectively. The relative standard deviations of Se are 2.4% (content = 0.00169 x 10(-2) mg.L-1) and 5.4% (content = 0.00056 x 10(-2) mg.L-1). The relative standard deviations of Bi are 5.0% (content = 0.00024 x 10(-2) mg.L-1) and 1.3% (content = 0.00229 x 10(-2) mg.L-1). The method has been applied to determination of Se and Bi in practical samples with satisfactory results.

Development of computer-based environment for simulating the voluntary upper-limb movements of persons with disability

Upper-limb orthotic systems have been designed for restoring the upper-limb functions of individuals with disabilities resulting from spinal cord injury (SCI), stroke and muscular dystrophy. These systems employ either functional electrical stimulation or external power. It is proposed that, instead of time-consuming and complicated monitoring using sensors and motion analysis, a software simulator with both angular displacement and acceleration parameters can facilitate the design of a control strategy for an orthosis. Reaching movements of three cervical SCI subjects are used to verify the simulator. A motion analysis system is used to measure the range of motion and joint angles during hand reaching. Results indicate that quaternion and spline curve techniques are suitable for interpolation of the hand reaching movements. The information needed for good simulation only compress the shoulder and elbow joint angles in a few key postures. Stimulated acceleration signals on the upper-arm segment have a high correlation coefficient (> 0.9) and a small root mean squared error (< 0.11 g) with a real bi-axial accelerometer.

Reliability of neural-network functional electrical stimulation gait-control system

Functional electrical stimulation (FES) has been used for restoring walking in spinal-cord injured (SCI) persons. Using artificial intelligence (AI), FES controllers have been developed that allow the automatic phasing of stimulation, to replace the function of hand or heel switches. However, there has been no study to evaluate the reliability of these AI systems. Neural networks were used to construct FES controllers to control the timing of stimulation. Different numbers of sensors in the sensor set and different numbers of data points from each sensor were used. Two incomplete-SCI subjects were recruited, and each was tested on three separate occasions. The results show the neural-network controllers can maintain a high accuracy (around 90% for the two- and three-sensor groups and 80% for the one-sensor group) over a period of six months. Two or three sensors were sufficient to provide enough information to construct a reliable FES control system, and the number of data points did not have any effect on the reliability of the system.

Gait control system for functional electrical stimulation using neural networks

In functional electrical stimulation (FES) systems for restoring walking in spinal cord injured (SCI) individuals, hand switches are the preferred method for controlling stimulation timing. Through practice the user becomes an 'expert' in determining when stimulation should be applied. Neural networks have been used to 'clone' this expertise but these applications have used small numbers of sensors, and their structure has used a binary output, giving rise to possible controller oscillations. It was proposed that a three-layer structure neural network with continuous function, using a larger number of sensors, including 'virtual' sensors, can be used to 'clone' this expertise to produce good controllers. Using a sensor set of ten force sensors and another of 13 'virtual' kinematic sensors, a good FES control system was constructed using a three-layer neural network with five hidden nodes. The sensor set comprising three sensors showed the best performance. The accuracy of the optimum three-sensor set for the force sensors and the virtual kinematic sensors was 90% and 93%, respectively, compared with 81% and 77% for a heel switch. With 32 synchronised sensors, binary neural networks and continuous neural networks were constructed and compared. The networks using continuous function had significantly fewer oscillations. Continuous neural networks offer the ability to generate good FES controllers.

Virtual artificial sensor technique for functional electrical stimulation

In the control of Functional Electrical Stimulation (FES) gait systems artificial sensors are used to provide the controller with feedback information. The sensors used range in complexity from simple heel or hand switches to tri-axial accelerometers. There are three basic problems connected with the selection of sensors: the type(s) of sensor(s) to be used, the number of sensors required and the optimum location of the sensor set. In general the choice of the sensor sets has been based on the availability of actual sensors and the experts understanding of where these sensors should be located. Using motion analysis data it is possible to construct an almost unlimited number of virtual sensors on any location of the body surface. Our aim was to develop this technique for construction of virtual sensors and compare these virtual sensors with their physical counterparts. Virtual goniometers, inclinometers, accelerometers and foot switches were constructed and compared with their physical counterparts. In addition visualisation tools were developed to aid in the choice of sensor location. There was a very good correlation between all the virtual and physical sensors. This technique gives flexibility to place virtual sensors almost anywhere on the body surface and also allows the construction of novel sensors.