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.

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).

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

[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.

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.