Vicky Chan

Optimizing Compliant, Model-Based Robotic Assistance to Promote Neurorehabilitation

Based on evidence from recent experiments in motor learning and neurorehabilitation, we hypothesize that three desirable features for a controller for robot-aided movement training following stroke are high mechanical compliance, the ability to assist patients in completing desired movements, and the ability to provide only the minimum assistance necessary. This paper presents a novel controller that successfully exhibits these characteristics. The controller uses a standard model-based, adaptive control approach in order to learn the patients abilities and assist in completing movements while remaining compliant. Assistance-as-needed is achieved by adding a novel force reducing term to the adaptive control law, which decays the force output from the robot when errors in task execution are small. Several tests are presented using the upper extremity robotic therapy device named Pneu-WREX to evaluate the performance of the adaptive, ldquoassist-as-neededrdquo controller with people who have suffered a stroke. The results of these experiments illustrate the ldquoslackingrdquo behavior of human motor control: given the opportunity, the human patient will reduce his or her output, letting the robotic device do the work for it. The experiments also demonstrate how including the ldquoassist-as-neededrdquo modification in the controller increases participation from the motor system.

A standardized approach to the Fugl-Meyer assessment and its implications for clinical trials.

Background. Standardizing scoring reduces variability and increases accuracy. A detailed scoring and training method for the Fugl-Meyer motor assessment (FMA) is described and assessed, and implications for clinical trials considered. Methods. A standardized FMA scoring approach and training materials were assembled, including a manual, scoring sheets, and instructional video plus patient videos. Performance of this approach was evaluated for the upper extremity portion. Results. Inter- and intrarater reliability in 31 patients were excellent (intraclass correlation coefficient = 0.98-0.99), validity was excellent (r = 0.74-0.93, P < .0001), and minimal detectable change was low (3.2 points). Training required 1.5 hours and significantly reduced error and variance among 50 students, with arm FMA scores deviating from the answer key by 3.8 ± 6.2 points pretraining versus 0.9 ± 4.9 points posttraining. The current approach was implemented without incident into training for a phase II trial. Among 66 patients treated with robotic therapy, change in FMA was smaller (P ≤ .01) at the high and low ends of baseline FMA scores. Conclusions. Training with the current method improved accuracy, and reduced variance, of FMA scoring; the 20% FMA variance reduction with training would decrease sample size requirements from 137 to 88 in a theoretical trial aiming to detect a 7-point FMA difference. Minimal detectable change was much smaller than FMA minimal clinically important difference. The variation in FMA gains in relation to baseline FMA suggests that future trials consider a sliding outcome approach when FMA is an outcome measure. The current training approach may be useful for assessing motor outcomes in restorative stroke trials.

Retraining and assessing hand movement after stroke using the MusicGlove: comparison with conventional hand therapy and isometric grip training

BackgroundIt is thought that therapy should be functional, be highly repetitive, and promote afferent input to best stimulate hand motor recovery after stroke, yet patients struggle to access such therapy. We developed the MusicGlove, an instrumented glove that requires the user to practice gripping-like movements and thumb-finger opposition to play a highly engaging, music-based, video game. The purpose of this study was to 1) compare the effect of training with MusicGlove to conventional hand therapy 2) determine if MusicGlove training was more effective than a matched form of isometric hand movement training; and 3) determine if MusicGlove game scores predict clinical outcomes.Methods12 chronic stroke survivors with moderate hemiparesis were randomly assigned to receive MusicGlove, isometric, and conventional hand therapy in a within-subjects design. Each subject participated in six one-hour treatment sessions three times per week for two weeks, for each training type, for a total of 18 treatment sessions. A blinded rater assessed hand impairment before and after each training type and at one-month follow-up including the Box and Blocks (B & B) test as the primary outcome measure. Subjects also completed the Intrinsic Motivation Inventory (IMI).ResultsSubjects improved hand function related to grasping small objects more after MusicGlove compared to conventional training, as measured by the B & B score (improvement of 3.21±3.82 vs. -0.29±2.27 blocks; P=0.010) and the 9 Hole Peg test (improvement of 2.14±2.98 vs. -0.85±1.29 pegs/minute; P=0.005). There was no significant difference between training types in the broader assessment batteries of hand function. Subjects benefited less from isometric therapy than MusicGlove training, but the difference was not significant (P>0.09). Subjects sustained improvements in hand function at a one month follow-up, and found the MusicGlove more motivating than the other two therapies, as measured by the IMI. MusicGlove games scores correlated strongly with the B & B score.ConclusionsThese results support the hypothesis that hand therapy that is engaging, incorporates high numbers of repetitions of gripping and thumb-finger opposition movements, and promotes afferent input is a promising approach to improving an individual’s ability to manipulate small objects. The MusicGlove provides a simple way to access such therapy.

Comparison of three-dimensional, assist-as-needed robotic arm/hand movement training provided with Pneu-WREX to conventional tabletop therapy after chronic stroke.

ObjectivesRobot-assisted movement training can help individuals with stroke reduce arm and hand impairment, but robot therapy is typically only about as effective as conventional therapy. Refining the way that robots assist during training may make them more effective than conventional therapy. Here, the authors measured the therapeutic effect of a robot that required individuals with a stroke to achieve virtual tasks in three dimensions against gravity. DesignThe robot continuously estimated how much assistance patients needed to perform the tasks and provided slightly less assistance than needed to reduce patient slacking. Individuals with a chronic stroke (n = 26; baseline upper limb Fugl-Meyer score, 23 ± 8) were randomized into two groups and underwent 24 one-hour training sessions over 2 mos. One group received the assist-as-needed robot training and the other received conventional tabletop therapy with the supervision of a physical therapist. ResultsTraining helped both groups significantly reduce their motor impairment, as measured by the primary outcome measure, the Fugl-Meyer score, but the improvement was small (3.0 ± 4.9 points for robot therapy vs. 0.9 ± 1.7 for conventional therapy). There was a trend for greater reduction for the robot-trained group (P = 0.07). The robot group largely sustained this gain at the 3-mo follow-up. The robot-trained group also experienced significant improvements in Box and Blocks score and hand grip strength, whereas the control group did not, but these improvements were not sustained at follow-up. In addition, the robot-trained group showed a trend toward greater improvement in sensory function, as measured by the Nottingham Sensory Test (P = 0.06). ConclusionsThese results suggest that in patients with chronic stroke and moderate-severe deficits, assisting in three-dimensional virtual tasks with an assist-as-needed controller may make robotic training more effective than conventional tabletop training.

Real-time computer modeling of weakness following stroke optimizes robotic assistance for movement therapy

This paper describes the development of a novel control system for a robotic arm orthosis for assisting patients in motor training following stroke. The robot allows naturalistic motion of the arm and is as mechanically compliant as a human therapists arms. This compliance preserves the connection between effort and error that appears essential for motor learning, but presents a challenge: accurately creating desired movements requires that the robot form a model of the patients weakness, since the robot cannot simply stiffly drive the arm along the desired path. We show here that a standard model-based adaptive controller allows the robot to form such a model of the patient and complete movements accurately. However, we found that the human motor system, when coupled to such an adaptive controller, reduces its own participation, allowing the adaptive controller to take over the performance of the task. This presents a problem for motor training, since active engagement by the patient is important for stimulating neuroplasticity. We show that this problem can be solved by making the controller continuously attempt to reduce its assistance when errors are small. The resulting robot successfully assists stroke patients in moving in desired patterns with very small errors, but also encourages intense participation by the patient. Such robot assistance may optimally provoke neural plasticity, since it intensely engages both descending and ascending motor pathways.

Do robotic and non-robotic arm movement training drive motor recovery after stroke by a common neural mechanism? experimental evidence and a computational model

Different dose-matched, upper extremity rehabilitation training techniques, including robotic and non-robotic techniques, can result in similar improvement in movement ability after stroke, suggesting they may elicit a common drive for recovery. Here we report experimental results that support the hypothesis of a common drive, and develop a computational model of a putative neural mechanism for the common drive. We compared weekly motor control recovery during robotic and unassisted movement training techniques after chronic stroke (n = 27), as assessed with quantitative measures of strength, speed, and coordination. The results showed that recovery in both groups followed an exponential time course with a time constant of about 4–5 weeks. Despite the greater range and speed of movement practiced by the robot group, motor recovery was very similar between the groups. The premise of the computational model is that improvements in motor control are caused by improvements in the ability to activate spared portions of the damaged corticospinal system, as learned by a biologically plausible search algorithm. Robot-assisted and unassisted training would in theory equally drive this search process.

Corticospinal Excitability as a Predictor of Functional Gains at the Affected Upper Limb Following Robotic Training in Chronic Stroke Survivors

Background. Robotic training can help improve function of a paretic limb following a stroke, but individuals respond differently to the training. A predictor of functional gains might improve the ability to select those individuals more likely to benefit from robot-based therapy. Studies evaluating predictors of functional improvement after a robotic training are scarce. One study has found that white matter tract integrity predicts functional gains following a robotic training of the hand and wrist. Objective. To determine the predictive ability of behavioral and brain measures in order to improve selection of individuals for robotic training. Methods: Twenty subjects with chronic stroke participated in an 8-week course of robotic exoskeletal training for the arm. Before training, a clinical evaluation, functional magnetic resonance imaging (fMRI), diffusion tensor imaging, and transcranial magnetic stimulation (TMS) were each measured as predictors. Final functional gain was defined as change in the Box and Block Test (BBT). Measures significant in bivariate analysis were fed into a multivariate linear regression model. Results. Training was associated with an average gain of 6 ± 5 blocks on the BBT (P < .0001). Bivariate analysis revealed that lower baseline motor-evoked potential (MEP) amplitude on TMS, and lower laterality M1 index on fMRI each significantly correlated with greater BBT change. In the multivariate linear regression analysis, baseline MEP magnitude was the only measure that remained significant. Conclusion. Subjects with lower baseline MEP magnitude benefited the most from robotic training of the affected arm. These subjects might have reserve remaining for the training to boost corticospinal excitability, translating into functional gains.

MusicGlove: Motivating and quantifying hand movement rehabilitation by using functional grips to play music

People with stroke typically must perform much of their hand exercise at home without professional assistance as soon as two weeks after the stroke. Without feedback and encouragement, individuals often lose motivation to practice using the affected hand, and this disuse contributes to further declines in hand function. We developed the MusicGlove as a way to facilitate and motivate at home practice of hand movement. This low-cost device uses music as an interactive and motivating medium to guide hand exercise and to quantitatively assess hand movement recovery. It requires the user to practice functional movements, including pincer grip, key-pinch grip, and finger-thumb opposition, by using those movements to play different musical notes, played along to songs displayed by an interactive computer game. We report here the design of the glove and the results of a single-session experiment with 10 participants with chronic stroke. We found that the glove is well suited for use by people with an impairment level quantified by a Box and Blocks score of at least around 7; that the glove can be used to obtain a measure of hand dexterity (% of notes hit) that correlates strongly with the Box and Blocks score; and that the incorporation of music into training significantly improved both objective measures of hand motor performance and self-ratings of motivation for training in the single session.

Robot-assisted Guitar Hero for finger rehabilitation after stroke

This paper describes the design and testing of a robotic device for finger therapy after stroke: FINGER (Finger Individuating Grasp Exercise Robot). FINGER makes use of stacked single degree-of-freedom mechanisms to assist subjects in moving individual fingers in a naturalistic grasping pattern through much of their full range of motion. The device has a high bandwidth of control (-3dB at approximately 8 Hz) and is backdriveable. These characteristics make it capable of assisting in grasping tasks that require precise timing. We therefore used FINGER to assist individuals with a stroke (n= 8) and without impairment (n= 4) in playing a game similar to Guitar Hero©. The subjects attempted to move their fingers to target positions at times specified by notes that were graphically streamed to popular music. We show here that by automatically adjusting the robot gains, it is possible to use FINGER to modulate the subjects success rate at the game, across a range of impairment levels. Modulating success rates did not alter the stroke subjects effort, although the unimpaired subjects exerted more force when they were made less successful. We also present a novel measure of finger individuation that can be assessed as individuals play Guitar Hero with FINGER. The results demonstrate the ability of FINGER to provide controlled levels of assistance during an engaging computer game, and to quantify finger individuation after stroke.

The variable relationship between arm and hand use: A rationale for using finger magnetometry to complement wrist accelerometry when measuring daily use of the upper extremity

Wrist-worn accelerometers are becoming more prevalent as a means to assess use of the impaired upper extremity in daily life after stroke. However, wrist accelerometry does not measure joint movements of the hand, which are integral to functional use of the upper extremity. In this study, we used a custom-built, non-obtrusive device called the manumeter to measure both arm use (via wrist accelerometry) and hand use (via finger magnetometry) of a group of unimpaired subjects while they performed twelve motor tasks at three intensities. We also gave the devices to four stroke subjects and asked them to wear them for six hours a day for one month. From the in-lab testing we found that arm use was a strong predictor of hand use for individual tasks, but that the slope of the relationship varied by up to a factor of ~12 depending on the task being performed. Consistent with this, in the daily use data collected from stroke subjects we found a broad spread in the relationship between arm and hand use. These results suggest that analyzing the spread of the relationship between daily hand and arm use will give more insight into upper extremity recovery than wrist accelerometry or finger magnetometry alone, because the spread reflects the nature of the daily tasks performed as well as the amount of upper extremity use.