User-Driven Functional Movement Training With a Wearable Hand Robot After Stroke.
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ABSTRACT:
SUBMITTER: Park S
PROVIDER: S-EPMC8274060 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Stroke often leads to hand motor dysfunction, and effective rehabilitation requires keeping patients engaged and motivated. Among the existing automated rehabilitation approaches, data glove-based systems are not easy to wear for patients due to spasticity, and single sensor-based approaches generally provided prohibitively limited information. We thus propose a wearable multimodal serious games approach for hand movement training after stroke. A force myography (FMG), electromyography (EMG), and inertial measurement unit (IMU)-based multi-sensor fusion model was proposed for hand movement classification, which was worn on the user’s affected arm. Two movement recognition-based serious games were developed for hand movement and cognition training. Ten stroke patients with mild to moderate motor impairments (Brunnstrom Stage for Hand II-VI) performed experiments while playing interactive serious games requiring 12 activities-of-daily-living (ADLs) hand movements taken from the Fugl Meyer Assessment. Feasibility was evaluated by movement classification accuracy and qualitative patient questionnaires. The offline classification accuracy using combined FMG-EMG-IMU was 81.0% for the 12 movements, which was significantly higher than any single sensing modality; only EMG, only FMG, and only IMU were 69.6, 63.2, and 47.8%, respectively. Patients reported that they were more enthusiastic about hand movement training while playing the serious games as compared to conventional methods and strongly agreed that they subjectively felt that the proposed training could be beneficial for improving upper limb motor function. These results showed that multimodal-sensor fusion improved hand gesture classification accuracy for stroke patients and demonstrated the potential of this proposed approach to be used as upper limb movement training after stroke.
| S-EPMC9204487 | biostudies-literature
Project description:ObjectiveSeventy-five percent of stroke survivors, caregivers, and health care professionals (HCP) believe current therapy practices are insufficient, specifically calling out the upper extremity as an area where innovation is needed to develop highly usable prosthetics/orthotics for the stroke population. A promising method for controlling upper extremity technologies is to infer movement intention non-invasively from surface electromyography (EMG). However, existing technologies are often limited to research settings and struggle to meet user needs.ApproachTo address these limitations, we have developed the NeuroLife® EMG System, an investigational device which consists of a wearable forearm sleeve with 150 embedded electrodes and associated hardware and software to record and decode surface EMG. Here, we demonstrate accurate decoding of 12 functional hand, wrist, and forearm movements in chronic stroke survivors, including multiple types of grasps from participants with varying levels of impairment. We also collected usability data to assess how the system meets user needs to inform future design considerations.Main resultsOur decoding algorithm trained on historical- and within-session data produced an overall accuracy of 77.1 ± 5.6% across 12 movements and rest in stroke participants. For individuals with severe hand impairment, we demonstrate the ability to decode a subset of two fundamental movements and rest at 85.4 ± 6.4% accuracy. In online scenarios, two stroke survivors achieved 91.34 ± 1.53% across three movements and rest, highlighting the potential as a control mechanism for assistive technologies. Feedback from stroke survivors who tested the system indicates that the sleeve's design meets various user needs, including being comfortable, portable, and lightweight. The sleeve is in a form factor such that it can be used at home without an expert technician and can be worn for multiple hours without discomfort.SignificanceThe NeuroLife EMG System represents a platform technology to record and decode high-resolution EMG for the real-time control of assistive devices in a form factor designed to meet user needs. The NeuroLife EMG System is currently limited by U.S. federal law to investigational use.
| S-EPMC10787968 | biostudies-literature
Project description:The wide variation in upper extremity motor impairments among stroke survivors necessitates more intelligent methods of customized therapy. However, current strategies for characterizing individual motor impairments are limited by the use of traditional clinical assessments (e.g., Fugl-Meyer) and simple engineering metrics (e.g., goal-directed performance). Our overall approach is to statistically identify the range of volitional movement capabilities, and then apply a robot-applied force vector field intervention that encourages under-expressed movements. We investigated whether explorative training with such customized force fields would improve stroke survivors' (n = 11) movement patterns in comparison to a control group that trained without forces (n = 11). Force and control groups increased Fugl-Meyer UE scores (average of 1.0 and 1.1, respectively), which is not considered clinically meaningful. Interestingly, participants from both groups demonstrated dramatic increases in their range of velocity during exploration following only six days of training (average increase of 166.4% and 153.7% for the Force and Control group, respectively). While both groups showed evidence of improvement, we also found evidence that customized forces affected learning in a systematic way. When customized forces were active, we observed broader distributions of velocity that were not present in the controls. Second, we found that these changes led to specific changes in unassisted motion. In addition, while the shape of movement distributions changed significantly for both groups, detailed analysis of the velocity distributions revealed that customized forces promoted a greater proportion of favorable changes. Taken together, these results provide encouraging evidence that patient-specific force fields based on individuals' movement statistics can be used to create new movement patterns and shape them in a customized manner. To the best of our knowledge, this paper is the first to directly link engineering assessments of stroke survivors' exploration movement behaviors to the design of customized robot therapy.
| S-EPMC5901661 | biostudies-literature
Project description: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.
| S-EPMC4022276 | biostudies-literature
Project description:Robot-assisted training combined with neural guided strategy has been increasingly applied to stroke rehabilitation. However, the induced neuroplasticity is seldom characterized. It is still uncertain whether this kind of guidance could enhance the long-term training effect for stroke motor recovery. This study was conducted to explore the clinical improvement and the neurological changes after 20-session guided or non-guided robot hand training using two measures: changes in brain discriminant ability between motor-imagery and resting states revealed from electroencephalography (EEG) signals and changes in brain network variability revealed from resting-state functional magnetic resonance imaging (fMRI) data in 24 chronic stroke subjects. The subjects were randomly assigned to receive either combined action observation (AO) with EEG-guided robot-hand training (RobotEEG_AO, n = 13) or robot-hand training without AO and EEG guidance (Robotnon-EEG_Text, n = 11). The robot hand in RobotEEG_AO group was activated only when significant mu suppression (8-12 Hz) was detected from subjects' EEG signals in ipsilesional hemisphere, while the robot hand in Robotnon-EEG_Text group was randomly activated regardless of their EEG signals. Paretic upper-limb motor functions were evaluated at three time-points: before, immediately after and 6 months after the interventions. Only RobotEEG_AO group showed a long-term significant improvement in their upper-limb motor functions while no significant and long-lasting training effect on the paretic motor functions was shown in Robotnon-EEG_Text group. Significant neuroplasticity changes were only observed in RobotEEG_AO group as well. The brain discriminant ability based on the ipsilesional EEG signals significantly improved after intervention. For brain network variability, the whole brain was first divided into six functional subnetworks, and significant increase in the temporal variability was found in four out of the six subnetworks, including sensory-motor areas, attention network, auditory network, and default mode network after intervention. Our results revealed the differences in the long-term training effect and the neuroplasticity changes following the two interventional strategies: with and without neural guidance. The findings might imply that sustainable motor function improvement could be achieved through proper neural guidance, which might provide insights into strategies for effective stroke rehabilitation. Furthermore, neuroplasticity could be promoted more profoundly by the intervention with proper neurofeedback, and might be shaped in relation to better motor skill acquisition.
| S-EPMC6186842 | biostudies-literature
Project description:Hand function improvement in stroke survivors in the chronic stage usually plateaus by 6 months. Brain-computer interface (BCI)-guided robot-assisted training has been shown to be effective for facilitating upper-limb motor function recovery in chronic stroke. However, the underlying neuroplasticity change is not well understood. This study aimed to investigate the whole-brain neuroplasticity changes after 20-session BCI-guided robot hand training, and whether the changes could be maintained at the 6-month follow-up. Therefore, the clinical improvement and the neurological changes before, immediately after, and 6 months after training were explored in 14 chronic stroke subjects. The upper-limb motor function was assessed by Action Research Arm Test (ARAT) and Fugl-Meyer Assessment for Upper-Limb (FMA), and the neurological changes were assessed using resting-state functional magnetic resonance imaging. Repeated-measure ANOVAs indicated that long-term motor improvement was found by both FMA (F[2,26] = 6.367, p = 0.006) and ARAT (F[2,26] = 7.230, p = 0.003). Seed-based functional connectivity analysis exhibited that significantly modulated FC was observed between ipsilesional motor regions (primary motor cortex and supplementary motor area) and contralesional areas (supplementary motor area, premotor cortex, and superior parietal lobule), and the effects were sustained after 6 months. The fALFF analysis showed that local neuronal activities significantly increased in central, frontal and parietal regions, and the effects were also sustained after 6 months. Consistent results in FC and fALFF analyses demonstrated the increase of neural activities in sensorimotor and fronto-parietal regions, which were highly involved in the BCI-guided training. Clinical Trial Registration: This study has been registered at ClinicalTrials.gov with clinical trial registration number NCT02323061.
| S-EPMC7855586 | biostudies-literature
Project description:Although rehabilitation robotics seems to be a promising therapy in the rehabilitation of the upper limb in stroke patients, consensus is still lacking on its additive effects. Therefore, there is a need for determining the possible success of robotic interventions on selected patients, which in turn determine the necessity for new investigating instruments supporting the treatment decision-making process and customization. The objective of the work presented in this preliminary study was to verify that fully robot assistance would not affect the physiological oscillatory cortical activity related to a functional movement in healthy subjects. Further, the clinical results following the robotic treatment of a chronic stroke patient, who positively reacted to the robotic intervention, were analyzed and discussed. First results show that there is no difference in EEG activation pattern between assisted and no-assisted movement in healthy subjects. Even more importantly, the patient's pretreatment EEG activation pattern in no-assisted movement was completely altered, while it recovered to a quasi-physiological one in robot-assisted movement. The functional improvement following treatment was large. Using pretreatment EEG recording during robot-assisted movement might be a valid approach to assess the potential ability of the patient for recovering.
| S-EPMC4739000 | biostudies-literature
Project description:BackgroundGait dysfunction is common in post-stroke patients as a result of impairment in cerebral gait mechanism. Powered robotic exoskeletons are promising tools to maximize neural recovery by delivering repetitive walking practice.ObjectivesThe purpose of this study was to investigate the modulating effect of the Gait Enhancing and Motivating System-Hip (GEMS-H) on cortical activation during gait in patients with chronic stroke.MethodsTwenty chronic stroke patients performed treadmill walking at a self-selected speed either with assistance of GEMS-H (GEMS-H) or without assistance of GEMS-H (NoGEMS-H). Changes in oxygenated hemoglobin (oxyHb) concentration in the bilateral primary sensorimotor cortex (SMC), premotor cortices (PMC), supplemental motor areas (SMA), and prefrontal cortices (PFC) were recorded using functional near infrared spectroscopy.ResultsWalking with the GEMS-H promoted symmetrical SMC activation, with more activation in the affected hemisphere than in NoGEMS-H conditions. GEMS-H also decreased oxyHb concentration in the late phase over the ipsilesional SMC and bilateral SMA (P < 0.05).ConclusionsThe results of the present study reveal that the GEMS-H promoted more SMC activation and a balanced activation pattern that helped to restore gait function. Less activation in the late phase over SMC and SMA during gait with GEMS-H indicates that GEMS-H reduces the cortical participation of stroke gait by producing rhythmic hip flexion and extension movement and allows a more coordinate and efficient gait patterns. Trial registration NCT03048968. Registered 06 Feb 2017.
| S-EPMC7596937 | biostudies-literature
Project description:Enabling users to teach their robots new tasks at home is a major challenge for research in personal robotics. This work presents a user study in which participants were asked to teach the robot Pepper a game of skill. The robot was equipped with a state-of-the-art skill learning method, based on dynamic movement primitives (DMPs). The only feedback participants could give was a discrete rating after each of Pepper's movement executions ("very good," "good," "average," "not so good," "not good at all"). We compare the learning performance of the robot when applying user-provided feedback with a version of the learning where an objectively determined cost via hand-coded cost function and external tracking system is applied. Our findings suggest that (a) an intuitive graphical user interface for providing discrete feedback can be used for robot learning of complex movement skills when using DMP-based optimization, making the tedious definition of a cost function obsolete; and (b) un-experienced users with no knowledge about the learning algorithm naturally tend to apply a working rating strategy, leading to similar learning performance as when using the objectively determined cost. We discuss insights about difficulties when learning from user provided feedback, and make suggestions how learning continuous movement skills from non-expert humans could be improved.
| S-EPMC7805866 | biostudies-literature
Project description:ImportanceEffective treatment of the affected hand after stroke is crucial for improved functional independence and recovery.ObjectiveTo determine the feasibility and clinical utility of an electromyography-triggered hand robot.DesignSingle-group repeated-measures design. Participants completed training 3×/wk for 6 wk. Feasibility data included participant feedback, adverse events, and compliance rates. Upper extremity outcomes were collected at baseline, discharge, and 6-wk follow-up.SettingOutpatient clinic.ParticipantsTwelve stroke survivors at least 6 mo poststroke living in the community.InterventionEighteen sessions of intensive robotic hand therapy over 6 wk. Each 60-min treatment session was personalized to match the participant's ability.Outcomes and measuresArm Motor Ability Test (AMAT), Stroke Impact Scale Hand subscale (SIS-H), Stroke Upper Limb Capacity Scale (SULCS), Fugl-Meyer Assessment, Box and Block Test, and dynamometer.ResultsAll participants completed the training phase. Mild skin pinching or rubbing at dorsal proximal interphalangeal joint and proximal arm fatigue were the most common adverse events. Improvements in raw scores were achieved from baseline to discharge for all outcome measures, except the SULCS. Participants significantly improved from baseline to discharge on the AMAT and the SIS-H, and improvements were maintained at 6-wk follow-up.Conclusion and relevanceRobotic hand training was feasible, safe, and well tolerated. Participants reported and demonstrated improvements in functional use of the affected arm. Thirty percent of participants achieved clinically significant improvements on the AMAT. We recommend further study of the device in a larger study using the AMAT as a primary outcome measure.What this article addsIt is feasible and safe to implement a robotic hand training protocol for people with moderate to severe arm impairment in an outpatient setting. Robotic training may provide a viable option for this group to actively participate in intensive training of the distal hand.
| S-EPMC8722515 | biostudies-literature