The objective of this study was to investigate the effectiveness of functional electrical stimulation (FES) applied to the wrist and finger extensors for wrist flexor spasticity in hemiplegic patients.
The complexity of upper extremity (UE) behavior requires recovery of near normal neuromuscular function to minimize residual disability following a stroke. This requirement places a premium on spontaneous recovery and neuroplastic adaptation to rehabilitation by the lesioned hemisphere. Motor skill learning is frequently cited as a requirement for neuroplasticity. Studies examining the links between training, motor learning, neuroplasticity, and improvements in hand motor function are indicated.
This case study describes a patient with slow recovering hand and finger movement (Total Upper Extremity Fugl-Meyer examination score = 25/66, Wrist and Hand items = 2/24 on poststroke day 37) following a stroke. The patient received an intensive eight-session intervention utilizing simulated activities that focused on the recovery of finger extension, finger individuation, and pinch-grasp force modulation.
Over the eight sessions, the patient demonstrated improvements on untrained transfer tasks, which suggest that motor learning had occurred, as well a dramatic increase in hand function and corresponding expansion of the cortical motor map area representing several key muscles of the paretic hand. Recovery of hand function and motor map expansion continued after discharge through the three-month retention testing.
This case study describes a neuroplasticity based intervention for UE hemiparesis and a model for examining the relationship between training, motor skill acquisition, neuroplasticity, and motor function changes. Implications for rehabilitation Intensive hand and finger rehabilitation activities can be added to an in-patient rehabilitation program for persons with subacute stroke. Targeted training of the thumb may have an impact on activity level function in persons with upper extremity hemiparesis. Untrained transfer tasks can be utilized to confirm that training tasks have elicited motor learning. Changes in cortical motor maps can be used to document changes in brain function which can be used to evaluate changes in motor behavior persons with subacute stroke.
Few patients recover full hand dexterity after an acquired brain injury such as stroke. Repetitive somatosensory electrical stimulation (SES) is a promising method to promote recovery of hand function. However, studies using SES have largely focused on gross motor function; it remains unclear if it can modulate distal hand functions such as finger individuation.
The specific goal of this study was to monitor the effects of SES on individuation as well as on cortical oscillations measured using EEG, with the additional goal of identifying neurophysiological biomarkers.
Eight participants with a history of acquired brain injury and distal upper limb motor impairments received a single two-hour session of SES using transcutaneous electrical nerve stimulation. Pre- and post-intervention assessments consisted of the Action Research Arm Test (ARAT), finger fractionation, pinch force, and the modified Ashworth scale (MAS), along with resting-state EEG monitoring.
SES was associated with significant improvements in ARAT, MAS and finger fractionation. Moreover, SES was associated with a decrease in low frequency (0.9-4 Hz delta) ipsilesional parietomotor EEG power. Interestingly, changes in ipsilesional motor theta (4.8–7.9 Hz) and alpha (8.8–11.7 Hz) power were significantly correlated with finger fractionation improvements when using a multivariate model.
We show the positive effects of SES on finger individuation and identify cortical oscillations that may be important electrophysiological biomarkers of individual responsiveness to SES. These biomarkers can be potential targets when customizing SES parameters to individuals with hand dexterity deficits. Trial registration: NCT03176550; retrospectively registered.
Despite recent advances in rehabilitation, a substantial fraction of stroke patients continue to experience persistent upper-limb deficits . At best, up to 1 out of 5 patients will recover full arm function, while 50% will not recover any functional use of the affected arm.  Improvement in upper limb function specifically depends on sensorimotor recovery of the paretic hand . Yet, there remains a lack of effective therapies readily available to the patient with acquired brain injury for recovery of hand and finger function; a systematic review found that conventional repetitive task training may not be consistently effective for the upper extremity . It is thus critical to explore inexpensive and scalable approaches to restore hand and finger dexterity, reduce disability and increase participation after stroke and other acquired brain injuries.
Sensory threshold somatosensory electrical stimulation (SES) is a promising therapeutic modality for targeting hand motor recovery . It is known to be a powerful tool to focally modulate sensorimotor cortices in both healthy and chronic stroke participants [5, 6, 7, 8]. Devices such as transcutaneous nerve stimulation (TENS) units can deliver SES and are commercially available, inexpensive, low risk, and easily applied in the home setting . Previous studies have demonstrated short-term and long-term improvements in hand function after SES [5, 10, 11, 12, 13, 14, 15]. However, the effect of SES on regaining the ability to selectively move a given digit independently from other digits (i.e. finger fractionation) has not been investigated. Poor finger individualization is an important therapeutic target because it is commonly present even after substantial recovery and may account for chronic hand dysfunction . Further, it is unclear if SES is associated with compensatory or restorative mechanisms. Prior studies have largely relied on relatively subjective clinical evaluations of impairment, such as the Fugl-Meyer Assessment, or timed and task-based assessments, such as the Jebson-Taylor Hand Function Test. Biomechanical analyses, on the other hand, can provide important objective and quantitative evidence of improvement in neurologic function and normative motor control [17, 18]. Therefore, we aimed to determine not only the functional effects, but also the kinematic effects, of SES on chronic hand dysfunction.
Simultaneously, it should be noted that although SES can potentially be an effective therapy, not all individuals who are administered SES experience positive effects. While improvement levels as high as 31–36% compared to baseline function have been reported, [11, 19] about half of one cohort demonstrated minimal or no motor performance improvement after a single session of SES . One method to shed more light on this discrepancy is to identify neurophysiological biomarkers associated with motor responses to SES. Neurophysiological biomarkers are increasingly used to predict treatment effects [20, 21]. Although some studies have examined biomarkers associated with treatment-induced motor recovery, to our knowledge none have been performed for SES [22, 23]. A recent study using electroencephalography (EEG) found that changes in patterns of connectivity predicted motor recovery after stroke . At present, little is known about the effect of peripheral neuromodulation on EEG activity, how existing neural dynamics interacts with peripheral stimulation, and whether this interaction is associated with improvements in motor function. Associating EEG activity with treatment response may also provide mechanistic insight regarding the effects of SES on neural plasticity. EEG activity can also potentially be used as a cost-effective real-time metric of the time-varying efficacy of SES. This novel application of EEG information may help tailor treatment efforts while reducing the variability in outcome.
The main goal of this pilot study was to evaluate both changes in finger fractionation in response to SES and identify the associated neural biomarkers through analyses of EEG dynamics. Outcomes from this study have potential in designing targeted SES therapy based on neural biomarkers to modulate and improve hand function after acquired brain injury such as stroke (e.g. enrollment in long-term studies of the efficacy of SES).
Repetitive movements at a constant rate require the integration of internal time counting and motor neural networks. Previous studies have proved that humans can follow short durations automatically (automatic timing) but require more cognitive efforts to track or estimate long durations. In this study, we studied sensorimotor oscillatory activities in healthy subjects and chronic stroke patients when subjects were performing repetitive finger movements. We found the movement-modulated changes in alpha and beta oscillatory activities were decreased with the increase of movement rates in finger lifting of healthy subjects and the non-paretic hands in stroke patients, whereas no difference was found in the paretic-hand movements at different movement rates in stroke patients. The significant difference in oscillatory activities between movements of non-paretic hands and paretic hands could imply the requirement of higher cognitive efforts to perform fast repetitive movements in paretic hands. The sensorimotor oscillatory response in fast repetitive movements could be a possible indicator to probe the recovery of motor function in stroke patients.
Timing in the brain has its important role in many aspects, such as speech perception, speech production, reading, attention, memory, cognitive processing, decision-making, and motor coordination1. Especially, internal time counting is crucial for motor control in our daily life activities. The processing of time estimation for movements has been studied in many literatures2. Morillon et al. postulated the time estimation in human motor system as a dual system, which can track a short duration automatically (automatic timing) but requires more cognitive demands to track a long duration by a so-called default mode network (DMN)3. Poppel E. studied the capability of time estimation in a stimulus reproduction task from 0.5 s to 7 s, and found movements become temporally irregular for inter-movement interval (IMI) above 3 s which indicated precisely control of movements with IMIs longer than 3 s is not possible4. Though these literatures have shown great difference between movements in short and long durations in healthy subjects, nevertheless, the study of brain responses induced by rapid movements in patients with motor neurological disorder was seldom reported.
Several imaging modalities have been developed to quantify motor response in human brain, including EEG, MEG, fMRI, TMS, etc.5,6. The EEG, which is the tool used most widely, has the advantages of low-cost, easy preparation, and its superiority of high temporal resolution to measure fast changes of neural oscillatory activities. Neural oscillatory activities in human brain can be either phase-locked or non-phase-locked reactive to external or internal stimuli. These oscillatory activities usually exist in specific frequency bands and spatial locations. Event-related non-phase-locked neural activities represent power changes, either enhanced or suppressed relative to baseline activities. The power changes in event-related activities can be caused by the decrease or increase in synchrony of the underlying activated neuronal populations. Pfurtscheller et al.7 studied the Mu-rhythm changes in discrete voluntary finger movements, and found oscillatory activities were suppressed, started about 1.5 s preceding movement onsets, followed by post-movement power rebound, occurred around 0.7 s~1 s after movement offsets7. The power suppression was referred to as event-related desynchronization (ERD), reflecting the motor planning and preparation of initialization a movement, whereas the post-movement power rebound was referred to as event-related synchronization (ERS), indicating the motor inhibition or idling of motor neural network. Other EEG techniques, such as temporal-spectral evolution (TSE)8, amplitude modulation (AM)9, autoregression model method (AR)10, etc., were also developed to quantify task-specific brain oscillatory activity. These signal processing tools enable researchers to quantify the neural activities under different experimental manipulations and provide evidences for diagnosing clinical neurological diseases11,12,13.
The difference of brain oscillatory activities between healthy and stroke patients has been investigated in some studies. Rossiter et al. studied the movement-related beta desynchronization (MRBD) in healthy and middle cerebral artery (MCA) stroke patients14. They found reduced MRBD when patients were performing visually-cued grip task with their affected hand, compared to the MRBD obtained from healthy subjects. Giaquinto et al. followed up the changes of resting EEG in different frequency bands over six months in MCA stroke patients15, and they observed the amplitudes of movement-related Mu – rhythm improved significantly in the first three months and reached stable states in six months after stroke. Tecchio et al. studied the rhythmic brain activity at resting states in mono-hemispheric MCA stoke patients16. They found both the values of spectral power in affected and unaffected hemispheres were increased over Rolandic areas. Stepien et al. studied alpha ERD in stroke patients with cortical and subcortical lesions in performing a visually-cued button press task17. They found suppressed ERD in affected hemisphere when moving paretic hand, while no suppression in alpha ERD was found in the affected hemisphere when moving non-paretic hand. These studies measured oscillatory activities of sensorimotor Mu rhythm in visual selection task or slow self-paced voluntary movement (IMI ≥ 7 s). Oscillatory activity induced by fast repetitive movement in stroke patient was not studied. Since fast simple movement has been reported to have strong coupled connections among motor-related cortices18, study of cortical oscillatory activity in rapid simple movements could be crucial for the understanding of motor function in stroke patients.
Fast repetitive movement with short IMI recruits several motor-related areas in human brain, including primary motor cortex (M1), premotor cortex, supplementary motor cortex, cingulate cortex, basal ganglia, and thalamus19. Studies in healthy subjects have shown clear difference between the oscillatory activities induced by slow and fast repetitive movements. Wu et al. recorded the post-movement beta rebound (PMBD) in healthy subjects and observed that the PMBD was suppressed with the decrease of IMI in repetitive finger-lifting movements19. Erbil and Ungan19 investigated EEG alpha and beta oscillatory activities in repetitive extension-flexion finger movements over rolandic regions. Sustained suppression in Mu rhythm was observed during continuous movements which indicated that continuous movements are conducted through neural processing distinct from discrete movements. Bortoletto and Cunnington measured the fMRI responses of repetitive movements, and compared the results with another two finger movements with highly cognitive demands, one was a complicated sequencing task and the other was a timing task20. They found neural activities in lateral prefrontal regions were participated differently in the three tasks, owing to the different levels of cognitive efforts involved in the three tasks. In this study, we aimed to study the oscillatory activities induced by simple repetitive movements in healthy subjects and chronic stroke patients. The difference of oscillatory activities between stroke patients and healthy subjects might be a potential feature to evaluate the recovery of motor function in stroke patients.[…]
Cerebral Palsy, trauma, and strokes are common causes for the loss of hand movements and the decrease in muscle strength for both children and adults. Improving fine motor skills usually involves the synchronization of wrists and fingers by performing appropriate tasks and activities. This demo introduces a novel patient-centered framework for the gamification of hand therapies in order to facilitate and encourage the rehabilitation process. This framework consists of an adaptive therapy-driven 3D environment augmented with our motion-based natural user interface. An intelligent game generator is developed, which translates the patient’s gestures into navigational movements with therapy-driven goals, while adapting the level of difficulty based on the patient profile and real-time performance. A comprehensive evaluation and clinical-based assessments were conducted in a local children disability center, and highlights of the results are presented.
Imad Afyouni , Faizan Ur Rehman , Ahmad Qamar , Akhlaq Ahmad , Mohamed Abdur Rahman , Saleh Basalamah, A GIS-based serious game recommender for online physical therapy, Proceedings of the Third ACM SIGSPATIAL International Workshop on the Use of GIS in Public Health, November 04-04, 2014, Dallas, Texas [doi>10.1145/2676629.2676634]
Cati Boulanger , Adam Boulanger , Lilian de Greef , Andy Kearney , Kiley Sobel , Russell Transue , Z Sweedyk , Paul H. Dietz , Steven Bathiche, Stroke rehabilitation with a sensing surface, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, April 27-May 02, 2013, Paris, France [doi>10.1145/2470654.2466160]
Maryam Khademi , Hossein Mousavi Hondori , Alison McKenzie , Lucy Dodakian , Cristina Videira Lopes , Steven C. Cramer, Free-hand interaction with leap motion controller for stroke rehabilitation, CHI ’14 Extended Abstracts on Human Factors in Computing Systems, April 26-May 01, 2014, Toronto, Ontario, Canada [doi>10.1145/2559206.2581203]
Impairment of hand and finger function after stroke is common and affects the ability to perform activities of daily living. Even though many of these coordination deficits such as finger individuation have been well characterized, it is critical to understand how stroke survivors learn to explore and reorganize their finger coordination patterns for optimizing rehabilitation. In this study, I examine the use of a body-machine interface to assess how participants explore their movement repertoire, and how this changes with continued practice.
Ten participants with chronic stroke wore a data glove and the finger joint angles were mapped on to the position of a cursor on a screen. The task of the participants was to move the cursor back and forth between two specified targets on a screen. Critically, the map between the finger movements and cursor motion was altered so that participants sometimes had to generate coordination patterns that required finger individuation. There were two phases to the experiment – an initial assessment phase on day 1, followed by a learning phase (days 2–5) where participants trained to reorganize their coordination patterns.
Participants showed difficulty in performing tasks which had maps that required finger individuation, and the degree to which they explored their movement repertoire was directly related to clinical tests of hand function. However, over four sessions of practice, participants were able to learn to reorganize their finger movement coordination pattern and improve their performance. Moreover, training also resulted in improvements in movement repertoire outside of the context of the specific task during free exploration.
Stroke survivors show deficits in movement repertoire in their paretic hand, but facilitating movement exploration during training can increase the movement repertoire. This suggests that exploration may be an important element of rehabilitation to regain optimal function.
Stroke often results in impairments of upper extremity, including hand and finger function, with 75% of stroke survivors facing difficulties performing activities of daily living [1, 2]. Critically, impairments after stroke not only include muscle- and joint-specific deficits such as weakness, and changes in the kinetic and kinematic workspace of the fingers [3, 4], but also coordination deficits such as reduced independent joint control  and impairments in finger individuation and enslaving [6, 7, 8, 9]. Therefore, understanding how to address these coordination deficits is critical for improving hand rehabilitation.
Typical approaches to hand rehabilitation emphasize repetition  and functional practice based on evidence that such experience can cause reorganization in the brain . Although this has proven to be reasonably successful, functional practice (such as repetitive grasping of objects) does not specify the coordination pattern to be used when performing the tasks. As a result, because of the redundancy in the human body, there is a risk that stroke survivors may adopt atypical compensatory movements to perform tasks . These compensatory movements have been mainly identified during reaching [13, 14], but there is evidence that they are also present in finger coordination patterns during grasping . Although there is still debate over the role of compensatory movements in rehabilitation , there is at least some evidence both in animal and humans that continued use of these compensatory patterns may be detrimental to true recovery [17, 18, 19].
To address this issue, there has been a greater focus on directly facilitating the learning of new coordination patterns. Specifically, in hand rehabilitation, virtual tasks (such as playing a virtual piano) have been examined as a way to train finger individuation [20, 21]. In these protocols, individuation is encouraged by asking participants to press a particular key with a finger, while keeping other fingers stationary. A similar approach to improve hand dexterity was also adopted by developing a glove that could be used as a controller for a popular guitar-playing video game . However, directly instructing desired coordination patterns to be produced becomes challenging as the number of degrees of freedom involved in the coordination pattern increase. For example, the hand has approximately 20 kinematic degrees of freedom, and providing verbal, visual or auditory feedback for simultaneously controlling all these degrees of freedom would be a major challenge. A potential solution that has been suggested is not to directly instruct the coordination pattern itself, but rather let participants explore different coordination patterns . This idea of motor exploration is based on dynamical systems theory that suggests that variability and exploration may help participants escape sub-optimal pre-existing coordination patterns and potentially settle in more optimal coordination patterns [24, 25, 26, 27]. Such exploration has been shown to be important in adapting existing movement repertoire , and has also been shown to be associated with faster rates of learning .
In order to test the hypothesis that exploration of novel coordination patterns can improve overall movement repertoire, I used a body-machine interface [30, 31] to examine how stroke survivors explore and reorganize finger coordination patterns with practice. A body-machine interface maps body movements (in this case finger movements) to the control of a real or virtual object (in this case a screen cursor), which can provide a way to elicit different coordination patterns in the context of an intuitive task. Specifically I examined: (i) how stroke survivors reorganize their finger coordination patterns, (ii) how training to explore novel coordination patterns affects their ability to reorganize their coordination pattern, and (iii) if training to explore novel coordination patterns has an effect on their overall movement repertoire. In this context, I use the term “novel” to indicate coordination patterns that require finger individuation. This assumption is motivated by the finding that stroke survivors have difficulty producing finger individuation even under explicit instruction [6, 9], and therefore it is highly likely that they would not use coordination patterns requiring finger individuation frequently in activities of daily living.[…]
The prototype robotic finger has three sliders, five links, ten bolts, and one motor. As the motor rotates, the blue crank moves the gray coupler forwards or backwards. The gray coupler pushes and pulls the yellow slider arm, making it move along the slot. When the yellow slider moves, this causes the green link, or N-shaped linkage, to rotate, which in turn causes the yellow and outer red sliders to move together. The N-shaped linkage continues to push and pull the outer red slider, causing it to move along the slot. The outer red slider connects to the human finger and causes it to bend.[…]
The objective of this study was to investigate the effectiveness of functional electrical stimulation (FES) applied to the wrist and finger extensors for wrist flexor spasticity in hemiplegic patients.
Thirty stroke patients treated as inpatients were included in the study. Patients were randomly divided into study and control groups. FES was applied to the study group. Wrist range of movement, the Modified Ashworth Scale (MAS), Rivermead Motor Assessment (RMA), Brunnstrom (BS) hand neurophysiological staging, Barthel Index (BI), and Upper Extremity Function Test (UEFT) are outcome measures.
There was no significant difference regarding range of motion (ROM) and BI values on admission between the groups. A significant difference was found in favor of the study group for these values at discharge. In the assessment within groups, there was no significant difference between admission and discharge RMA, BS hand, and UEFT scores in the control group, but there was a significant difference between the admission and discharge values for these parameters in the study group. Both groups showed improvement in MAS values on internal assessment.
It was determined that FES application is an effective method to reduce spasticity and to improve ROM, motor, and functional outcomes in hemiplegic wrist flexor spasticity.
MusicGlove is a hand therapy device that is clinically proven to improve hand function in 2 weeks.
The device is a sensorized “glove” that allows users to perform hundreds of hand and finger exercises while playing a therapy-based musical game.
To use the device, you simply put the MusicGlove on your hand, plug it into your personal laptop or Flint tablet, and press play.
Then, follow along and make the appropriate pinching movements when each musical note floats down the screen.
Exercise with MusicGlove has been clinically proven to:
How is it different?
Most assistive hand devices help open your hand but fail to retrain your brain how to use your hand again.
MusicGlove is unique because it’s designed to initiate neuroplasticity, the process that your brain uses to rewire itself after injury. The more you play the game, the better your brain becomes at controlling your hand!
To use MusicGlove hand therapy actively without assistance, you need the ability to touch your thumb to at least one of your fingertips or side of your index finger.
If you cannot make this movement, then you can try using the device passively. Read this article to learn more.
MusicGlove is intended to treat:
If you have received hand therapy in clinic and want to continue at home, MusicGlove is for you!
If so, please visit our MusicGlove for Clinic Use page!
Visit Site —> MusicGlove for Stroke Therapy – Flint Rehab