Use of computational modeling to examine fingertip force production in children with hemiplegic cerebral palsy.

Most children with hemiplegic cerebral palsy (HCP), one of the most prevalent subtypes of cerebral palsy, struggle with grasping and manipulating objects. This impairment may arise from a diminished capacity to properly direct forces created with the finger pad due to aberrant force application. Children with HCP were asked to create maximal force with the index finger pad in the palmar (normal) direction with both the paretic and non-paretic hands. The resulting forces and finger postures were then applied to a computational musculoskeletal model of the hand to estimate the corresponding muscle activation patterns. Subjects tended to create greater shear force relative to normal force with the paretic hand (p < 0.05). The resultant force was directed 33.6°±10.8° away from the instructed palmar direction in the paretic hand, but only 8.0°±7.3° in the non-paretic hand. Additionally, participants created greater palmar force with the non-paretic hand than with the paretic hand (p < 0.05). These differences in force production are likely due to differences in muscle activation pattern, as our computational models showed differences in which muscles are active and their relative activations when recreating the measured force vectors for the two hands (p < 0.01). The models predicted reduced activation in the extrinsic and greater reductions in activation in the intrinsic finger muscles, potentially due to reduced voluntary activation or muscle atrophy. As the large shear forces could lead to objects slipping from grasp, muscle activation patterns may provide an important target for therapeutic treatment in children with HCP.

Effects of wrist posture and stabilization on precision grip force production and muscle activation patterns.

Most of the power for generating forces in the fingers arises from muscles located in the forearm. This configuration maximizes finger joint range of motion while minimizing finger mass and inertia. The resulting multiarticular arrangement of the tendons, however, complicates independent control of the wrist and the digits. Actuating the wrist impacts sensorimotor control of the fingers and vice versa. The goal of this study was to systematically investigate interactions between isometric wrist and digit control. Specifically, we examined how the need to maintain a specified wrist posture influences precision grip. Fifteen healthy adults produced maximum precision grip force at 11 different wrist flexion/extension angles, with the arm supported, under two conditions: 1) the participant maintained the desired wrist angle while performing the precision grip and 2) a robot maintained the specified wrist angle. Wrist flexion/extension posture significantly impacted maximum precision grip force (P < 0.001), with the greatest grip force achieved when the wrist was extended 30° from neutral. External wrist stabilization by the robot led to a 20% increase in precision grip force across wrist postures. Increased force was accompanied by increased muscle activation but with an activation pattern similar to the one used when the participant had to stabilize their wrist. Thus, simultaneous wrist and finger requirements impacted performance of an isometric finger task. External wrist stabilization can promote increased precision grip force resulting from increased muscle activation. These findings have potential clinical significance for individuals with neurologically driven finger weakness, such as stroke survivors.NEW & NOTEWORTHY We explored the interdependence between wrist and fingers by assessing the influence of wrist posture and external stabilization on precision grip force generation. We found that maximum precision grip force occurred at an extended wrist posture and was 20% greater when the wrist was Externally Stabilized. The latter resulted from amplification of muscle activation patterns from the Self-Stabilized condition rather than adoption of new patterns exploiting external wrist stabilization.

Cosimulation of the index finger extensor apparatus with finite element and musculoskeletal models.

Musculoskeletal modeling has been effective for simulating dexterity and exploring the consequences of disability. While previous approaches have examined motor function using multibody dynamics, existing musculoskeletal models of the hand and fingers have difficulty simulating soft tissue such as the extensor mechanism of the fingers, which remains underexplored. To investigate the extensor mechanism and its impact on finger motor function, we developed a finite element model of the index finger extensor mechanism and a cosimulation method that combines the finite element model with a multibody dynamic model. The finite element model and cosimulation were validated through comparison with experimentally derived tissue strains and fingertip endpoint forces respectively. Tissue strains predicted by the finite element model were consistent with the experimentally observed strains of the 9 postures tested in cadaver specimens. Fingertip endpoint forces predicted using the cosimulation were well aligned in both force (difference within 0.60 N) and direction (difference within 30°with experimental results. Sensitivity of the extensor mechanism to changes in modulus and adhesion configuration were evaluated for ± 50% of experimental moduli, presence of the radial and ulnar adhesions, and joint capsule. Simulated strains and endpoint forces were found to be minimally sensitive to alterations in moduli and adhesions. These results are promising and demonstrate the ability of the cosimulation to predict global behavior of the extensor mechanism, while enabling measurement of stresses and strains within the structure itself. This model could be used in the future to predict the outcomes for different surgical repairs of the extensor mechanism.

Robust neural decoding for dexterous control of robotic hand kinematics.

BACKGROUND: Manual dexterity is a fundamental motor skill that allows us to perform complex daily tasks. Neuromuscular injuries, however, can lead to the loss of hand dexterity. Although numerous advanced assistive robotic hands have been developed, we still lack dexterous and continuous control of multiple degrees of freedom in real-time. In this study, we developed an efficient and robust neural decoding approach that can continuously decode intended finger dynamic movements for real-time control of a prosthetic hand. METHODS: High-density electromyogram (HD-EMG) signals were obtained from the extrinsic finger flexor and extensor muscles, while participants performed either single-finger or multi-finger flexion-extension movements. We implemented a deep learning-based neural network approach to learn the mapping from HD-EMG features to finger-specific population motoneuron firing frequency (i.e., neural-drive signals). The neural-drive signals reflected motor commands specific to individual fingers. The predicted neural-drive signals were then used to continuously control the fingers (index, middle, and ring) of a prosthetic hand in real-time. RESULTS: Our developed neural-drive decoder could consistently and accurately predict joint angles with significantly lower prediction errors across single-finger and multi-finger tasks, compared with a deep learning model directly trained on finger force signals and the conventional EMG-amplitude estimate. The decoder performance was stable over time and was robust to variations of the EMG signals. The decoder also demonstrated a substantially better finger separation with minimal predicted error of joint angle in the unintended fingers. CONCLUSIONS: This neural decoding technique offers a novel and efficient neural-machine interface that can consistently predict robotic finger kinematics with high accuracy, which can enable dexterous control of assistive robotic hands.

Concurrent and Continuous Prediction of Finger Kinetics and Kinematics via Motoneuron Activities.

OBJECTIVE: Robust neural decoding of intended motor output is crucial to enable intuitive control of assistive devices, such as robotic hands, to perform daily tasks. Few existing neural decoders can predict kinetic and kinematic variables simultaneously. The current study developed a continuous neural decoding approach that can concurrently predict fingertip forces and joint angles of multiple fingers. METHODS: We obtained motoneuron firing activities by decomposing high-density electromyogram (HD EMG) signals of the extrinsic finger muscles. The identified motoneurons were first grouped and then refined specific to each finger (index or middle) and task (finger force and dynamic movement) combination. The refined motoneuron groups (separate matrix) were then applied directly to new EMG data in real-time involving both finger force and dynamic movement tasks produced by both fingers. EMG-amplitude-based prediction was also performed as a comparison. RESULTS: We found that the newly developed decoding approach outperformed the EMG-amplitude method for both finger force and joint angle estimations with a lower prediction error (Force: 3.47±0.43 vs 6.64±0.69% MVC, Joint Angle: 5.40±0.50° vs 12.8±0.65°) and a higher correlation (Force: 0.75±0.02 vs 0.66±0.05, Joint Angle: 0.94±0.01 vs 0.5±0.05) between the estimated and recorded motor output. The performance was also consistent for both fingers. CONCLUSION: The developed neural decoding algorithm allowed us to accurately and concurrently predict finger forces and joint angles of multiple fingers in real-time. SIGNIFICANCE: Our approach can enable intuitive interactions with assistive robotic hands, and allow the performance of dexterous hand skills involving both force control tasks and dynamic movement control tasks.

Exploring Kinetic and Kinematic Finger Individuation Capability in Children With Hemiplegic Cerebral Palsy.

While fine manual dexterity develops over time, the extent to which children show independent control of their digits in each hand and the impact of perinatal brain injury on this individuation have not been well quantified. Our goal in this study was to assess and compare finger force and movement individuation in 8-14 year old children with hemiplegic cerebral palsy (hCP; n = 4) and their typically developing peers (TD; n = 10). We evaluated finger force individuation with five independent load cells and captured joint movement individuation with video tracking. We observed no significant differences in individuation indices between the dominant and non-dominant hands of TD children, but individuated force and movement were substantially reduced in the paretic versus non paretic hands of children with hCP (p < 0.001). In TD participants, the thumb tended to have the greatest level of independent control. This small sample of children with hCP showed substantial loss of individuation in the paretic hand and some deficits in the non-paretic hand, suggesting possible benefit from targeted training of digit independence in both hands for children with CP.

Abnormal proximal-distal interactions in upper-limb of stroke survivors during object manipulation: A pilot study.

Despite its importance, abnormal interactions between the proximal and distal upper extremity muscles of stroke survivors and their impact on functional task performance has not been well described, due in part to the complexity of upper extremity tasks. In this pilot study, we elucidated proximal-distal interactions and their functional impact on stroke survivors by quantitatively delineating how hand and arm movements affect each other across different phases of functional task performance, and how these interactions are influenced by stroke. Fourteen subjects, including nine chronic stroke survivors and five neurologically-intact subjects participated in an experiment involving transport and release of cylindrical objects between locations requiring distinct proximal kinematics. Distal kinematics of stroke survivors, particularly hand opening, were significantly affected by the proximal kinematics, as the hand aperture decreased and the duration of hand opening increased at the locations that requires shoulder abduction and elbow extension. Cocontraction of the extrinsic hand muscles of stroke survivors significantly increased at these locations, where an increase in the intermuscular coherence between distal and proximal muscles was observed. Proximal kinematics of stroke survivors was also affected by the finger extension, but the cocontraction of their proximal muscles did not significantly increase, suggesting the changes in the proximal kinematics were made voluntarily. Our results showed significant proximal-to-distal interactions between finger extension and elbow extension/shoulder abduction of stroke survivors exist during their functional movements. Increased cocontraction of the hand muscles due to increased neural couplings between the distal and proximal muscles appears to be the underlying mechanism.

Use of cyproheptadine hydrochloride (HCl) to reduce neuromuscular hypertonicity in stroke survivors: A Randomized Trial: Reducing Hypertonicity in Stroke.

OBJECTIVES: The goal of this study was to examine how the administration and dosing of the anti-serotonergic medication cyproheptadine hydrochloride (HCl) affects involuntary muscle hypertonicity of the spastic and paretic hands of stroke survivors. MATERIALS AND METHODS: A randomized, double-blinded, placebo-controlled longitudinal intervention study was performed as a component of a larger clinical trial. 94 stroke survivors with chronic, severe hand impairment, rated as levels 2 or 3 on the Chedoke-McMaster Stroke Assessment Stage of Hand (CMSA-H), were block randomized to groups receiving doses of cyproheptadine HCl or matched doses of placebo. Doses were increased from 4 mg BID to 8 mg TID over 3 weeks. Outcomes were assessed at baseline and after each of the three weeks of intervention. Primary outcome measure was grip termination time; other measures included muscle strength, spasticity, coactivation of the long finger flexors, and recording of potential adverse effects such as sleepiness and depression. RESULTS: 89 participants (receiving cyproheptadine HCl: 44, receiving placebo: 45) completed the study. The Cyproheptadine group displayed significant reduction in grip termination time, in comparison with the Placebo group (p<0.05). Significant change in the Cyproheptadine group (45% time reduction) was observed after only one week at the 4mg BID dosage. The effect was pronounced for those participants in the Cyproheptadine group with more severe hand impairment (CMSA-H level 2) at baseline. Conversely, no significant effect of Group * Session interaction was observed for spasticity (p=0.6) or coactivation (p=0.53). There were no significant changes in strength (p=0.234) or depression (p=0.441) during the trial. CONCLUSIONS: Use of cyproheptadine HCl was associated with a significant reduction in relaxation time of finger flexor muscles, without adversely affecting voluntary strength, although spasticity and coactivation were unchanged. Decreasing the duration of involuntary flexor activity can facilitate object release and repeated prehensile task performance. REGISTRATION: Clinical Trial number: NCT02418949.

Sensitivity analysis guided improvement of an electromyogram-driven lumped parameter musculoskeletal hand model.

EMG-driven neuromusculoskeletal models have been used to study many impairments and hold great potential to facilitate human-machine interactions for rehabilitation. A challenge to successful clinical application is the need to optimize the model parameters to produce accurate kinematic predictions. In order to identify the key parameters, we used Monte-Carlo simulations to evaluate the sensitivities of wrist and metacarpophalangeal (MCP) flexion/extension prediction accuracies for an EMG-driven, lumped-parameter musculoskeletal model. Four muscles were modeled with 22 total optimizable parameters. Model predictions from EMG were compared with measured joint angles from 11 able-bodied subjects. While sensitivities varied by muscle, we determined muscle moment arms, maximum isometric force, and tendon slack length were highly influential, while passive stiffness and optimal fiber length were less influential. Removing the two least influential parameters from each muscle reduced the optimization search space from 22 to 14 parameters without significantly impacting prediction correlation (wrist: 0.90 ± 0.05 vs 0.90 ± 0.05, p = 0.96; MCP: 0.74 ± 0.20 vs 0.70 ± 0.23, p = 0.51) and normalized root mean square error (wrist: 0.18 ± 0.03 vs 0.19 ± 0.03, p = 0.16; MCP: 0.18 ± 0.06 vs 0.19 ± 0.06, p = 0.60). Additionally, we showed that wrist kinematic predictions were insensitive to parameters of the modeled MCP muscles. This allowed us to develop a novel optimization strategy that more reliably identified the optimal set of parameters for each subject (27.3 ± 19.5%) compared to the baseline optimization strategy (6.4 ± 8.1%; p = 0.004). This study demonstrated how sensitivity analyses can be used to guide model refinement and inform novel and improved optimization strategies, facilitating implementation of musculoskeletal models for clinical applications.

Noninvasively recorded high-gamma signals improve synchrony of force feedback in a novel neurorehabilitation brain-machine interface for brain injury.

Objective.Brain injury is the leading cause of long-term disability worldwide, often resulting in impaired hand function. Brain-machine interfaces (BMIs) offer a potential way to improve hand function. BMIs often target replacing lost function, but may also be employed in neurorehabilitation (nrBMI) by facilitating neural plasticity and functional recovery. Here, we report a novel nrBMI capable of acquiring high-γ(70-115 Hz) information through a unique post-traumatic brain injury (TBI) hemicraniectomy window model, and delivering sensory feedback that is synchronized with, and proportional to, intended grasp force.Approach. We developed the nrBMI to use electroencephalogram recorded over a hemicraniectomy (hEEG) in individuals with TBI. The nrBMI empowered users to exert continuous, proportional control of applied force, and provided continuous force feedback. We report the results of an initial testing group of three human participants with TBI, along with a control group of three skull- and motor-intact volunteers.Main results. All participants controlled the nrBMI successfully, with high initial success rates (2 of 6 participants) or performance that improved over time (4 of 6 participants). We observed high-γmodulation with force intent in hEEG but not skull-intact EEG. Most significantly, we found that high-γcontrol significantly improved the timing synchronization between neural modulation onset and nrBMI output/haptic feedback (compared to low-frequency nrBMI control).Significance. These proof-of-concept results show that high-γnrBMIs can be used by individuals with impaired ability to control force (without immediately resorting to invasive signals like electrocorticography). Of note, the nrBMI includes a parameter to change the fraction of control shared between decoded intent and volitional force, to adjust for recovery progress. The improved synchrony between neural modulations and force control for high-γsignals is potentially important for maximizing the ability of nrBMIs to induce plasticity in neural circuits. Inducing plasticity is critical to functional recovery after brain injury.

Effect of novel training to normalize altered finger force direction post-stroke: study protocol for a double-blind randomized controlled trial.

BACKGROUND: Functional task performance requires proper control of both movement and force generation in three-dimensional space, especially for the hand. Control of force in three dimensions, however, is not explicitly treated in current physical rehabilitation. To address this gap in treatment, we have developed a tool to provide visual feedback on three-dimensional finger force. Our objective is to examine the effectiveness of training with this tool to restore hand function in stroke survivors. METHODS: Double-blind randomized controlled trial. All participants undergo 18 1-h training sessions to practice generating volitional finger force of various target directions and magnitudes. The experimental group receives feedback on both force direction and magnitude, while the control group receives feedback on force magnitude only. The primary outcome is hand function as measured by the Action Research Arm Test. Other outcomes include the Box and Block Test, Stroke Impact Scale, ability to direct finger force, muscle activation pattern, and qualitative interviews. DISCUSSION: The protocol for this clinical trial is described in detail. The results of this study will reveal whether explicit training of finger force direction in stroke survivors leads to improved motor control of the hand. This study will also improve the understanding of neuromuscular mechanisms underlying the recovery of hand function. TRIAL REGISTRATION: ClinicalTrials.gov NCT03995069 . Registered on June 21, 2019.

A generic neural network model to estimate populational neural activity for robust neural decoding.

BACKGROUND: Robust and continuous neural decoding is crucial for reliable and intuitive neural-machine interactions. This study developed a novel generic neural network model that can continuously predict finger forces based on decoded populational motoneuron firing activities. METHOD: We implemented convolutional neural networks (CNNs) to learn the mapping from high-density electromyogram (HD-EMG) signals of forearm muscles to populational motoneuron firing frequency. We first extracted the spatiotemporal features of EMG energy and frequency maps to improve learning efficiency, given that EMG signals are intrinsically stochastic. We then established a generic neural network model by training on the populational neuron firing activities of multiple participants. Using a regression model, we continuously predicted individual finger forces in real-time. We compared the force prediction performance with two state-of-the-art approaches: a neuron-decomposition method and a classic EMG-amplitude method. RESULTS: Our results showed that the generic CNN model outperformed the subject-specific neuron-decomposition method and the EMG-amplitude method, as demonstrated by a higher correlation coefficient between the measured and predicted forces, and a lower force prediction error. In addition, the CNN model revealed more stable force prediction performance over time. CONCLUSIONS: Overall, our approach provides a generic and efficient continuous neural decoding approach for real-time and robust human-robot interactions.

Use of an EMG-Controlled Game as a Therapeutic Tool to Retrain Hand Muscle Activation Patterns Following Stroke: A Pilot Study.

BACKGROUND/PURPOSE: To determine the feasibility of training with electromyographically (EMG) controlled games to improve control of muscle activation patterns in stroke survivors. METHODS: Twenty chronic stroke survivors (>6 months) with moderate hand impairment were randomized to train either unilaterally (paretic only) or bilaterally over 9 one-hour training sessions. EMG signals from the unilateral or bilateral limbs controlled a cursor location on a computer screen for gameplay. The EMG muscle activation vector was projected onto the plane defined by the first 2 principal components of the activation workspace for the nonparetic hand. These principal components formed the x- and y-axes of the computer screen. RESULTS: The recruitment goal (n = 20) was met over 9 months, with no screen failure, no attrition, and 97.8% adherence rate. After training, both groups significantly decreased the time to move the cursor to a novel sequence of targets (P = 0.006) by reducing normalized path length of the cursor movement (P = 0.005), and improved the Wolf Motor Function Test (WMFT) quality score (P = 0.01). No significant group difference was observed. No significant change was seen in the WMFT time or Box and Block Test. DISCUSSION/CONCLUSIONS: Stroke survivors could successfully use the EMG-controlled games to train control of muscle activation patterns. While the nonparetic limb EMG was used in this study to create target EMG patterns, the system supports various means for creating target patterns per user desires. Future studies will employ training with the EMG-controlled games in conjunction with functional task practice for a longer intervention duration to improve overall hand function.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A379).

Offline Evaluation Matters: Investigation of the Influence of Offline Performance on Real-time Operation of Electromyography-based Neural-Machine Interfaces.

There has been a debate on the most appropriate way to evaluate electromyography (EMG)-based neural-machine interfaces (NMIs). Accordingly, this study examined whether a relationship between offline kinematic predictive accuracy (R2) and user real-time task performance while using the interface could be identified. A virtual posture-matching task was developed to evaluate motion capture-based control and myoelectric control with artificial neural networks (ANNs) trained to low (R2 ≈ 0.4), moderate (R2 ≈ 0.6), and high (R2 ≈ 0.8) offline performance levels. Twelve able-bodied subjects trained with each offline performance level decoder before evaluating final real-time posture matching performance. Moderate to strong relationships were detected between offline performance and all real-time task performance metrics: task completion percentage (r=0.66, p<0.001), normalized task completion time (r = -0.51, p = 0.001), path efficiency (r = 0.74, p < 0.001), and target overshoots (r = -0.79, p < 0.001). Significant improvements in each real-time task evaluation metric were also observed between the different offline performance levels. Additionally, subjects rated myoelectric controllers with higher offline performance more favorably. The results of this study support the use and validity of offline analyses for optimization of NMIs in myoelectric control research and development.

Characteristics of the severely impaired hand in survivors of stroke with chronic impairments.

BACKGROUND: Diminished sensorimotor control of the hand is one of the most common outcomes following stroke. This hand impairment substantially impacts overall function and quality of life; standard therapy often results in limited improvement. Mechanisms of dysfunction of the severely impaired post-stroke hand are still incompletely understood, thereby impeding the development of new targeted treatments. OBJECTIVE: To identify and determine potential relationships among the mechanisms responsible for hand impairment following stroke. METHODS: This cohort study observed stroke survivors (n = 95) with severe, chronic hand impairment (Chedoke-McMaster Hand score = 2-3). Custom instrumentation created precise perturbations and measured kinematic responses. Muscle activation was recorded through electromyography. Strength, spasticity, muscle relaxation time, and muscle coactivation were quantified. RESULTS: Maximum grip strength in the paretic hand was only 12% of that achieved by the nonparetic hand, and only 6 of 95 participants were able to produce any net extension force. Despite force deficits, spastic reflex response of the finger flexor evoked by imposed stretch averaged 90.1 ± 26.8% of maximum voluntary activation, relaxation time averaged 3.8 ± 0.8 seconds, and coactivation during voluntary extension exceeded 30% of maximum contraction, thereby resulting in substantial net flexion. Surprisingly, these hypertonicity measures were not significantly correlated with each other. CONCLUSIONS: Survivors of severe, chronic hemiparetic stroke experience profound weakness of both flexion and extension that arises from increased involuntary antagonist activation and decreased voluntary activation. The lack of correlation amongst hypertonicity measures suggests that these phenomena may arise from multiple, potentially independent mechanisms that could require different treatments.

Hand function development of children with hemiplegic cerebral palsy: A scoping review.

PURPOSE: Hemiplegic cerebral palsy (hCP) typically impacts sensorimotor control of the hand, but comprehensive assessments of the hands of children with hCP are relatively rare. This scoping review summarizes the development of hand function for children with hCP. METHODS: This scoping review focused on the development of hand function in children with hCP. Electronic databases (PubMed, PEDro, Web of Science, CINAHL, and SpringerLink) were searched to identify studies assessing hand function in children with hCP. The search was performed using keywords (e.g., "hemiplegia"). An iterative approach verified by two authors was used to select the studies. Articles which reported quantitative data for children with hCP on any items of a specified set of hand evaluations were included. Measures were sorted into three categories: quantitative neuromechanics, clinical assessments, and clinical functional evaluations. RESULTS: Initial searches returned 1536 articles, 131 of which were included in the final review. Trends between assessment scores and age were examined for both hands. CONCLUSION: While several studies have evaluated hand function in children with hCP, the majority relied on clinical scales, assessments, or qualitative descriptions. Further assessments of kinematics, kinetics, and muscle activation patterns are needed to identify the underlying impairment mechanisms that should be targeted for treatment.

High Compliance Pneumatic Actuators to Promote Finger Extension in Stroke Survivors.

Compliant pneumatic systems are well suited for wearable robotic applications. The actuators are lightweight, conformable to irregular shapes, and tolerant of uncontrolled degrees of freedom. These attributes are especially desirable for hand exoskeletons given their space and mass constraints. Creating active digit extension with these exoskeletons is especially critical for clinical populations such as stroke survivors who often have great difficulty opening their paretic hand. To achieve active digit extension with a soft actuator, we have created pneumatic chambers that lie along the palmar surface of the digits. These chambers can directly extend the digits when pressurized. We present a characterization of the extension force and passive flexion resistance generated by these pneumatic chambers across a range of joint angles as a function of cross-sectional shape, dimension, and wall thickness. The chambers were fabricated out of DragonSkin 20 using custom molds and were tested on a custom jig. Extension forces created at the end of the chamber (where fingertip contact would occur) exceeded 3.00 N at relatively low pressure (48.3 kPa). A rectangular cross-section generated higher extension force than a semi-obround cross-sectional shape. Extension force was significantly higher (p < 0.05) for actuators with the highest wall thickness compared to those with the thinnest walls. In comparison to previously used polyurethane actuators, the DragonSkin actuators had a much higher extension force for a similar passive bending resistance. Passive bending resistance of the chamber (simulating finger flexion) did not vary significantly with actuator shape, wall thickness, width, or depth. The flexion resistance, however, could be significantly reduced by applying a vacuum. These results provide guidance in designing pneumatic actuators for assisting finger extension and resisting unwanted flexion in the fingers.

A Hand Exoskeleton for Stroke Survivors' Activities of Daily Life.

Stroke is a leading cause of disability in the U.S. Hand impairment is a common consequence of stroke, potentially impacting all facets of life as the hands are the primary means of interacting with the world. Typically, therapy is the prescribed treatment after stroke. However, a majority of stroke survivors have limited recovery and thus chronic impairment. Assistive, rather than therapeutic, devices may help these individuals restore lost function and improve independence and engagement in society. Current assistive devices, however, typically fail to address the greatest barriers to successful use with stroke survivors. In the hand, weakness and incoordination arise from a seemingly paradoxical combination of limited voluntary activation of muscles and involuntary neuromuscular hyperexcitability. Thus, profound strength deficits can be accompanied by substantial forces opposing the intended movement. The assistive device presented in this paper can provide both sufficient flexion and extension assistance to overcome these barriers. A single actuator for each digit provides flexion or extension assistance through push-pull cables guided along the dorsal side of the hand. User intent can be decoded from Electromyographic (EMG) signals to drive the device throughout the movement. EMG control is customized to the capabilities of each user by examining the voluntary EMG workspace.

Automatic Detection of Contracting Muscle Regions via the Deformation Field of Transverse Ultrasound Images: A Feasibility Study.

Accurate identification of contracting muscles can help us to understand the muscle function in both physiological and pathological conditions. Conventional electromyography (EMG) have limited access to deep muscles, crosstalk, or instability in the recordings. Accordingly, a novel framework was developed to detect contracting muscle regions based on the deformation field of transverse ultrasound images. We first estimated the muscle movements in a stepwise calculation, to derive the deformation field. We then calculated the divergence of the deformation field to locate the expanding or shrinking regions during muscle contractions. Two preliminary experiments were performed to evaluate the feasibility of the developed algorithm. Using concurrent intramuscular EMG recordings, Experiment I verified that the divergence map can capture the activity of superficial and deep muscles, when muscles were activated voluntarily or through electrical stimulation. Experiment II verified that the divergence map can only capture contracting muscles but not muscle shortening during passive movements. The results demonstrated that the divergence can individually capture the activity of muscles at different depths, and was not sensitive to muscle shortening during passive movements. The proposed framework can automatically detect the regions of contracting muscle, and could potentially serve as a tool to assess the functions of a group of muscles concurrently.