Effect of Sensory Feedback from the Proximal Upper Limb on Voluntary Isometric Finger Flexion and Extension in Hemiparetic Stroke Subjects

This study investigated the potential influence of proximal sensory feedback on voluntary distal motor activity in the paretic upper limb of hemiparetic stroke survivors and the potential effect of voluntary distal motor activity on proximal muscle activity. Ten stroke subjects and 10 neurologically intact control subjects performed maximum voluntary isometric flexion and extension, respectively, at the metacarpophalangeal (MCP) joints of the fingers in two static arm postures and under three conditions of electrical stimulation of the arm. The tasks were quantified in terms of maximum MCP torque [MCP flexion (MCPflex) or MCP extension (MCPext)] and activity of targeted (flexor digitorum superficialis or extensor digitorum communis) and nontargeted upper limb muscles. From a previous study on the MCP stretch reflex poststroke, we expected stroke subjects to exhibit a modulation of voluntary MCP torque production by arm posture and electrical stimulation and increased nontargeted muscle activity. Posture 1 (flexed elbow, neutral shoulder) led to greater MCPflex in stroke subjects than posture 2 (extended elbow, flexed shoulder). Electrical stimulation did not influence MCPflex or MCPext in either subject group. In stroke subjects, posture 1 led to greater nontargeted upper limb flexor activity during MCPflex and to greater elbow flexor and extensor activity during MCPext. Stroke subjects exhibited greater elbow flexor activity during MCPflex and greater elbow flexor and extensor activity during MCPext than control subjects. The results suggest that static arm posture can modulate voluntary distal motor activity and accompanying muscle activity in the paretic upper limb poststroke

Design considerations for a wearable monitor to measure finger posture

BACKGROUND: Objective measures of hand function as individuals participate in home and community activities are needed in order to better plan and evaluate rehabilitation treatments. Traditional measures collected in the clinical setting are often not reflective of actual functional performance. Recent advances in technology, however, enable the development of a lightweight, comfortable data collection monitor to measure hand kinematics. METHODS: This paper presents the design analysis of a wearable sensor glove with a specific focus on the sensors selected to measure bend. The most important requirement for the glove is easy donning and removal for individuals with significantly reduced range of motion in the hands and fingers. Additional requirements include comfort and durability, cost effectiveness, and measurement repeatability. These requirements eliminate existing measurement gloves from consideration. Glove construction is introduced, and the sensor selection and glove evaluation process are presented. RESULTS: Evaluation of commercial bend sensors shows that although most are not appropriate for repeatable measurements of finger flexion, one has been successfully identified. A case study for sensor glove repeatability using the final glove configuration and sensors does show a high degree of repeatability in both the gripped and flat hand positions (average coefficient of variability = 2.96% and 0.10%, respectively). CONCLUSION: Measuring functional outcomes in a portable manner can provide a wealth of information important to clinicians for the evaluation and treatment of movement disorders in the hand and fingers. This device is an important step in that direction as both a research and an evaluation method

Modulation of Stretch Reflexes of the Finger Flexors by Sensory Feedback from the Proximal Upper Limb Poststroke

Neural coupling of proximal and distal upper limb segments may have functional implications in the recovery of hemiparesis after stroke. The goal of the present study was to investigate whether the stretch reflex response magnitude of spastic finger flexor muscles poststroke is influenced by sensory input from the shoulder and the elbow and whether reflex coupling of muscles throughout the upper limb is altered in spastic stroke survivors. Through imposed extension of the metacarpophalangeal (MCP) joints, stretch of the relaxed finger flexors of the four fingers was imposed in 10 relaxed stroke subjects under different conditions of proximal sensory input, namely static arm posture (3 different shoulder/elbow postures) and electrical stimulation (surface stimulation of biceps brachii or triceps brachii, or none). Fast (300°/s) imposed stretch elicited stretch reflex flexion torque at the MCP joints and reflex electromyographic (EMG) activity in flexor digitorum superficialis. Both measures were greatest in an arm posture of 90° of elbow flexion and neutral shoulder position. Biceps stimulation resulted in greater MCP stretch reflex flexion torque. Fast imposed stretch also elicited reflex EMG activity in nonstretched heteronymous upper limb muscles, both proximal and distal. These results suggest that in the spastic hemiparetic upper limb poststroke, sensorimotor coupling of proximal and distal upper limb segments is involved in both the increased stretch reflex response of the finger flexors and an increased reflex coupling of heteronymous muscles. Both phenomena may be mediated through changes poststroke in the spinal reflex circuits and/or in the descending influence of supraspinal pathways

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

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

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

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

Survivors of Chronic Stroke Experience Continued Impairment of Dexterity But Not Strength in the Nonparetic Upper Limb

Objective To investigate the performance of the less affected upper limb in people with stroke compared with normative values. To examine less affected upper limb function in those whose prestroke dominant limb became paretic and those whose prestroke nondominant limb became paretic. Design Cohort study of survivors of chronic stroke (7.2±6.7y post incident). Setting The study was performed at a freestanding academic rehabilitation hospital. Participants Survivors of chronic stroke (N=40) with severe hand impairment (Chedoke-McMaster Stroke Assessment rating of 2-3 on Stage of Hand) participated in the study. In 20 participants the prestroke dominant hand (DH) was tested (nondominant hand [NH] affected by stroke), and in 20 participants the prestroke NH was tested (DH affected by stroke). Interventions Not applicable. Main Outcome Measure Jebsen-Taylor Hand Function Test. Data from survivors of stroke were compared with normative age- and sex-matched data from neurologically intact individuals. Results When combined, DH and NH groups performed significantly worse on fine motor tasks with their nonparetic hand relative to normative data (PP\u3e.140). Conclusions Survivors of stroke with severe impairment of the paretic limb continue to present significant upper extremity impairment in their nominally nonparetic limb even years after stroke. This phenomenon was observed regardless of whether the DH or NH hand was primarily affected. Because this group of survivors of stroke is especially dependent on the nonparetic limb for performing functional tasks, our results suggest that the nonparetic upper limb should be targeted for rehabilitation

Development of a 3D, networked multi-user virtual reality environment for home therapy after stroke

Abstract Background Impairment of upper extremity function is a common outcome following stroke, to the detriment of lifestyle and employment opportunities. Yet, access to treatment may be limited due to geographical and transportation constraints, especially for those living in rural areas. While stroke rates are higher in these areas, stroke survivors in these regions of the country have substantially less access to clinical therapy. Home therapy could offer an important alternative to clinical treatment, but the inherent isolation and the monotony of self-directed training can greatly reduce compliance. Methods We developed a 3D, networked multi-user Virtual Environment for Rehabilitative Gaming Exercises (VERGE) system for home therapy. Within this environment, stroke survivors can interact with therapists and/or fellow stroke survivors in the same virtual space even though they may be physically remote. Each user’s own movement controls an avatar through kinematic measurements made with a low-cost, Kinect™ device. The system was explicitly designed to train movements important to rehabilitation and to provide real-time feedback of performance to users and clinicians. To obtain user feedback about the system, 15 stroke survivors with chronic upper extremity hemiparesis participated in a multisession pilot evaluation study, consisting of a three-week intervention in a laboratory setting. For each week, the participant performed three one-hour training sessions with one of three modalities: 1) VERGE system, 2) an existing virtual reality environment based on Alice in Wonderland (AWVR), or 3) a home exercise program (HEP). Results Over 85% of the subjects found the VERGE system to be an effective means of promoting repetitive practice of arm movement. Arm displacement averaged 350 m for each VERGE training session. Arm displacement was not significantly less when using VERGE than when using AWVR or HEP. Participants were split on preference for VERGE, AWVR or HEP. Importantly, almost all subjects indicated a willingness to perform the training for at least 2–3 days per week at home. Conclusions Multi-user VR environments hold promise for home therapy, although the importance of reducing complexity of operation for the user in the VR system must be emphasized. A modified version of the VERGE system is currently being used in a home therapy study