Responsiveness to exoskeleton loading during bimanual reaching is associated with corticospinal tract integrity in stroke.

Background: Device-based rehabilitation of upper extremity impairment following stroke often employs one-sized-fits-all approaches that do not account for individual differences in patient characteristics. Objective: Determine if corticospinal tract lesion load could explain individual differences in the responsiveness to exoskeleton loading of the arms in chronic stroke participants. Methods: Fourteen stroke participants performed a bimanual shared cursor reaching task in virtual reality while exoskeletons decreased the effective weight of the more-impaired arm and increased the effective weight of the less-impaired arm. We calculated the change in relative displacement between the arms (RC) and the change in relative muscle activity (MC) between the arms from the biceps and deltoids. We calculated corticospinal tract lesion load (wCSTLL) in a subset of 10 participants. Results: Exoskeleton loading did not change RC (p = 0.07) or MC (p = 0.47) at the group level, but significant individual differences emerged. Participants with little overlap between the lesion and corticospinal tract responded to loading by decreasing muscle activity in the more-impaired arm relative to the less-impaired arm. The change in deltoid MC was associated with smaller wCSTLL (R2 = 0.43, p = 0.039); there was no such relationship for biceps MC (R2 < 0.001, p = 0.98). Conclusion: Here we provide evidence that corticospinal tract integrity is a critical feature that determines one's ability to respond to upper extremity exoskeleton loading. Our work contributes to the development of personalized device-based interventions that would allow clinicians and researchers to titrate constraint levels during bimanual activities.

Concurrent validity of machine learning-classified functional upper extremity use from accelerometry in chronic stroke.

Objective: This study aims to investigate the validity of machine learning-derived amount of real-world functional upper extremity (UE) use in individuals with stroke. We hypothesized that machine learning classification of wrist-worn accelerometry will be as accurate as frame-by-frame video labeling (ground truth). A second objective was to validate the machine learning classification against measures of impairment, function, dexterity, and self-reported UE use. Design: Cross-sectional and convenience sampling. Setting: Outpatient rehabilitation. Participants: Individuals (>18 years) with neuroimaging-confirmed ischemic or hemorrhagic stroke >6-months prior (n = 31) with persistent impairment of the hemiparetic arm and upper extremity Fugl-Meyer (UEFM) score = 12-57. Methods: Participants wore an accelerometer on each arm and were video recorded while completing an "activity script" comprising activities and instrumental activities of daily living in a simulated apartment in outpatient rehabilitation. The video was annotated to determine the ground-truth amount of functional UE use. Main outcome measures: The amount of real-world UE use was estimated using a random forest classifier trained on the accelerometry data. UE motor function was measured with the Action Research Arm Test (ARAT), UEFM, and nine-hole peg test (9HPT). The amount of real-world UE use was measured using the Motor Activity Log (MAL). Results: The machine learning estimated use ratio was significantly correlated with the use ratio derived from video annotation, ARAT, UEFM, 9HPT, and to a lesser extent, MAL. Bland-Altman plots showed excellent agreement between use ratios calculated from video-annotated and machine-learning classification. Factor analysis showed that machine learning use ratios capture the same construct as ARAT, UEFM, 9HPT, and MAL and explain 83% of the variance in UE motor performance. Conclusion: Our machine learning approach provides a valid measure of functional UE use. The accuracy, validity, and small footprint of this machine learning approach makes it feasible for measurement of UE recovery in stroke rehabilitation trials.

Measurement of Functional Use in Upper Extremity Prosthetic Devices Using Wearable Sensors and Machine Learning.

Trials for therapies after an upper limb amputation (ULA) require a focus on the real-world use of the upper limb prosthesis. In this paper, we extend a novel method for identifying upper extremity functional and nonfunctional use to a new patient population: upper limb amputees. We videotaped five amputees and 10 controls performing a series of minimally structured activities while wearing sensors on both wrists that measured linear acceleration and angular velocity. The video data was annotated to provide ground truth for annotating the sensor data. Two different analysis methods were used: one that used fixed-size data chunks to create features to train a Random Forest classifier and one that used variable-size data chunks. For the amputees, the fixed-size data chunk method yielded good results, with 82.7% median accuracy (range of 79.3-85.8) on the 10-fold cross-validation intra-subject test and 69.8% in the leave-one-out inter-subject test (range of 61.4-72.8). The variable-size data method did not improve classifier accuracy compared to the fixed-size method. Our method shows promise for inexpensive and objective quantification of functional upper extremity (UE) use in amputees and furthers the case for use of this method in assessing the impact of UE rehabilitative treatments.

Evolution of a System to Monitor Infant Neuromotor Development in the Home: Lessons from COVID-19.

In the nine months leading up to COVID-19, our biomedical engineering research group was in the very early stages of development and in-home testing of HUGS, the Hand Use and Grasp Sensor (HUGS) system. HUGS was conceived as a tool to allay parents' anxiety by empowering them to monitor their infants' neuromotor development at home. System focus was on the evolving patterns of hand grasp and general upper extremity movement, over time, in the naturalistic environment of the home, through analysis of data captured from force-sensor-embedded toys and 3D video as the baby played. By the end of March, 2020, as the COVID-19 pandemic accelerated and global lockdown ensued, home visits were no longer possible and HUGS system testing ground to an abrupt halt. In the spring of 2021, still under lockdown, we were able to resume recruitment and in-home testing with HUGS-2, a system whose key requirement was that it be contactless. Participating families managed the set up and use of HUGS-2, supported by a detailed library of video materials and virtual interaction with the HUGS team for training and troubleshooting over Zoom. Like the positive/negative poles of experience reported by new parents under the isolation mandated to combat the pandemic, HUGS research was both impeded and accelerated by having to rely solely on distance interactions to support parents, troubleshoot equipment, and securely transmit data. The objective of this current report is to chronicle the evolution of HUGS. We describe a system whose design and development straddle the pre- and post-pandemic worlds of family-centered health technology design. We identify and classify the clinical approaches to infant screening that predominated in the pre-COVID-19 milieu and describe how these procedural frameworks relate to the family-centered conceptualization of HUGS. We describe how working exclusively through the proxy of parents revealed the family's priorities and goals for child interaction and surfaced HUGS design shortcomings that were not evident in researcher-managed, in-home testing prior to the pandemic.

Hand Use and Grasp Sensor System in Monitoring Infant Fine Motor Development.

Objective: To assess the feasibility of a hand use and grasp sensor system in collecting and quantifying fine motor development longitudinally in an infant's home environment. Design: Cohort study. Researchers made home visits monthly to participating families to collect grasp data from infants using a hand use and grasp sensor. Setting: Data collection were conducted in each participant's home. Participants: A convenience sample of 14 typical developmental infants were enrolled from 3 months to 9 months of age. Two infants dropped out. A total of 62 testing sessions involving 12 infants were available for analysis (N=12). Interventions: At each session, the infant was seated in a standardized infant seat. Each instrumented toy was hung on the hand use and grasp sensor structure, presented for 6 minutes in 3 feedback modes: visual, auditory, and vibratory. Main Outcome Measures: Infant grasp frequency and duration, peak grasping force, average grasping force, force coefficient of variation, and proportion of bimanual grasps. Results: A total of 2832 recorded grasp events from 12 infants were analyzed. In linear mixed-effects model analysis, when interacting with each toy, infants' peak grasp force, average grasp force, and accumulated grasp time all increased significantly with age (all P<.001). Bimanual grasps also occupied an increasingly greater percentage of infants' total grasps as they grew older (bar toy P<.001, candy toy P=.021). Conclusions: We observed significant changes in hand use and grasp sensor outcome measures with age that are consistent with maturation of grasp skills. We envision the evolution of hand use and grasp sensor technology into an inexpensive and convenient tool to track infant grasp development for early detection of possible developmental delay and/or cerebral palsy as a supplement to clinical evaluations.

A trade-off between kinematic and dynamic control of bimanual reaching in virtual reality.

Bimanual coordination is an essential component of human movement. Cooperative bimanual reaching tasks are widely used to assess the optimal control of goal-directed reaching. However, little is known about the neuromuscular mechanisms governing these tasks. Twelve healthy, right-handed participants performed a bimanual reaching task in a three-dimensional virtual reality environment. They controlled a shared cursor, located at the midpoint between the hands, and reached targets located at 80% of full arm extension. Following a baseline of normal reaches, we placed a wrist weight on one arm and measured the change in coordination. Relative contribution (RC) was computed as the displacement of the right hand divided by the sum of displacements of both hands. We used surface electromyography placed over the anterior deltoid and biceps brachii to compute muscle contribution (MC) from root mean squared muscle activity data. We found RC was no different than 50% during baseline, indicating participants reached equal displacements when no weights were applied. Participants systematically altered limb coordination in response to altered limb dynamics. RC increased by 0.91% and MC decreased by 5.3% relative to baseline when the weight was applied to the left arm; RC decreased by 0.94% and MC increased by 6.3% when the weight was applied to the right arm. Participants adopted an optimal control strategy that attempted to minimize both kinematic and muscular asymmetries between limbs. What emerged was a trade-off between these two parameters, and we propose this trade-off as a potential neuromuscular mechanism of cooperative bimanual reaching.NEW & NOTEWORTHY This study is the first to propose a trade-off between kinematic and dynamic control parameters governing goal-directed reaching. We propose a straightforward tool to assess this trade-off without the need for computational modeling. The technologies and techniques developed in this study are discussed in the context of upper extremity rehabilitation.

A tracking device for a wearable high-DOF passive hand exoskeleton.

In previous work, we developed an exoskeleton (HandSOME II) that allows movement at 15 hand degrees of freedom (DOF) and is intended for take-home use. An activity tracking device was developed in order to track index finger movement with a pair of magnetometers and magnet. The goal was to detect grip attempts by the individual. Machine learning was utilized to estimate angles for metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints at the index finger. Testing was performed with healthy control and individuals with stroke.Clinical Relevance- This device and method of data collection during daily activities might be useful for stroke rehabilitation and compliance with home-based therapy programs.

Clinical Test of a Wearable, High DOF, Spring Powered Hand Exoskeleton (HandSOME II).

In previous work, we developed an exoskeleton, Hand Spring Operated Movement Enhancer (HandSOME II), that allows movement at 15 hand degrees of freedom (DOF). Eleven separate elastic elements can be added to customize the extension assistance for individuals with impaired hand function. In this pilot study of twelve individuals with stroke, we measured the immediate improvements in range of motion (ROM) and upper extremity function when wearing the device. Index finger ROM was significantly improved at the PIP (p=.01) and DIP joints (p=.026), and the max extension was significantly increased at the MCP (p<.001), PIP (p=.013) and DIP joints (p=.016). The thumb CMC abduction max (p=.017) and CMC flexion/extension ROM also increased (p=.04). In a grip and release task involving various objects, six subjects were unable to complete the tasks without assistance. Across these 6 subjects, 13 of 42 tasks were completed without assistance, while 36 of 42 tasks were completed when wearing HandSOME II. Despite the extension assistance provided by the device, flexion grip force was not statistically decreased. HandSOME II can potentially increase the effectiveness of repetitive task practice in patients with moderate-severe hand impairment by allowing completion of grasp and release tasks that are impossible to complete unassisted.

Shoulder position and handedness differentially affect excitability and intracortical inhibition of hand muscles.

Individuals with stroke show distinct differences in hand function impairment when the shoulder is in adduction, within the workspace compared to when the shoulder is abducted, away from the body. To better understand how shoulder position affects hand control, we tested the corticomotor excitability and intracortical control of intrinsic and extrinsic hand muscles important for grasp in twelve healthy individuals. Motor evoked potentials (MEP) using single and paired-pulse transcranial magnetic stimulation were elicited in extensor digitorum communis (EDC), flexor digitorum superficialis (FDS), first dorsal interosseous (FDI), and abductor pollicis brevis (APB). The shoulder was fully supported in horizontal adduction (ADD) or abduction (ABD). Separate mixed-effect models were fit to the MEP parameters using shoulder position (or upper-extremity [UE] side) as fixed and participants as random effects. In the non-dominant UE, EDC showed significantly greater MEPs in shoulder ABD than ADD. In contrast, the dominant side EDC showed significantly greater MEPs in ADD compared to ABD; %facilitation of EDC on dominant side showed significant stimulus intensity x position interaction, EDC excitability was significantly greater in ADD at 150% of the resting threshold. Intrinsic hand muscles of the dominant UE received significantly more intracortical inhibition (SICI) when the shoulder was in ADD compared to ABD; there was no position-dependent modulation of SICI on the non-dominant side. Our findings suggest that these resting-state changes in hand muscle excitabilities reflect the natural statistics of UE movements, which in turn may arise from as well as shape the nature of shoulder-hand coupling underlying UE behaviors.

Home-Based Therapy After Stroke Using the Hand Spring Operated Movement Enhancer (HandSOME II).

We have developed a passive and lightweight wearable hand exoskeleton (HandSOME II) that improves range of motion and functional task practice in laboratory testing. For this longitudinal study, we recruited 15 individuals with chronic stroke and asked them to use the device at home for 1.5 h per weekday for 8 weeks. Subjects visited the clinic once per week to report progress and troubleshoot problems. Subjects were then given the HandSOME II for the next 3 months, and asked to continue to use it, but without any scheduled contact with the project team. Clinical evaluations and biomechanical testing was performed before and after the 8 week intervention and at the 3 month followup. EEG measures were taken before and after the 8 weeks of training to examine any recovery associated brain reorganization. Ten subjects completed the study. After 8 weeks of training, functional ability (Action Research Arm Test), flexor tone (Modified Ashworth Test), and real world use of the impaired limb (Motor Activity Log) improved significantly (p < 0.05). Gains in real world use were retained at the 3-month followup (p = 0.005). At both post-training and followup time points, biomechanical testing found significant gains in finger ROM and hand displacement in a reaching task (p < 0.05). Baseline functional connectivity correlated with gains in motor function, while changes in EEG functional connectivity paralleled changes in motor recovery. HandSOME II is a low-cost, home-based intervention that elicits brain plasticity and can improve functional motor outcomes in the chronic stroke population.

Pilot test of dosage effects in HEXORR II for robotic hand movement therapy in individuals with chronic stroke.

Impaired use of the hand in functional tasks remains difficult to overcome in many individuals after a stroke. This often leads to compensation strategies using the less-affected limb, which allows for independence in some aspects of daily activities. However, recovery of hand function remains an important therapeutic goal of many individuals, and is often resistant to conventional therapies. In prior work, we developed HEXORR I, a robotic device that allows practice of finger and thumb movements with robotic assistance. In this study, we describe modifications to the device, now called HEXORR II, and a clinical trial in individuals with chronic stroke. Fifteen individuals with a diagnosis of chronic stroke were randomized to 12 or 24 sessions of robotic therapy. The sessions involved playing several video games using thumb and finger movement. The robot applied assistance to extension movement that was adapted based on task performance. Clinical and motion capture evaluations were performed before and after training and again at a 6-month followup. Fourteen individuals completed the protocol. Fugl-Meyer scores improved significantly at the 6 month time point compared to baseline, indicating reductions in upper extremity impairment. Flexor hypertonia (Modified Ashworth Scale) also decreased significantly due to the intervention. Motion capture found increased finger range of motion and extension ability after the intervention that continued to improve during the followup period. However, there was no change in a functional measure (Action Research Arm Test). At the followup, the high dose group had significant gains in hand displacement during a forward reach task. There were no other significant differences between groups. Future work with HEXORR II should focus on integrating it with functional task practice and incorporating grip and squeezing tasks.

Perceived effort affects choice of limb and reaction time of movements.

The decision regarding which arm to use to perform a task reflects a complex process that can be influenced by many factors, including effort requirements of acquiring the goal. In this study, we considered a virtual reality environment in which people reached to a visual target in three-dimensional space. To vary the cost of reaching, we altered the visual feedback associated with motion of one arm but not the other. This altered the extent of motion that was required to reach, thus changing the effort required to acquire the goal. We then measured how that change in effort affected the decision regarding which arm to use, as well as the preparation time for the movement that ensued. As expected, with increased visual amplification of one arm (reduced effort to reach the goal), subjects increased the probability of choosing that arm. Surprisingly, however, the reaction times to start these movements were also reduced: despite constancy of the visual representation of the target, reaction times were shorter for movements with less effort. Thus, as the perceived effort associated with accomplishing a goal was reduced for a given limb, the decision-making process was biased toward use of that limb. Furthermore, movements that were perceived to be less effortful were performed with shorter reaction times. These results suggest that visual amplification can alter the perceived effort associated with using a limb, thus increasing frequency of use. This may provide a useful method to increase use of a limb during rehabilitation.NEW & NOTEWORTHY We report that visual amplification may serve as an effective means to alter the perceived effort associated with use of a limb. This method may provide an effective tool with which use of the affected limb can be encouraged noninvasively after neurological injury.

Improving Accelerometry-Based Measurement of Functional Use of the Upper Extremity After Stroke: Machine Learning Versus Counts Threshold Method.

BACKGROUND: Wrist-worn accelerometry provides objective monitoring of upper-extremity functional use, such as reaching tasks, but also detects nonfunctional movements, leading to ambiguity in monitoring results. OBJECTIVE: Compare machine learning algorithms with standard methods (counts ratio) to improve accuracy in detecting functional activity. METHODS: Healthy controls and individuals with stroke performed unstructured tasks in a simulated community environment (Test duration = 26 ± 8 minutes) while accelerometry and video were synchronously recorded. Human annotators scored each frame of the video as being functional or nonfunctional activity, providing ground truth. Several machine learning algorithms were developed to separate functional from nonfunctional activity in the accelerometer data. We also calculated the counts ratio, which uses a thresholding scheme to calculate the duration of activity in the paretic limb normalized by the less-affected limb. RESULTS: The counts ratio was not significantly correlated with ground truth and had large errors (r = 0.48; P = .16; average error = 52.7%) because of high levels of nonfunctional movement in the paretic limb. Counts did not increase with increased functional movement. The best-performing intrasubject machine learning algorithm had an accuracy of 92.6% in the paretic limb of stroke patients, and the correlation with ground truth was r = 0.99 (P < .001; average error = 3.9%). The best intersubject model had an accuracy of 74.2% and a correlation of r =0.81 (P = .005; average error = 5.2%) with ground truth. CONCLUSIONS: In our sample, the counts ratio did not accurately reflect functional activity. Machine learning algorithms were more accurate, and future work should focus on the development of a clinical tool.

HandMATE: Wearable Robotic Hand Exoskeleton and Integrated Android App for At Home Stroke Rehabilitation.

We have developed HandMATE (Hand Movement Assisting Therapy Exoskeleton); a wearable motorized hand exoskeleton for home-based movement therapy following stroke. Each finger and the thumb is powered by a linear actuator which provides flexion and extension assistance. Force sensitive resistors integrated into the design measure grasp and extension initiation force. An assistive therapy mode is based on an admittance control strategy. We evaluated our control system via subject and bench testing. Errors during a grip force tracking task while using the HandMATE were minimal (<1%) and comparable to unassisted healthy hand performance. We also outline a dedicated app we have developed for optimal use of HandMATE at home. The exoskeleton communicates wirelessly with an Android tablet which features guided exercises, therapeutic games and performance feedback. We surveyed 5 chronic stroke patients who used the HandMATE device to further evaluate our system, receiving positive feedback on the exoskeleton and integrated app.

Characterizing upper extremity motor behavior in the first week after stroke.

BACKGROUND: Animal models of brain recovery identify the first days after lesioning as a time of great flux in sensorimotor function and physiology. After rodent motor system lesioning, daily skill training in the less affected forelimb reduces skill acquisition in the more affected forelimb. We asked whether spontaneous human motor behaviors of the less affected upper extremity (UE) early after stroke resemble the animal training model, with the potential to suppress clinical recovery. METHODS: This prospective observational study used a convenience sample of patients (n = 25, mean 4.5 ±1.8) days after stroke with a wide severity range; Controls were hospitalized for non-neurological conditions (n = 12). Outcome measures were Accelerometry, Upper-Extremity Fugl-Meyer (UEFM), Action Research Arm Test (ARAT), Shoulder Abduction/ Finger Extension Test (SAFE), NIH Stroke Scale (NIHSS). RESULTS: Accelerometry indicated total paretic UE movement was reduced compared to controls, primarily due to a 44% reduction of bilateral UE use. Unilateral paretic movement was unchanged. Thus, movement shifted early after stroke; bilateral use was reduced and unilateral use of the non-paretic UE was increased by 77%. Low correlations between movement time and motor performance prompted an exploratory factor analysis (EFA) revealing a 2-component solution; motor performance tests load on one component (motor performance) whereas accelerometry-derived variables load on a second orthogonal component (quantity of movement). CONCLUSIONS: Early after stroke, spontaneous overall UE movement is reduced, and movement shifts to unilateral use of the non-paretic UE. Two mechanisms that could influence motor recovery may already be in place 4.5 ± 1.8 days post stroke: (1) the overuse of the less affected UE, which could set the stage for learned non-use and (2) skill acquisition in the non-paretic limb that could impede recovery. Accurate UE motor assessment requires two independent constructs: motor performance and quantity of movement. These findings provide opportunities and measurement methods for studies to develop new behaviorally-based stroke recovery treatments that begin early after onset.

Comparison of Two Series Elastic Actuator Designs Incorporated into a Shoulder Exoskeleton.

Low impedance and torque control are critical for movement rehabilitation using robotic exoskeletons. A grounded 3 degree of freedom shoulder exoskeleton was designed for movement assistance in shoulder abduction/adduction, flexion/extension, and shoulder internal/external rotation. Two series elastic actuators designs were developed using a linear spring arrangement with a global nonlinear stiffness behavior. RMS errors during application of constant torque were less than.06 Nm in shoulder add/abd and less than.04 Nm in arm rotation as the limb was moved in sinusoidal trajectories up to 3.5 Hz. For abd/adduction, the step response rise time was.05 s, and free mode impedance peaked at.007 Nm/deg during 3.5 Hz oscillations. For arm rotation, the step response rise time was.03 s, and impedance peaked at.023 Nm/deg during 3.5 Hz oscillations. Both SEA designs had performance measurements that were similar to other SEA designs in terms of torque tracking, but with much lower impedance than previously reported.

An Elbow Exoskeleton for Upper Limb Rehabilitation with Series Elastic Actuator and Cable-driven Differential.

Movement impairments resulting from neurologic injuries, such as stroke, can be treated with robotic exoskeletons that assist with movement retraining. Exoskeleton designs benefit from low impedance and accurate torque control. We designed a 2 degree-of-freedom tethered exoskeleton that can provide independent torque control on elbow flexion/extension and forearm supination/pronation. Two identical series elastic actuators (SEAs) are used to actuate the exoskeleton. The two SEAs are coupled through a novel cable-driven differential. The exoskeleton is compact and lightweight, with a mass of 0.9 kg. Applied RMS torque errors were less than 0.19 Nm. Benchtop tests demonstrated a torque rise time of approximately 0.1 s, a torque control bandwidth of 3.7 Hz and an impedance of less than 0.03 Nm/deg at 1 Hz. The controller can simulate a stable maximum wall stiffness of 0.45 Nm/deg. The overall performance is adequate for robotic therapy applications and the novelty of the design is discussed.

Pilot testing of the spring operated wearable enhancer for arm rehabilitation (SpringWear).

BACKGROUND: Robotic devices for neurorehabilitation of movement impairments in persons with stroke have been studied extensively. However, the vast majority of these devices only allow practice of stereotyped components of simulated functional tasks in the clinic. Previously we developed SpringWear, a wearable, spring operated, upper extremity exoskeleton capable of assisting movements during real-life functional activities, potentially in the home. SpringWear assists shoulder flexion, elbow extension and forearm supination/pronation. The assistance profiles were designed to approximate the torque required to move the joint passively through its range. These three assisted DOF are combined with two passive shoulder DOF, allowing complex multi-joint movement patterns. METHODS: We performed a cross-sectional study to assess changes in movement patterns when assisted by SpringWear. Thirteen persons with chronic stroke performed range of motion (ROM) and functional tasks, including pick and place tasks with various objects. Sensors on the device measured rotation at all 5 DOF and a kinematic model calculated position of the wrist relative to the shoulder. Within subject t-tests were used to determine changes with assistance from SpringWear. RESULTS: Maximum shoulder flexion, elbow extension and forearm pronation/supination angles increased significantly during both ROM and functional tasks (p < 0.002). Elbow flexion/extension ROM also increased significantly (p < 0.001). When the subjects volitionally held up the arm against gravity, extension at the index finger proximal interphalangeal joint increased significantly (p = 0.033) when assisted by SpringWear. The forward reach workspace increased 19% (p = 0.002). Nine subjects could not complete the functional tasks unassisted and only one showed improvement on task completion with SpringWear. CONCLUSIONS: SpringWear increased the usable workspace during reaching movements, but there was no consistent improvement in the ability to complete functional tasks. Assistance levels at the shoulder were increased only until the shoulder could be voluntarily held at 90 degrees of flexion. A higher level of assistance may have yielded better results. Also combining SpringWear with HandSOME, an exoskeleton for assisting hand opening, may yield the most dramatic improvements in functional task performance. These low-cost devices can potentially reduce effort and improve performance during task practice, increasing adherence to home training programs for rehabilitation.

Home-Based Therapy After Stroke Using the Hand Spring Operated Movement Enhancer (HandSOME).

In previous work, we developed a lightweight wearable hand exoskeleton (Hand Spring Operated Movement Enhancer) that improves range of motion and function in laboratory testing. In this pilot study, we added the ability to log movement data for extended periods and recruited ten chronic stroke subjects to use the device during reach and grasp task practice at home for 1.5 h/day, five days per week, and for four weeks. Seven subjects completed the study, performing 448 ± 651 hand movements per training day. After training, impairment was reduced (Fugl-Meyer test; gain = 4.9 ± 4.1; p = .039) and function was improved (Action Research Arm Test; gain = 3.3 ± 2.6; p = .032). There was a significant correlation between gains in the Action Research Arm Test and the number of movements during training (r = 0.90; p = .005). Proximal arm control also improved, as evidenced by a significant reduction in the reach path ratio (p = 0.038). Five subjects responded well to the treatment, having gains of six points or more on the Fugl-Meyer or action research arm test, and achieving significant gains in digit extension (gain = 19.8 ± 10.2°; p = 0.024). However, all of the gains that were significant immediately after training were no longer significant at the three month follow-up. This treatment approach appears promising, but longer periods of home training may be needed to achieve sustainable gains.

Neural coupling between homologous muscles during bimanual tasks: effects of visual and somatosensory feedback.

While the effects of sensory feedback on bimanual tasks have been studied extensively at two ends of the motor control hierarchy, the cortical and behavioral levels, much less is known about how it affects the intermediate levels, including neural control of homologous muscle groups. We investigated the effects of somatosensory input on the neural coupling between homologous arm muscles during bimanual tasks. Twelve subjects performed symmetric elbow flexion/extension tasks under different types of sensory feedback. The first two types involve visual feedback, with one imposing stricter force symmetry than the other. The third incorporated somatosensory feedback via a balancing apparatus that forced the two limbs to produce equal force levels. Although the force error did not differ between feedback conditions, the somatosensory feedback significantly increased temporal coupling of bilateral force production, indicated by a high correlation between left/right force profiles (P < 0.001). More importantly, intermuscular coherence between biceps brachii muscles was significantly higher with somatosensory feedback than others (P = 0.001). Coherence values also significantly differed between tasks (flexion/extension). Notably, whereas feedback type mainly modulated coherence in the α- and γ-bands, task type only affected ß-band coherence. Similar feedback effects were observed for triceps brachii muscles, but there was also a strong phase effect on the coherence values (P < 0.001) that could have diluted feedback effects. These results suggest that somatosensory feedback can significantly increase neural coupling between homologous muscles. Additionally, the between-task difference in ß-band coherence may reflect different neural control strategies for the elbow flexor and extensor muscles. NEW & NOTEWORTHY: This study investigated the effects of somatosensory feedback during bimanual tasks on the neural coupling between arm muscles, which remains largely unexplored. Somatosensory feedback using a balancing apparatus, compared with visual feedback, significantly increased neural coupling between homologous muscles (indicated by intermuscular coherence values) and improved temporal correlation of bilateral force production. Notably, feedback type modulated coherence in the α- and γ-bands (more subcortical pathways), whereas task type mainly affected ß-band coherence (corticospinal pathway).