A single-center, assessor-blinded, randomized controlled clinical trial to test the safety and efficacy of a novel brain-computer interface controlled functional electrical stimulation (BCI-FES) intervention for gait rehabilitation in the chronic stroke population.

BACKGROUND: In the United States, there are over seven million stroke survivors, with many facing gait impairments due to foot drop. This restricts their community ambulation and hinders functional independence, leading to several long-term health complications. Despite the best available physical therapy, gait function is incompletely recovered, and this occurs mainly during the acute phase post-stroke. Therapeutic options are limited currently. Novel therapies based on neurobiological principles have the potential to lead to long-term functional improvements. The Brain-Computer Interface (BCI) controlled Functional Electrical Stimulation (FES) system is one such strategy. It is based on Hebbian principles and has shown promise in early feasibility studies. The current study describes the BCI-FES clinical trial, which examines the safety and efficacy of this system, compared to conventional physical therapy (PT), to improve gait velocity for those with chronic gait impairment post-stroke. The trial also aims to find other secondary factors that may impact or accompany these improvements and establish the potential of Hebbian-based rehabilitation therapies. METHODS: This Phase II clinical trial is a two-arm, randomized, controlled, longitudinal study with 66 stroke participants in the chronic (> 6 months) stage of gait impairment. The participants undergo either BCI-FES paired with PT or dose-matched PT sessions (three times weekly for four weeks). The primary outcome is gait velocity (10-meter walk test), and secondary outcomes include gait endurance, range of motion, strength, sensation, quality of life, and neurophysiological biomarkers. These measures are acquired longitudinally. DISCUSSION: BCI-FES holds promise for gait velocity improvements in stroke patients. This clinical trial will evaluate the safety and efficacy of BCI-FES therapy when compared to dose-matched conventional therapy. The success of this trial will inform the potential utility of a Phase III efficacy trial. TRIAL REGISTRATION: The trial was registered as "BCI-FES Therapy for Stroke Rehabilitation" on February 19, 2020, at clinicaltrials.gov with the identifier NCT04279067.

NSF DARE-Transforming modeling in neurorehabilitation: Four threads for catalyzing progress.

We present an overview of the Conference on Transformative Opportunities for Modeling in Neurorehabilitation held in March 2023. It was supported by the Disability and Rehabilitation Engineering (DARE) program from the National Science Foundation's Engineering Biology and Health Cluster. The conference brought together experts and trainees from around the world to discuss critical questions, challenges, and opportunities at the intersection of computational modeling and neurorehabilitation to understand, optimize, and improve clinical translation of neurorehabilitation. We organized the conference around four key, relevant, and promising Focus Areas for modeling: Adaptation & Plasticity, Personalization, Human-Device Interactions, and Modeling 'In-the-Wild'. We identified four common threads across the Focus Areas that, if addressed, can catalyze progress in the short, medium, and long terms. These were: (i) the need to capture and curate appropriate and useful data necessary to develop, validate, and deploy useful computational models (ii) the need to create multi-scale models that span the personalization spectrum from individuals to populations, and from cellular to behavioral levels (iii) the need for algorithms that extract as much information from available data, while requiring as little data as possible from each client (iv) the insistence on leveraging readily available sensors and data systems to push model-driven treatments from the lab, and into the clinic, home, workplace, and community. The conference archive can be found at (dare2023.usc.edu). These topics are also extended by three perspective papers prepared by trainees and junior faculty, clinician researchers, and federal funding agency representatives who attended the conference.

Counting Finger and Wrist Movements Using Only a Wrist-Worn, Inertial Measurement Unit: Toward Practical Wearable Sensing for Hand-Related Healthcare Applications.

The ability to count finger and wrist movements throughout the day with a nonobtrusive, wearable sensor could be useful for hand-related healthcare applications, including rehabilitation after a stroke, carpal tunnel syndrome, or hand surgery. Previous approaches have required the user to wear a ring with an embedded magnet or inertial measurement unit (IMU). Here, we demonstrate that it is possible to identify the occurrence of finger and wrist flexion/extension movements based on vibrations detected by a wrist-worn IMU. We developed an approach we call "Hand Activity Recognition through using a Convolutional neural network with Spectrograms" (HARCS) that trains a CNN based on the velocity/acceleration spectrograms that finger/wrist movements create. We validated HARCS with the wrist-worn IMU recordings obtained from twenty stroke survivors during their daily life, where the occurrence of finger/wrist movements was labeled using a previously validated algorithm called HAND using magnetic sensing. The daily number of finger/wrist movements identified by HARCS had a strong positive correlation to the daily number identified by HAND (R2 = 0.76, p < 0.001). HARCS was also 75% accurate when we labeled the finger/wrist movements performed by unimpaired participants using optical motion capture. Overall, the ringless sensing of finger/wrist movement occurrence is feasible, although real-world applications may require further accuracy improvements.

Force acquisition frequency is less impaired compared to grip strength or hand dexterity in individuals with chronic stroke.

Despite numerous studies that show force regulation is impaired after stroke, two recent studies suggest that the ability to regulate submaximal, isometric grip forces may remain substantially intact. Here we asked how this aspect of hand motor control, measured for both a power grip and pinch grip, compares to two other key aspects of hand function-grip strength and dexterity. For 20 individuals with a range of hand impairment levels in the chronic phase of stroke (Age: 62 ± 16.0 years, Time post stroke: 958.3 ± 966.5 days, Sex: 19 M 1F) we quantified the average rate of target acquisition for force targets in the range of 3 to 30% maximum voluntary contraction as participants used the two different grips to squeeze a force sensor to control a cursor on a screen. The same force sensor was used to assess subject grip strength for the two grips, while dexterity was assessed using the Box and Blocks Test (BBT), and the Nine Hole Peg Test (NHPT) for both the paretic and non-paretic hand. On average, the relative rate of force acquisition of the paretic hand using a power grip was 74.0 ± 18.6 SD % of the non-paretic hand, a value significantly higher than the 48.6 ± 25.6 SD% for grip strength (paired t test, p < .005) or the 41.4 ± 29.1 SD % (p < .005) and 23.3 ± 30.1 SD % (p < .005) for the BBT and NHPT, respectively. Results were similar for the lateral pinch grip, suggesting similar thumb force tracking performance. Grip force tracking, measured as rate of force acquisition, is less impaired after stroke than hand strength or dexterity, a finding with implications for the neural mechanisms of stroke and the design of assistive technologies.

Providing Real-Time Wearable Feedback to Increase Hand Use after Stroke: A Randomized, Controlled Trial.

After stroke, many people substantially reduce use of their impaired hand in daily life, even if they retain even a moderate level of functional hand ability. Here, we tested whether providing real-time, wearable feedback on the number of achieved hand movements, along with a daily goal, can help people increase hand use intensity. Twenty participants with chronic stroke wore the Manumeter, a novel magnetic wristwatch/ring system that counts finger and wrist movements. We randomized them to wear the device for three weeks with (feedback group) or without (control group) real-time hand count feedback and a daily goal. Participants in the control group used the device as a wristwatch, but it still counted hand movements. We found that the feedback group wore the Manumeter significantly longer (11.2 ± 1.3 h/day) compared to the control group (10.1 ± 1.1 h/day). The feedback group also significantly increased their hand counts over time (p = 0.012, slope = 9.0 hand counts/hour per day, which amounted to ~2000 additional counts per day by study end), while the control group did not (p-value = 0.059; slope = 4.87 hand counts/hour per day). There were no significant differences between groups in any clinical measures of hand movement ability that we measured before and after the feedback period, although several of these measures improved over time. Finally, we confirmed that the previously reported threshold relationship between hand functional capacity and daily use was stable over three weeks, even in the presence of feedback, and established the minimal detectable change for hand count intensity, which is about 30% of average daily intensity. These results suggest that disuse of the hand after stroke is temporarily modifiable with wearable feedback, but do not support that a 3-week intervention of wearable hand count feedback provides enduring therapeutic gains.

Using a bimanual lever-driven wheelchair for arm movement practice early after stroke: A pilot, randomized, controlled, single-blind trial.

OBJECTIVE: Many patients with subacute stroke rely on the nonparetic arm and leg to propel manual wheelchairs. We designed a bimanual, lever-driven wheelchair (LARA) to promote overground mobility and hemiparetic arm exercise. This study measured the feasibility of using LARA to increase arm movement, achieve mobility, and improve arm motor recovery (clinicaltrials.gov/ct2/show/NCT02830893). DESIGN: Randomized, assessor-blind, controlled trial. SETTING: Two inpatient rehabilitation facilities. SUBJECTS: Nineteen patients with subacute stroke (1 week to 2 months post-stroke) received 30 minutes extra arm movement practice daily, while admitted to inpatient rehabilitation (n = 10) or before enrollment in outpatient therapy (n = 9). INTERVENTIONS: Patients were randomized to train with the LARA wheelchair (n = 11) or conventional exercises with a rehabilitation therapist (n = 8). MAIN MEASURES: Number of arm movements per training session; overground speed; Upper Extremity Fugl-Meyer score at three-month follow-up. RESULTS: Participants who trained with LARA completed 254 (median) arm movements with the paretic arm each session. For three participants, LARA enabled wheelchair mobility at practical indoor speeds (0.15-0.30 m/s). Fugl-Meyer score increased 19 ± 13 points for patients who trained with LARA compared to 14 ± 7 points with conventional exercises (P = 0.32). Secondary measures including shoulder pain and increased tone did not differ between groups. Mixed model analysis found significant interaction between LARA training and treatment duration (P = 0.037), informing power analysis for future investigation. CONCLUSIONS: Practising arm movement with a lever-driven wheelchair is a feasible method for increasing arm movement early after stroke. It enabled wheelchair mobility for a subset of patients and shows potential for improving arm motor recovery.

A day in the life: a qualitative study of clinical decision-making and uptake of neurorehabilitation technology.

BACKGROUND: Neurorehabilitation engineering faces numerous challenges to translating new technologies, but it is unclear which of these challenges are most limiting. Our aim is to improve understanding of rehabilitation therapists' real-time decision-making processes on the use of rehabilitation technology (RT) in clinical treatment. METHODS: We used a phenomenological qualitative approach, in which three OTs and two PTs employed at a major, technology-encouraging rehabilitation hospital wrote vignettes from a written prompt describing their RT use decisions during treatment sessions with nine patients (4 with stroke, 2 traumatic brain injury, 1 spinal cord injury, 1 with multiple sclerosis). We then coded the vignettes using deductive qualitative analysis from 17 constructs derived from the RT literature and the Consolidated Framework for Implementation Research (CFIR). Data were synthesized using summative content analysis. RESULTS: Of the constructs recorded, the five most prominent are from CFIR determinants of: (i) relative advantage, (ii) personal attributes of the patients, (iii) clinician knowledge and beliefs of the device/intervention, (iv) complexity of the devices including time and setup, and (v) organizational readiness to implement. Therapists characterized candidate RT as having a relative disadvantage compared to conventional treatment due to lack of relevance to functional training. RT design also often failed to consider the multi-faceted personal attributes of the patients, including diagnoses, goals, and physical and cognitive limitations. Clinicians' comfort with RT was increased by their previous training but was decreased by the perceived complexity of RT. Finally, therapists have limited time to gather, setup, and use RT. CONCLUSIONS: Despite decades of design work aimed at creating clinically useful RT, many lack compatibility with clinical translation needs in inpatient neurologic rehabilitation. New RT continue to impede the immediacy, versatility, and functionality of hands-on therapy mediated treatment with simple everyday objects.

Magnetically Counting Hand Movements: Validation of a Calibration-Free Algorithm and Application to Testing the Threshold Hypothesis of Real-World Hand Use after Stroke.

There are few wearable sensors suitable for daily monitoring of wrist and finger movements for hand-related healthcare applications. Here, we describe the development and validation of a novel algorithm for magnetically counting hand movements. We implemented the algorithm on a wristband that senses magnetic field changes produced by movement of a magnetic ring worn on the finger (the "Manumeter"). The "HAND" (Hand Activity estimated by Nonlinear Detection) algorithm assigns a "HAND count" by thresholding the real-time change in magnetic field created by wrist and/or finger movement. We optimized thresholds to achieve a HAND count accuracy of ~85% without requiring subject-specific calibration. Then, we validated the algorithm in a dexterity-impaired population by showing that HAND counts strongly correlate with clinical assessments of upper extremity (UE) function after stroke. Finally, we used HAND counts to test a recent hypothesis in stroke rehabilitation that real-world UE hand use increases only for stroke survivors who achieve a threshold level of UE functional capability. For 29 stroke survivors, HAND counts measured at home did not increase until the participants' Box and Blocks Test scores exceeded ~50% normal. These results show that a threshold-based magnetometry approach can non-obtrusively quantify hand movements without calibration and also verify a key concept of real-world hand use after stroke.

Evaluation of an exercise-enabling control interface for powered wheelchair users: a feasibility study with Duchenne muscular dystrophy.

BACKGROUND: Powered wheelchairs are an essential technology to support mobility, yet their use is associated with a high level of sedentarism that can have negative health effects for their users. People with Duchenne muscular dystrophy (DMD) start using a powered wheelchair in their early teens due to the loss of strength in their legs and arms. There is evidence that low-intensity exercise can help preserve the functional abilities of people with DMD, but options for exercise when sitting in a powered wheelchair are limited. METHODS: In this paper, we present the design and the feasibility study of a new version of the MOVit device that allows powered-wheelchair users to exercise while driving the chair. Instead of using a joystick to drive the wheelchair, users move their arms through a cyclical motion using two powered, mobile arm supports that provide controller inputs to the chair. The feasibility study was carried out with a group of five individuals with DMD and five unimpaired individuals. Participants performed a series of driving tasks in a wheelchair simulator and on a real driving course with a standard joystick and with the MOVit 2.0 device. RESULTS: We found that driving speed and accuracy were significantly lowered for both groups when driving with MOVit compared to the joystick, but the decreases were small (speed was 0.26 m/s less and maximum path error was 0.1 m greater). Driving with MOVit produced a significant increase in heart rate (7.5 bpm) compared to the joystick condition. Individuals with DMD reported a high level of satisfaction with their performance and comfort in using MOVit. CONCLUSIONS: These results show for the first time that individuals with DMD can easily transition to driving a powered wheelchair using cyclical arm motions, achieving a reasonable driving performance with a short period of training. Driving in this way elicits cardiopulmonary exercise at an intensity found previously to produce health-related benefits in DMD.

JNER at 15 years: analysis of the state of neuroengineering and rehabilitation.

On JNER's 15th anniversary, this editorial analyzes the state of the field of neuroengineering and rehabilitation. I first discuss some ways that the nature of neurorehabilitation research has evolved in the past 15 years based on my perspective as editor-in-chief of JNER and a researcher in the field. I highlight increasing reliance on advanced technologies, improved rigor and openness of research, and three, related, new paradigms - wearable devices, the Cybathlon competition, and human augmentation studies - indicators that neurorehabilitation is squarely in the age of wearability. Then, I briefly speculate on how the field might make progress going forward, highlighting the need for new models of training and learning driven by big data, better personalization and targeting, and an increase in the quantity and quality of usability and uptake studies to improve translation.

Real-time slacking as a default mode of grip force control: implications for force minimization and personal grip force variation.

During trial-to-trial movement adaptation, the motor system systematically reduces extraneous muscle forces when kinematic errors experienced on previous movements are small, a phenomenon termed "slacking." There is also growing evidence that the motor system slacks continuously (i.e., in real-time) during arm movement or grip force control, but the initiation of this slacking is not well-characterized, obfuscating its physiological cause. Here, we addressed this issue by asking participants ( n = 32) to track discrete force targets presented visually using isometric grip force, then applying a brief, subtle error-clamp to that visual feedback on random trials. Participants reduced their force in an exponential fashion, on these error-clamp trials, except when the target force was <10% maximum voluntary contraction (MVC). This force drift began <250 ms after the onset of the error-clamp, consistent with slacking being an ongoing process unmasked immediately after the motor system finished reacting to the last veridical feedback. Above 10% MVC, the slacking rate increased linearly with grip force magnitude. Grip force variation was approximately 50-100% higher with veridical feedback, largely due to heightened signal power at ~1 Hz, the band of visuomotor feedback control. Finally, the slacking rate measured for each participant during error-clamp trials correlated with their force variation during control trials. That is, participants who slacked more had greater force variation. These results suggest that real-time slacking continuously reduces grip force until visual error prompts correction. Whereas such slacking is suited for force minimization, it may also account for ~30% of the variability in personal grip force variation. NEW & NOTEWORTHY We provide evidence that a form of slacking continuously conditions real-time grip force production. This slacking is well-suited to promote efficiency but is expected to increase force variation by triggering additional feedback corrections. Moreover, we show that the rate at which a person slacks is substantially correlated with the variation of their grip force. In combination, at the neurophysiological level, our results suggest slacking is caused by one or more relatively smooth neural adaptations.

Neural circuits activated by error amplification and haptic guidance training techniques during performance of a timing-based motor task by healthy individuals.

To promote motor learning, robotic devices have been used to improve subjects' performance by guiding desired movements (haptic guidance-HG) or by artificially increasing movement errors to foster a more rapid learning (error amplification-EA). To better understand the neurophysiological basis of motor learning, a few studies have evaluated brain regions activated during EA/HG, but none has compared both approaches. The goal of this study was to investigate using fMRI which brain networks were activated during a single training session of HG/EA in healthy adults learning to play a computerized pinball-like timing task. Subjects had to trigger a robotic device by flexing their wrist at the correct timing to activate a virtual flipper and hit a falling ball towards randomly positioned targets. During training with HG/EA, subjects' timing errors were decreased/increased, respectively, by the robotic device to delay or accelerate their wrist movement. The results showed that at the beginning of the training period with HG/EA, an error-detection network, including cerebellum and angular gyrus, was activated, consistent with subjects recognizing discrepancies between their intended actions and the actual movement timing. At the end of the training period, an error-detection network was still present for EA, while a memory consolidation/automatization network (caudate head and parahippocampal gyrus) was activated for HG. The results indicate that training movement with various kinds of robotic input relies on different brain networks. Better understanding the neurophysiological underpinnings of brain processes during HG/EA could prove useful for optimizing rehabilitative movement training for people with different patterns of brain damage.

Dissociating motor learning from recovery in exoskeleton training post-stroke.

BACKGROUND: A large number of robotic or gravity-supporting devices have been developed for rehabilitation of upper extremity post-stroke. Because these devices continuously monitor performance data during training, they could potentially help to develop predictive models of the effects of motor training on recovery. However, during training with such devices, patients must become adept at using the new "tool" of the exoskeleton, including learning the new forces and visuomotor transformations associated with the device. We thus hypothesized that the changes in performance during extensive training with a passive, gravity-supporting, exoskeleton device (the Armeo Spring) will follow an initial fast phase, due to learning to use the device, and a slower phase that corresponds to reduction in overall arm impairment. Of interest was whether these fast and slow processes were related. METHODS: To test the two-process hypothesis, we used mixed-effect exponential models to identify putative fast and slow changes in smoothness of arm movements during 80 arm reaching tests performed during 20 days of exoskeleton training in 53 individuals with post-acute stroke. RESULTS: In line with our hypothesis, we found that double exponential models better fit the changes in smoothness of arm movements than single exponential models. In contrast, single exponential models better fit the data for a group of young healthy control subjects. In addition, in the stroke group, we showed that smoothness correlated with a measure of impairment (the upper extremity Fugl Meyer score - UEFM) at the end, but not at the beginning, of training. Furthermore, the improvement in movement smoothness due to the slow component, but not to the fast component, strongly correlated with the improvement in the UEFM between the beginning and end of training. There was no correlation between the change of peaks due to the fast process and the changes due to the slow process. Finally, the improvement in smoothness due to the slow, but not the fast, component correlated with the number of days since stroke at the onset of training - i.e. participants who started exoskeleton training sooner after stroke improved their smoothness more. CONCLUSIONS: Our results therefore demonstrate that at least two processes are involved in in performance improvements measured during mechanized training post-stroke. The fast process is consistent with learning to use the exoskeleton, while the slow process independently reflects the reduction in upper extremity impairment.

Correction to: Dissociating motor learning from recovery in exoskeleton training post-stroke.

The original article [1] contained an error whereby the co-author, Karima Bakhti's name was displayed incorrectly.

How a diverse research ecosystem has generated new rehabilitation technologies: Review of NIDILRR's Rehabilitation Engineering Research Centers.

Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a "total approach to rehabilitation", combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970's, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program.

Use of a robotic device to measure age-related decline in finger proprioception.

Age-related changes in proprioception are known to affect postural stability, yet the extent to which such changes affect the finger joints is poorly understood despite the importance of finger proprioception in the control of skilled hand movement. We quantified age-related changes in finger proprioception in 37 healthy young, middle-aged, and older adults using two robot-based tasks wherein participants' index and middle fingers were moved by an exoskeletal robot. The first task assessed finger position sense by asking participants to indicate when their index and middle fingers were directly overlapped during a passive crisscross movement; the second task assessed finger movement detection by asking participants to indicate the onset of passive finger movement. When these tasks were completed without vision, finger position sense errors were 48 % larger in older adults compared to young participants (p < 0.05); proprioceptive reaction time was 78 % longer in older adults compared to young adults (p < 0.01). When visual feedback was provided in addition to proprioception, these age-related differences were no longer apparent. No difference between dominant and non-dominant hand performance was found for either proprioception task. These findings demonstrate that finger proprioception is impaired in older adults, and visual feedback can be used to compensate for this deficit. The findings also support the feasibility and utility of the FINGER robot as a sensitive tool for detecting age-related decline in proprioception.

Computational neurorehabilitation: modeling plasticity and learning to predict recovery.

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling - regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity.

Effects of robotically modulating kinematic variability on motor skill learning and motivation.

It is unclear how the variability of kinematic errors experienced during motor training affects skill retention and motivation. We used force fields produced by a haptic robot to modulate the kinematic errors of 30 healthy adults during a period of practice in a virtual simulation of golf putting. On day 1, participants became relatively skilled at putting to a near and far target by first practicing without force fields. On day 2, they warmed up at the task without force fields, then practiced with force fields that either reduced or augmented their kinematic errors and were finally assessed without the force fields active. On day 3, they returned for a long-term assessment, again without force fields. A control group practiced without force fields. We quantified motor skill as the variability in impact velocity at which participants putted the ball. We quantified motivation using a self-reported, standardized scale. Only individuals who were initially less skilled benefited from training; for these people, practicing with reduced kinematic variability improved skill more than practicing in the control condition. This reduced kinematic variability also improved self-reports of competence and satisfaction. Practice with increased kinematic variability worsened these self-reports as well as enjoyment. These negative motivational effects persisted on day 3 in a way that was uncorrelated with actual skill. In summary, robotically reducing kinematic errors in a golf putting training session improved putting skill more for less skilled putters. Robotically increasing kinematic errors had no performance effect, but decreased motivation in a persistent way.

Design and Evaluation of the Kinect-Wheelchair Interface Controlled (KWIC) Smart Wheelchair for Pediatric Powered Mobility Training.

BACKGROUND: Children with severe disabilities are sometimes unable to access powered mobility training. Thus, we developed the Kinect-Wheelchair Interface Controlled (KWIC) smart wheelchair trainer that converts a manual wheelchair into a powered wheelchair. The KWIC Trainer uses computer vision to create a virtual tether with adaptive shared-control between the wheelchair and a therapist during training. It also includes a mixed-reality video game system. METHODS: We performed a year-long usability study of the KWIC Trainer at a local clinic, soliciting qualitative and quantitative feedback on the device after extended use. RESULTS: Eight therapists used the KWIC Trainer for over 50 hours with 8 different children. Two of the children obtained their own powered wheelchair as a result of the training. The therapists indicated the device allowed them to provide mobility training for more children than would have been possible with a demo wheelchair, and they found use of the device to be as safe as or safer than conventional training. They viewed the shared control algorithm as counter-productive because it made it difficult for the child to discern when he or she was controlling the chair. They were enthusiastic about the video game integration for increasing motivation and engagement during training. They emphasized the need for additional access methods for controlling the device. CONCLUSION: The therapists confirmed that the KWIC Trainer is a useful tool for increasing access to powered mobility training and for engaging children during training sessions. However, some improvements would enhance its applicability for routine clinical use.

Time flies when you are in a groove: using entrainment to mechanical resonance to teach a desired movement distorts the perception of the movement's timing.

The human motor system quickly entrains rhythmic limb movement to the resonant frequency of mechanical systems with which it interacts, suggesting that entrainment to an appropriately designed training device might be a convenient way to teach desired movements. We tested this possibility by asking healthy subjects (N = 30) to learn to move with a desired movement timing using a simple resonating arm training device: a lever attached to a manual wheelchair. The subjects tried to learn to roll the lever-driven wheelchair back and forth in place at a target frequency initially presented using a series of auditory beeps. One-third of the subjects trained without resonance and with no further feedback about rolling frequency; their performance did not improve. Another group trained with continual visual feedback of frequency error but no resonance; they quickly learned to roll the chair at the target frequency, as evidenced at both short-term and long-term (1 day later) retention tests. A third group trained with elastic bands attached to the lever that caused the system to resonate at the target frequency, providing a timing template. While these participants quickly entrained to the target frequency during training, they did not accurately reproduce this frequency when the system was no longer resonant, moving too slowly with the same systematic error at both the short-term and long-term retention tests. They also did not exhibit a timing aftereffect on the initial movements made when they transitioned from a resonant to non-resonant system or vice versa. This suggests they did not realize they were performing the task with a temporal error. Entrainment to mechanical resonance conveys usable information about movement timing, but seems to cause that movement timing to be perceived as slower than it actually is, as if a putative internal clock speeds up, which is a factor to consider in designing machine-assisted motor training.