Uncovering clinical rehabilitation technology trends: field observations, mixed methods analysis, and data visualization

Objective

To analyze real-world rehabilitation technology (RT) use, with a view toward enhancing RT development and adoption.

Design

A convergent, mixed-methods study using direct field observations, semi-structured templates, and summative content analysis.

Setting

Ten neurorehabilitation units in a single health system.

Participants

3 research clinicians (1OT, 2PTs) observed ∼60 OTs and 70 PTs in inpatient; ∼18 OTs and 30 PTs in outpatient.

Interventions

Not applicable.

Main Outcome Measures

Characteristics of RT, time spent setting up and using RT, and clinician behaviors.

Results

90 distinct devices across 15 different focus areas were inventoried. 329 RT-uses were documented over 44 hours with 42% of inventoried devices used. RT was used more during interventions (72%) than measurement (28%). Intervention devices used frequently were balance/gait (39%), strength/endurance (30%), and transfer/mobility training (16%). Measurement devices were frequently used to measure vitals (83%), followed by grip strength (7%), and upper extremity function (5%). Device characteristics were predominately AC-powered (56%), actuated (57%), monitor-less (53%), multi-use (68%), and required little familiarization (57%). Set-up times were brief (mean ± SD = 3.8±4.21 and 0.8±1.3 for intervention and measurement, respectively); more time was spent with intervention RT (25.6±15) than measurement RT (7.3±11.2). RT nearly always involved verbal instructions (72%) with clinicians providing more feedback on performance (59.7%) than on results (30%). Therapists' attention was split evenly between direct attention towards the patient during clinician treatment (49.7%) and completing other tasks such as documentation (50%).

Conclusions

Even in a tech-friendly hospital, majority of available RT were observed un-used, but identifying these usage patterns is crucial to predict eventual adoption of new designs from earlier stages of RT development. An interactive data visualization page supplement is provided to facilitate this study.

A Systematic Review of the Learning Dynamics of Proprioception Training: Specificity, Acquisition, Retention, and Transfer

OBJECTIVE

We aimed to identify key aspects of the learning dynamics of proprioception training including: 1) specificity to the training type, 2) acquisition of proprioceptive skills, 3) retention of learning effects, and 4) transfer to different proprioceptive skills.

METHODS

We performed a systematic literature search using the database (MEDLINE, EMBASE, Cochrane Library, and PEDro). The inclusion criteria required adult participants who underwent any training program that could enhance proprioceptive function, and at least 1 quantitative assessment of proprioception before and after the intervention. We analyzed within-group changes to quantify the effectiveness of an intervention.

RESULTS

In total, 106 studies with 343 participant-outcome groups were included. Proprioception-specific training resulted in large effect sizes with a mean improvement of 23.4 to 42.6%, nonspecific training resulted in medium effect sizes with 12.3 to 22% improvement, and no training resulted in small effect sizes with 5.0 to 8.9% improvement. Single-session training exhibited significant proprioceptive improvement immediately (10 studies). For training interventions with a midway evaluation (4 studies), trained groups improved by approximately 70% of their final value at the midway point. Proprioceptive improvements were largely maintained at a delayed follow-up of at least 1 week (12 studies). Finally, improvements in 1 assessment were significantly correlated with improvements in another assessment (10 studies).

CONCLUSIONS

Proprioceptive learning appears to exhibit several features similar to motor learning, including specificity to the training type, 2 time constant learning curves, good retention, and improvements that are correlated between different assessments, suggesting a possible, common mechanism for the transfer of training.

Robotically quantifying finger and ankle proprioception: Role of range, speed, anticipatory errors, and learning

Proprioception plays a key role in motor control and stroke recovery. Robotic devices are increasingly being used to improve proprioceptive assessments, but there is a lack of knowledge about how programmable factors such as testing range, speed, and prior exposure affect tests. From a physiological standpoint, such factors may regulate the sensitivity of limb proprioceptors, thereby influencing assessment results when not controlled for. To determine the relative influence of such factors, we studied the Crisscross proprioceptive assessment, a recently developed robotic assessment that requires participants to indicate when two joints pass by each other as they are moved passively by the robot. We implemented Crisscross with novel robots for the fingers and ankles and tested young unimpaired participants in single sessions (N = 16) and longitudinally (N = 5, across 15-30 sessions over 3-10 weeks). In single-session testing, we found that proprioceptive acuity was better for the fingers than the ankle (p < 0.01). For both limbs, acuity improved near the ends of the range of motion, which may be due to greater involvement of load and joint receptors. Acuity was poorer for slower movements due to greater anticipatory errors. These results show how the range and speed selected for a proprioceptive test affect proprioceptive acuity and highlight the heightened role of anticipatory errors at slow speeds. Improvements in proprioceptive acuity were not detectable in a single session, but acuity improved across multiple testing sessions (p < 0.01). This result shows that multiple prior exposure over at least several days can affect acuity.Clinical Relevance- Proprioceptive assessments should account for range and speed, which could be enabled by leveraging robotics technology. Proprioceptive acuity can be improved through repeated testing, an observation that is relevant to proprioceptive rehabilitation as well.

Exercise repetition rate measured with simple sensors at home can be used to estimate Upper Extremity Fugl-Meyer score after stroke

Introduction

It would be valuable if home-based rehabilitation training technologies could automatically assess arm impairment after stroke. Here, we tested whether a simple measure-the repetition rate (or "rep rate") when performing specific exercises as measured with simple sensors-can be used to estimate Upper Extremity Fugl-Meyer (UEFM) score.

Methods

41 individuals with arm impairment after stroke performed 12 sensor-guided exercises under therapist supervision using a commercial sensor system comprised of two pucks that use force and motion sensing to measure the start and end of each exercise repetition. 14 of these participants then used the system at home for three weeks.

Results

Using linear regression, UEFM score was well estimated using the rep rate of one forward-reaching exercise from the set of 12 exercises (r² = 0.75); this exercise required participants to alternately tap pucks spaced about 20 cm apart (one proximal, one distal) on a table in front of them. UEFM score was even better predicted using an exponential model and forward-reaching rep rate (Leave One Out Cross Validation (LOOCV) r² = 0.83). We also tested the ability of a nonlinear, multivariate model (a regression tree) to predict UEFM, but such a model did not improve prediction (LOOCV r² = 0.72). However, the optimal decision tree also used the forward-reaching task along with a pinch grip task to subdivide more and less impaired patients in a way consistent with clinical intuition. At home, rep rate for the forward-reaching exercise well predicted UEFM score using an exponential model (LOOCV r² = 0.69), but only after we re-estimated coefficients using the home data.

Discussion

These results show how a simple measure-exercise rep rate measured with simple sensors-can be used to infer an arm impairment score and suggest that prediction models should be tuned separately for the clinic and home environments.

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.

Optimized Home Rehabilitation Technology Reduces Upper Extremity Impairment Compared to a Conventional Home Exercise Program: A Randomized, Controlled, Single-Blind Trial in Subacute Stroke

BACKGROUND

Upper extremity (UE) stroke rehabilitation requires patients to perform exercises at home, yet patients show limited benefit from paper-based home exercise programs.

OBJECTIVE

To compare the effectiveness of 2 home exercise programs for reducing UE impairment: a paper-based approach and a sensorized exercise system that incorporates recommended design features for home rehabilitation technology.

METHODS

In this single-blind, randomized controlled trial, 27 participants in the subacute phase of stroke were assigned to the sensorized exercise (n = 14) or conventional therapy group (n = 13), though 2 participants in the conventional therapy group were lost to follow-up. Participants were instructed to perform self-guided movement training at home for at least 3 hours/week for 3 consecutive weeks. The sensorized exercise group used FitMi, a computer game with 2 puck-like sensors that encourages movement intensity and auto-progresses users through 40 exercises. The conventional group used a paper book of exercises. The primary outcome measure was the change in Upper Extremity Fugl-Meyer (UEFM) score from baseline to follow-up. Secondary measures included the Modified Ashworth Scale for spasticity (MAS) and the Visual Analog Pain (VAP) scale.

RESULTS

Participants who used FitMi improved by an average of 8.0 ± 4.6 points on the UEFM scale compared to 3.0 ± 6.1 points for the conventional participants, a significant difference (t-test, P = .029). FitMi participants exhibited no significant changes in UE MAS or VAP scores.

CONCLUSIONS

A sensor-based exercise system incorporating a suite of recommended design features significantly and safely reduced UE impairment compared to a paper-based, home exercise program.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03503617.

Targeting neuroplasticity to improve motor recovery after stroke: an artificial neural network model

After a neurological injury, people develop abnormal patterns of neural activity that limit motor recovery. Traditional rehabilitation, which concentrates on practicing impaired skills, is seldom fully effective. New targeted neuroplasticity protocols interact with the central nervous system to induce beneficial plasticity in key sites and thereby enable wider beneficial plasticity. They can complement traditional therapy and enhance recovery. However, their development and validation is difficult because many different targeted neuroplasticity protocols are conceivable, and evaluating even one of them is lengthy, laborious, and expensive. Computational models can address this problem by triaging numerous candidate protocols rapidly and effectively. Animal and human empirical testing can then concentrate on the most promising ones. Here, we simulate a neural network of corticospinal neurons that control motoneurons eliciting unilateral finger extension. We use this network to (i) study the mechanisms and patterns of cortical reorganization after a stroke; and (ii) identify and parameterize a targeted neuroplasticity protocol that improves recovery of extension torque. After a simulated stroke, standard training produced abnormal bilateral cortical activation and suboptimal torque recovery. To enhance recovery, we interdigitated standard training with trials in which the network was given feedback only from a targeted population of sub-optimized neurons. Targeting neurons in secondary motor areas on ∼20% of the total trials restored lateralized cortical activation and improved recovery of extension torque. The results illuminate mechanisms underlying suboptimal cortical activity post-stroke; they enable the identification and parameterization of the most promising targeted neuroplasticity protocols. By providing initial guidance, computational models could facilitate and accelerate the realization of new therapies that improve motor recovery.

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.

A Dynamic Wheelchair Armrest for Promoting Arm Exercise and Mobility After Stroke

Arm movement recovery after stroke can improve with sufficient exercise. However, rehabilitation therapy sessions are typically not enough. To address the need for effective methods of increasing arm exercise outside therapy sessions we developed a novel armrest, called Boost. It easily attaches to a standard manual wheelchair just like a conventional armrest and enables users to exercise their arm in a linear forward-back motion. This paper provides a detailed design description of Boost, the biomechanical analysis method to evaluate the joint torques required to operate it, and the results of pilot testing with five stroke patients. Biomechanics results show the required shoulder flexion and elbow extension torques range from −25% to +36% of the torques required to propel a standard pushrim wheelchair, depending on the direction of applied force. In pilot testing, all five participants were able to exercise the arm with Boost in stationary mode (with lower physical demand). Three achieved overground ambulation (with higher physical demand) exceeding 2 m/s after 2–5 practice trials; two of these could not propel their wheelchair with the pushrim. This simple to use, dynamic armrest provides people with hemiparesis a way to access repetitive arm exercise outside of therapy sessions, independently right in their wheelchair. Significantly, Boost removes the requirements to reach, grip, and release the pushrim to propel a wheelchair, an action many individuals with stroke cannot complete.

Using Large-Scale Sensor Data to Test Factors Predictive of Perseverance in Home Movement Rehabilitation: Optimal Challenge and Steady Engagement

Persevering with home rehabilitation exercise is a struggle for millions of people in the US each year. A key factor that may influence motivation to engage with rehabilitation exercise is the challenge level of the assigned exercises, but this hypothesis is currently supported only by subjective, self-report. Here, we studied the relationship between challenge level and perseverance using long-term, self-determined exercise patterns of a large number of individuals (N = 2,581) engaging in home rehabilitation with a sensor-based exercise system without formal supervision. FitMi is comprised of two puck-like sensors and a library of 40 gamified exercises for the hands, arms, trunk, and legs that are designed for people recovering from a stroke. We found that individuals showed the greatest perseverance with the system over a 2-month period if they had (1) a moderate level of motor impairment and (2) high but not perfect success during the 1st week at completing the exercise game. Further, a steady usage pattern (vs. accelerating or decelerating use) was associated with more overall exercise, and declines in exercise amount over time were associated with exponentially declining session initiation probability rather than decreasing amounts of exercise once a session was initiated. These findings confirm that an optimized challenge level and regular initiation of exercise sessions predict achievement of a greater amount of overall rehabilitation exercise in a group of users of commercial home rehabilitation technology and suggest how home rehabilitation programs and exercise technologies can be optimized to promote perseverance.

Proprioceptive Gaming: Making Finger Sensation Training Intense and Engaging with the P-Pong Game and PINKIE Robot

Proprioceptive deficits are common after a stroke and are thought to negatively impact motor learning. Despite this, there is a lack of practical robotic devices for assessing proprioception, as well as few robotic rehabilitation techniques that intensely and engagingly target proprioception. This work first presents the design of a simple robotic device, PINKIE, developed to assess and train finger proprioception. PINKIE uses low-cost actuators and sensors and is fabricated completely from 3D printed, laser cut, and off-the-shelf components. We then describe the design and testing of a gamified proprioceptive training technique, Proprioceptive-Pong (P-Pong), implemented with PINKIE. In P-Pong, players must continuously make game decisions based on sensed index and middle finger positions, as the game robotically moves their fingers instead of screen pixels to express the motion of the ball and paddle. We also report the results of a pilot study in which we investigated the effect of a short bout of P-Pong play on proprioceptive acuity, and quantified user engagement and intrinsic motivation of game play. We randomly assigned 15 unimpaired human participants to play 15 minutes of P-Pong (proprioceptive training group) or a similar but video-only version of Pong (control group). We assessed finger proprioception acuity before and after game play using the Crisscross assessment previously developed by our laboratory, engagement using the User Engagement Scale, and motivation using the Intrinsic Motivation Inventory survey. Following game play, there was a significant improvement in proprioceptive acuity (2.2 ± 2.6 SD mm, p = 0.023) in the proprioceptive training group but not the control group (0.5 ± 0.9 SD mm, p = 0.101). Participants rated P-Pong highly on all survey subscales, and as highly as visual Pong, except in the Perceived Usability and Competence subscales, a finding we discuss. To our knowledge, this work presents the first computer gaming approach for providing intense and engaging finger proprioception training, by splitting the feedback of game elements between the visual and proprioceptive senses. The pilot experiment indicates that the human sensory motor system has the ability to at least temporarily improve proprioception acuity with such game-based training.

Measuring Movement Quality of the Stroke-Impaired Upper Extremity with a Wearable Sensor: Toward a Smoothness Metric for Home Rehabilitation Exercise Programs

Remote patient monitoring systems show promise for assisting stroke patients in home exercise programs. While these systems typically measure exercise repetitions in order to monitor compliance, a key goal of therapists is to also monitor movement quality. Here we develop a measure of movement quality - Peak Intensity - that is a measure of movement smoothness that is implementable with a wrist-worn inertial measurement unit (IMU) in the context of performing repetitions of an upper extremity exercise. To calculate Peak Intensity, we assume we have an accurate count of the number of exercise repetitions in an exercise set, then calculate Peak Intensity as the total number of movement peaks from the continuous stream of IMU data generated across the set, divided by the number of repetitions. Using wrist-worn IMU measurements from 19 participants with chronic stroke performing a sample exercise in which they picked up and moved blocks across a divider (i.e. the Box and Blocks Test) we show that Peak Intensity is moderately correlated with a widely used measure of movement quality, the Quality of Movement score of the Motor Activity Log. Peak Intensity is also strongly correlated with a measure of hand function (the BBT score itself), but is more sensitive at greater levels of impairment. Finally, we show Peak Intensity can be validly derived from either wrist acceleration or angular velocity. These results suggest Peak Intensity could serve as an indicator of movement exercise quality for therapists monitoring home rehabilitation, and, potentially, as a means to provide augmented feedback to patients about their exercise quality.

A Pilot Study of a Sensor Enhanced Activity Management System for Promoting Home Rehabilitation Exercise Performed during the COVID-19 Pandemic: Therapist Experience, Reimbursement, and Recommendations for Implementation

Adherence to home exercise programs (HEPs) during physical rehabilitation is usually unmonitored and is thought to be low from self-reports. This article describes exploratory implementation of a Sensor Enhanced Activity Management (SEAM) system that combines HEP management software with a movement sensor for monitoring and motivating HEP adherence. The article also presents results from attempting to gain reimbursement for home use of the system with therapist oversight using Remote Physiologic Monitoring (RPM) codes. Four therapists used the system in their regular practice during the first six months of the COVID-19 pandemic. Therapists filled out surveys, kept notes, and participated in interviews. Billing and reimbursement data were obtained from the treatment facility. Exercise data from the SEAM system were used to understand HEP adherence. Patients were active for a mean of 40% (26% SD) of prescribed days and completed a mean of 25% (25% SD) of prescribed exercises. The therapists billed 23 RPM codes (USD 2353), and payers reimbursed eight of those instances (USD 649.21). The therapists reported that remote monitoring and the use of a physical movement sensor was motivating to their patients and increased adherence. Sustained technical support for therapists will likely improve implementation of new remote monitoring and treatment systems. RPM codes may enable reimbursement for review and program management activities, but, despite COVID-19 CMS waivers, organizations may have more success if these services are billed under supervision of a physician.

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.

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.

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.

Rudimentary Dexterity Corresponds With Reduced Ability to Move in Synergy After Stroke: Evidence of Competition Between Corticoreticulospinal and Corticospinal Tracts?

OBJECTIVE

When a stroke damages the corticospinal tract (CST), it has been hypothesized that the motor system switches to using the corticoreticulospinal tract (CRST) resulting in abnormal arm synergies. Is use of these tracts mutually exclusive, or can the motor system spontaneously switch between them depending on the type of movement it wants to make? If the motor system can share control at will, then people with a rudimentary ability to make dexterous movements should be able to perform synergistic arm movements as well.

METHODS

We analyzed clinical assessments of 319 persons' abilities to perform "out-of-synergy" and "in-synergy" arm movements after chronic stroke using the Upper Extremity Fugl-Meyer (UEFM) scale.

RESULTS

We identified a moderate range of arm impairment (UEFM = ~30-40) where subjects had a rudimentary ability to make out-of-synergy (~23%-50% on the out-of-synergy score) and dexterous hand movements (~3-10 blocks on Box and Blocks Test). Below this range persons could perform in-synergy but not out-of-synergy or dexterous movements. In the moderate range, however, scoring better on out-of-synergy movements correlated with scoring worse on in-synergy movements (P = .001, r ≈ -0.6).

CONCLUSION

Rudimentary dexterity corresponded with reduced ability to move the arm in-synergy. This finding supports the idea that CST and CRST compete and has implications for rehabilitation therapy.

Feasibility of Wearable Sensing for In-Home Finger Rehabilitation Early After Stroke

Wearable grip sensing shows potential for hand rehabilitation, but few studies have studied feasibility early after stroke. Here, we studied a wearable grip sensor integrated with a musical computer game (MusicGlove). Among the stroke patients admitted to a hospital without limiting complications, 13% had adequate hand function for system use. Eleven subjects used MusicGlove at home over three weeks with a goal of nine hours of use. On average they achieved 4.1 ± 3.2 (SD) hours of use and completed 8627 ± 7500 grips, an amount comparable to users in the chronic phase of stroke measured in a previous study. The rank-order usage data were well fit by distributions that arise in machine failure theory. Users operated the game at high success levels, achieving note-hitting success >75% for 84% of the 1061 songs played. They changed game parameters infrequently (31% of songs), but in a way that logically modulated challenge, consistent with the Challenge Point Hypothesis from motor learning. Thus, a therapy based on wearable grip sensing was feasible for home rehabilitation, but only for a fraction of subacute stroke subjects. Subjects made usage decisions consistent with theoretical models of machine failure and motor learning.

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.

Breaking Proportional Recovery After Stroke

People with hemiparesis after stroke appear to recover 70% to 80% of the difference between their baseline and the maximum upper extremity Fugl-Meyer (UEFM) score, a phenomenon called proportional recovery (PR). Two recent commentaries explained that PR should be expected because of mathematical coupling between the baseline and change score. Here we ask, If mathematical coupling encourages PR, why do a fraction of stroke patients (the "nonfitters") not exhibit PR? At the neuroanatomical level of analysis, this question was answered by Byblow et al-nonfitters lack corticospinal tract (CST) integrity at baseline-but here we address the mathematical and behavioral causes. We first derive a new interpretation of the slope of PR: It is the average probability of scoring across remaining scale items at follow-up. PR therefore breaks when enough test items are discretely more difficult for a patient at follow-up, flattening the slope of recovery. For the UEFM, we show that nonfitters are most unlikely to recover the ability to score on the test items related to wrist/hand dexterity, shoulder flexion without bending the elbow, and finger-to-nose movement, supporting the finding that nonfitters lack CST integrity. However, we also show that a subset of nonfitters respond better to robotic movement training in the chronic phase of stroke. These persons are just able to move the arm out of the flexion synergy and pick up small blocks, both markers of CST integrity. Nonfitters therefore raise interesting questions about CST function and the basis for response to intensive movement training.

Development and Evaluation of MOVit: An Exercise-Enabling Interface for Driving a Powered Wheelchair

Powered wheelchair users can experience negative health effects from reduced physical activity. If a user could exercise by driving the chair, it might improve fitness. This paper presents the development of MOVit, an exercise-enabling, wheelchair driving interface. The design goal of MOVit was that users cyclically move their arms to drive the chair, thereby providing a light level of exercise while driving. MOVit supports this arm movement with custom mobile arm supports that also serve as the sensors that provide controller inputs. Here, we first quantified how increasing the frequency and amplitude of arm movement increase oxygen consumption and heart rate. Then, we evaluated two novel control methods for driving by moving the arm supports. Participants without impairment ( N = 24 ) were randomized to one of the two methods, or conventional joystick control, and performed driving tests over two days on a simulator and test course. Our results indicate that driving speed and accuracy were significantly lowered with the exercise-enabling methods compared to joystick control (ANOVA, ), but the decreases were small (speed was ~0.1 m/s less and course tracking error ~1 cm greater). These results show, for the first time, the feasibility of exercising while driving a powered wheelchair.

Neural Correlates of Passive Position Finger Sense After Stroke

Background. Proprioception of fingers is essential for motor control. Reduced proprioception is common after stroke and is associated with longer hospitalization and reduced quality of life. Neural correlates of proprioception deficits after stroke remain incompletely understood, partly because of weaknesses of clinical proprioception assessments. Objective. To examine the neural basis of finger proprioception deficits after stroke. We hypothesized that a model incorporating both neural injury and neural function of the somatosensory system is necessary for delineating proprioception deficits poststroke. Methods. Finger proprioception was measured using a robot in 27 individuals with chronic unilateral stroke; measures of neural injury (damage to gray and white matter, including corticospinal and thalamocortical sensory tracts), neural function (activation of and connectivity of cortical sensorimotor areas), and clinical status (demographics and behavioral measures) were also assessed. Results. Impairment in finger proprioception was present contralesionally in 67% and bilaterally in 56%. Robotic measures of proprioception deficits were more sensitive than standard scales and were specific to proprioception. Multivariable modeling found that contralesional proprioception deficits were best explained (r² = 0.63; P = .0006) by a combination of neural function (connectivity between ipsilesional secondary somatosensory cortex and ipsilesional primary motor cortex) and neural injury (total sensory system injury). Conclusions. Impairment of finger proprioception occurs frequently after stroke and is best measured using a quantitative device such as a robot. A model containing a measure of neural function plus a measure of neural injury best explained proprioception performance. These measurements might be useful in the development of novel neurorehabilitation therapies.

The Effectiveness of Protective Headgear in Attenuating Ball-to-Forehead Impacts in Water Polo

Recent reports have demonstrated that there is a serious risk of head impact and injury in water polo. The use of protective headgear in contact sports is a commonly accepted strategy for reducing the risk of head injury, but there are few available protective headgears for use in water polo. Many of those that are available are banned by the sport's governing bodies due to a lack of published data supporting the effectiveness of those headgears in reducing head impact kinematics. To address this gap in knowledge, we launched a water polo ball at the forehead of an anthropomorphic testing device fitted with either a standard water polo headgear or one of two protective headgears. We selected a range of launch speeds representative of those observed across various athlete ages. Mixed-model ANOVAs revealed that, relative to standard headgear, protective headgears reduced peak linear acceleration (by 10.8-21.6%; p < 0.001), and peak rotational acceleration (by 24.5-48.5%; p < 0.001) induced by the simulated ball-to-forehead impacts. We discuss the possibility of using protective headgears in water polo to attenuate head impact kinematics.

Somatosensory system integrity explains differences in treatment response after stroke

OBJECTIVE

To test the hypothesis that, in the context of robotic therapy designed to enhance proprioceptive feedback via a Hebbian model, integrity of both somatosensory and motor systems would be important in understanding interparticipant differences in treatment-related motor gains.

METHODS

In 30 patients with chronic stroke, behavioral performance, neural injury, and neural function were quantified for somatosensory and motor systems. Patients then received a 3-week robot-based therapy targeting finger movements with enhanced proprioceptive feedback.

RESULTS

Hand function improved after treatment (Box and Blocks score increase of 2.8 blocks, p = 0.001) but with substantial variability: 9 patients showed improvement exceeding the minimal clinically important difference (6 blocks), while 8 patients (all of whom had >2-SD greater proprioception deficit compared to 25 healthy controls) showed no improvement. In terms of baseline behavioral assessments, a somatosensory measure (finger proprioception assessed robotically) best predicted treatment gains, outperforming all measures of motor behavior. When the neural basis underlying variability in treatment response was examined, somatosensory-related variables were again the strongest predictors. A multivariate model combining total sensory system injury and sensorimotor cortical connectivity (between ipsilesional primary motor and secondary somatosensory cortices) explained 56% of variance in treatment-induced hand functional gains (p = 0.002).

CONCLUSIONS

Measures related to the somatosensory network best explained interparticipant differences in treatment-related hand function gains. These results underscore the importance of baseline somatosensory integrity for improving hand function after stroke and provide insights useful for individualizing rehabilitation therapy.

CLINICALTRIALSGOV IDENTIFIER

NCT02048826.

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.

Directly Measuring the Rate of Slacking as Stroke Survivors produced Isometric Forces during a Tracking Task

Slacking limits the rehabilitative effectiveness of certain exercises following stroke. When patients receive assistance during an exercise, they exhibit a persistent tendency to reduce their own contribution to that exercise. This phenomenon was first coined 'slacking' in the context of robot-mediated therapy, where controller design continues to involve prediction and mitigation of slacking. In this pilot study, 14 individuals in the chronic stage of stroke participated in a visuomotor tracking task during which they produced isometric grip forces. Visual feedback displayed on a monitor helped participants track eight distinct forces ranging effort level from 4 to 30% maximum voluntary contraction (MVC). A specialized method of toggling between veridical and nonveridical visual feedback isolated each participant's realtime slacking rate at each of the eight effort levels, with both their contralesional and ipsilesional hand. Below 10-15% MVC, participants did not slack. At higher effort levels, participants slacked, and their slacking rate increased non-linearly with effort. Slacking took the form of smooth reductions in grip force. On average, across participants, slacking rates were remarkably similar between hands, just marginally faster with the contralesional hand. However, individualized slacking rates varied from almost zero to approximately double the acrossparticipant average. The paradigm for measuring slacking rate, used here, might be incorporated into robot-mediated therapy to maintain an accurate, individualized estimate of a patient's slacking rate at various force levels and ensure the robot provides assistance only as needed.

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.

Controlling pre-movement sensorimotor rhythm can improve finger extension after stroke

OBJECTIVE

Brain-computer interface (BCI) technology is attracting increasing interest as a tool for enhancing recovery of motor function after stroke, yet the optimal way to apply this technology is unknown. Here, we studied the immediate and therapeutic effects of BCI-based training to control pre-movement sensorimotor rhythm (SMR) amplitude on robot-assisted finger extension in people with stroke.

APPROACH

Eight people with moderate to severe hand impairment due to chronic stroke completed a four-week three-phase protocol during which they practiced finger extension with assistance from the FINGER robotic exoskeleton. In Phase 1, we identified spatiospectral SMR features for each person that correlated with the intent to extend the index and/or middle finger(s). In Phase 2, the participants learned to increase or decrease SMR features given visual feedback, without movement. In Phase 3, the participants were cued to increase or decrease their SMR features, and when successful, were then cued to immediately attempt to extend the finger(s) with robot assistance.

MAIN RESULTS

Of the four participants that achieved SMR control in Phase 2, three initiated finger extensions with a reduced reaction time after decreasing (versus increasing) pre-movement SMR amplitude during Phase 3. Two also extended at least one of their fingers more forcefully after decreasing pre-movement SMR amplitude. Hand function, measured by the box and block test (BBT), improved by 7.3  ±  7.5 blocks versus 3.5  ±  3.1 blocks in those with and without SMR control, respectively. Higher BBT scores at baseline correlated with a larger change in BBT score.

SIGNIFICANCE

These results suggest that learning to control person-specific pre-movement SMR features associated with finger extension can improve finger extension ability after stroke for some individuals. These results merit further investigation in a rehabilitation context.

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.

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.

How do strength and coordination recovery interact after stroke? A computational model for informing robotic training

Robotic devices can train strength, coordination, or a combination of both. If a robotic device focuses on coordination, what happens to strength recovery, and vice versa? Understanding this interaction could help optimize robotic training. We developed a computational neurorehabilitation model to gain insight into the interaction between strength and coordination recovery after stroke. In the model, the motor system recovers by optimizing the activity of residual corticospinal cells (focally connected, excitatory and inhibitory) and reticulospinal cells (diffusely connected and excitatory) to achieve a motor task. To do this, the model employs a reinforcement learning algorithm that uses stochastic search based on a reward signal produced by task execution. We simulated two tasks that require strength and coordination: a finger movement task and a bilateral wheelchair propulsion task. We varied the reward signal to value strength versus coordination, determined by a weighting factor. The model predicted a nonlinear relationship between strength and coordination recovery consistent with clinical data obtained for each task. The model also predicted that stroke can cause a competition between strength and coordination recovery, due to a scarcity of focal and inhibitory cells. These results provide a rationale for implementing robotic movement therapy that can adaptively alter the combination of force and coordination training to target desired components of motor recovery.

Robotic Assistance for Training Finger Movement Using a Hebbian Model: A Randomized Controlled Trial

BACKGROUND

Robots that physically assist movement are increasingly used in rehabilitation therapy after stroke, yet some studies suggest robotic assistance discourages effort and reduces motor learning.

OBJECTIVE

To determine the therapeutic effects of high and low levels of robotic assistance during finger training.

METHODS

We designed a protocol that varied the amount of robotic assistance while controlling the number, amplitude, and exerted effort of training movements. Participants (n = 30) with a chronic stroke and moderate hemiparesis (average Box and Blocks Test 32 ± 18 and upper extremity Fugl-Meyer score 46 ± 12) actively moved their index and middle fingers to targets to play a musical game similar to GuitarHero 3 h/wk for 3 weeks. The participants were randomized to receive high assistance (causing 82% success at hitting targets) or low assistance (55% success). Participants performed ~8000 movements during 9 training sessions.

RESULTS

Both groups improved significantly at the 1-month follow-up on functional and impairment-based motor outcomes, on depression scores, and on self-efficacy of hand function, with no difference between groups in the primary endpoint (change in Box and Blocks). High assistance boosted motivation, as well as secondary motor outcomes (Fugl-Meyer and Lateral Pinch Strength)-particularly for individuals with more severe finger motor deficits. Individuals with impaired finger proprioception at baseline benefited less from the training.

CONCLUSIONS

Robot-assisted training can promote key psychological outcomes known to modulate motor learning and retention. Furthermore, the therapeutic effectiveness of robotic assistance appears to derive at least in part from proprioceptive stimulation, consistent with a Hebbian plasticity model.

Home-based hand rehabilitation after chronic stroke: Randomized, controlled single-blind trial comparing the MusicGlove with a conventional exercise program

Individuals with chronic stroke have limited options for hand rehabilitation at home. Here, we sought to determine the feasibility and efficacy of home-based MusicGlove therapy. Seventeen participants with moderate hand impairment in the chronic phase of stroke were randomized to 3 wk of home-based exercise with either the MusicGlove or conventional tabletop exercises. The primary outcome measure was the change in the Box and Blocks test score from baseline to 1 mo posttreatment. Both groups significantly improved their Box and Blocks test score, but no significant difference was found between groups. The MusicGlove group did exhibit significantly greater improvements than the conventional exercise group in motor activity log quality of movement and amount of use scores 1 mo posttherapy (p = 0.007 and p = 0.04, respectively). Participants significantly increased their use of MusicGlove over time, completing 466 gripping movements per day on average at study end. MusicGlove therapy was not superior to conventional tabletop exercises for the primary end point but was nevertheless feasible and led to a significantly greater increase in self-reported functional use and quality of movement of the impaired hand than conventional home exercises.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov; "Influence of Timing on Motor Learning"; NCT01769326; https://clinicaltrials.gov/ct2/show/NCT01769326.

Wearable sensing for rehabilitation after stroke: Bimanual jerk asymmetry encodes unique information about the variability of upper extremity recovery

Wearable sensing is a new tool for quantifying upper extremity (UE) rehabilitation after stroke. However, it is unclear whether it provides information beyond what is available through standard clinical assessments. To investigate this question, people with a chronic stroke (n=9) wore accelerometers on both wrists for 9 hours on a single day during their daily activities. We used principal components analysis (PCA) to characterize how novel kinematic measures of jerk and acceleration asymmetry, along with conventional measures of limb use asymmetry and clinical function, explained the behavioral variance of UE recovery across participants. The first PC explained 55% of the variance and described a strong correlation between standard clinical assessments and limb use asymmetry, as has been observed previously. The second PC explained a further 31% of the variance and described a strong correlation between bimanual magnitude and jerk asymmetry. Because of the nature of PCA, this second PC is mathematically orthogonal to the first and thus uncorrelated with the clinical assessments. Therefore, kinematic metrics obtainable from bimanual accelerometry, including bimanual jerk asymmetry, encoded additional information about UE recovery. One interpretation is that the first PC relates to "functional status" and the second to "movement quality". We also describe a new graphical format for presenting bimanual wrist accelerometry data that facilitates identification of asymmetries.

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.

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.

A Home-Based Telerehabilitation Program for Patients With Stroke

BACKGROUND

Although rehabilitation therapy is commonly provided after stroke, many patients do not derive maximal benefit because of access, cost, and compliance. A telerehabilitation-based program may overcome these barriers. We designed, then evaluated a home-based telerehabilitation system in patients with chronic hemiparetic stroke.

METHODS

Patients were 3 to 24 months poststroke with stable arm motor deficits. Each received 28 days of telerehabilitation using a system delivered to their home. Each day consisted of 1 structured hour focused on individualized exercises and games, stroke education, and an hour of free play.

RESULTS

Enrollees (n = 12) had baseline Fugl-Meyer (FM) scores of 39 ± 12 (mean ± SD). Compliance was excellent: participants engaged in therapy on 329/336 (97.9%) assigned days. Arm repetitions across the 28 days averaged 24,607 ± 9934 per participant. Arm motor status showed significant gains (FM change 4.8 ± 3.8 points, P = .0015), with half of the participants exceeding the minimal clinically important difference. Although scores on tests of computer literacy declined with age ( r = -0.92; P < .0001), neither the motor gains nor the amount of system use varied with computer literacy. Daily stroke education via the telerehabilitation system was associated with a 39% increase in stroke prevention knowledge ( P = .0007). Depression scores obtained in person correlated with scores obtained via the telerehabilitation system 16 days later ( r = 0.88; P = .0001). In-person blood pressure values closely matched those obtained via this system ( r = 0.99; P < .0001).

CONCLUSIONS

This home-based system was effective in providing telerehabilitation, education, and secondary stroke prevention to participants. Use of a computer-based interface offers many opportunities to monitor and improve the health of patients after stroke.

There is plenty of room for motor learning at the bottom of the Fugl-Meyer: Acquisition of a novel bimanual wheelchair skill after chronic stroke using an unmasking technology

Many people with a stroke have a severely paretic arm, and it is often assumed that they are unable to learn novel, skilled behaviors that incorporate use of that arm. Here, we show that a group of people with chronic stroke (n = 5, upper extremity Fugl-Meyer scores: 31, 30, 26, 22, 8) learned to use their impaired arm to propel a novel, yoked-clutch lever drive wheelchair. Over six daily training sessions, each involving about 134 training movements with their "useless" arm, the users gradually achieved a 3-fold increase in wheelchair speed on average, with a 4-6 fold increase for three of the participants. They did this by learning a bimanual skill: pushing the levers with both arms while activating the yoked-clutches at the right time with their ipsilesional (i.e. "good") hand to propel the wheelchair forward. They perceived the task as highly motivating and useful. The speed improvements exceeded a 1.5-factor improvement observed when young, unimpaired users learned to propel the chair. The learning rate also exceeded a sample of learning rates from a variety of classic learning studies. These results suggest that appropriately-designed assistive technologies (or "unmasking technologies - UTs") can unleash a powerful, latent ability for motor learning even for severely paretic arms. While UTs may not reduce clinical impairment, they may facilitate large improvements in a specific functional ability.

Model-based assistance-as-needed for robotic movement therapy after stroke

This paper extends an adaptive control approach for robotic movement therapy that learns deficiencies in a patient's neuromuscular output and assists accordingly. In this method, adaptation is based on trajectory tracking error and a model of unimpaired motor control forces. The controller presented here adaptively learns and fills the gaps in the patient's ability to generate inertial forces, instead of just static forces, as has been proposed before. To test this method, a two dimensional model of an impaired human arm was used to simulate reaching movements in the horizontal plane. The results from simulation demonstrate that the inertia-based controller assists more effectively without need for increasing the controller's impedance, which suggests that modeling inertial forces during robot movement therapy could improve the ability of robots to deliver assistance-as-needed.

Design of a thumb module for the FINGER rehabilitation robot

This paper describes the design and initial prototype of a thumb curling exoskeleton for movement therapy. This add-on device for the Finger INdividuating Grasp Exercise Robot (FINGER) guides the thumb through a single-degree-of-freedom naturalistic grasping motion. This motion complements the grasping motions of the index and middle fingers provided by FINGER. The kinematic design and mechanism synthesis described herein utilized 3D motion capture and included the determination of the principle plane of the thumb motion for the simple grasping movement. The results of the design process and the creation of a first prototype indicate that this thumb module for finger allows naturalistic thumb motion that expands the capabilities of the FINGER device.

Journal of NeuroEngineering and Rehabilitation reviewer acknowledgement 2015

The editors of Journal of NeuroEngineering and Rehabilitation would like to thank all of our reviewers who have contributed to the journal in volume 12 (2015).

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.

Abstract TP155: Robotic Retraining of Finger Movements After Stroke

Background: Robots aid motor rehabilitation, but there has been limited attention to recovery of finger movements. This study evaluated robotic assistance during finger movement training. Functional MRI (fMRI) was acquired at baseline to understand predictors of treatment gains.

Methods: Patients with chronic stroke underwent a baseline fMRI scan, alternating rest with affected-side finger movements similar to those made during robotic therapy. Next, subjects received therapy 3 hr/wk for 3 weeks using FINGER (Finger Individuating Grasp Exercise Robot), with which subjects moved their paretic index and middle fingers to play a musical game similar to GuitarHero. FINGER used an assistance-as-needed algorithm to facilitate completion of grasping movements, which increased sensory feedback without altering voluntary motor output. Participants were randomized to receive High Assistance (to insure 85% success) or Low Assistance (55% success).

Results: 30 subjects (mean age 58 yr; baseline Fugl-Meyer 46 out of 66; 37 mo post-stroke) completed the study. Significant gains were found in the primary outcome measure, change in Box & Blocks (B&B) score (23 to 25.5, p<0.0001). There was no difference between High and Low Assistance groups in the primary endpoint (p=0.65), though some secondary outcomes favored High Assistance. The fMRI scans found that greater treatment gains were associated with higher laterality index in primary sensory cortex, indicating greater boost in B&B score over time with higher pretreatment balance of activation towards ipsilesional rather than contralesional sensory cortex; laterality index in primary motor cortex lacked predictive value.

Conclusions: Significant motor gains were found with a robotic device that targets finger movements. Sensory factors appear key: treatment content emphasized augmented sensory feedback, and the hemispheric balance of fMRI activation within sensory but not motor cortex predicted treatment gains. Together these findings suggest Hebbian rules of sensorimotor cortex plasticity during finger robotic therapy after stroke.

Robotic Rehabilitator of the Rodent Upper Extremity: A System and Method for Assessing and Training Forelimb Force Production after Neurological Injury

Rodent models of spinal cord injury are critical for the development of treatments for upper limb motor impairment in humans, but there are few methods for measuring forelimb strength of rodents, an important outcome measure. We developed a novel robotic device--the Robotic Rehabilitator of the Rodent Upper Extremity (RUE)--that requires rats to voluntarily reach for and pull a bar to retrieve a food reward; the resistance of the bar can be programmed. We used RUE to train forelimb strength of 16 rats three times per week for 23 weeks before and 38 weeks after a mild (100 kdyne) unilateral contusion at the cervical level 5 (C5). We measured maximum force produced when RUE movement was unexpectedly blocked. We compared this blocked pulling force (BPF) to weekly measures of forelimb strength obtained with a previous, well-established method: the grip strength meter (GSM). Before injury, BPF was 2.6 times higher (BPF, 444.6 ± 19.1 g; GSM, 168.4 ± 3.1 g) and 4.9 times more variable (p < 0.001) than pulling force measured with the GSM; the two measurement methods were uncorrelated (R(2) = 0.03; p = 0.84). After injury, there was a significant decrease in BPF of 134.35 g ± 14.71 g (p < 0.001). Together, our findings document BPF as a repeatable measure of forelimb force production, sensitive to a mild spinal cord injury, which comes closer to measuring maximum force than the GSM and thus may provide a useful measure for quantifying the effects of treatment in rodent models of SCI.

The variable relationship between arm and hand use: a rationale for using finger magnetometry to complement wrist accelerometry when measuring daily use of the upper extremity

Wrist-worn accelerometers are becoming more prevalent as a means to assess use of the impaired upper extremity in daily life after stroke. However, wrist accelerometry does not measure joint movements of the hand, which are integral to functional use of the upper extremity. In this study, we used a custom-built, non-obtrusive device called the manumeter to measure both arm use (via wrist accelerometry) and hand use (via finger magnetometry) of a group of unimpaired subjects while they performed twelve motor tasks at three intensities. We also gave the devices to four stroke subjects and asked them to wear them for six hours a day for one month. From the in-lab testing we found that arm use was a strong predictor of hand use for individual tasks, but that the slope of the relationship varied by up to a factor of ~12 depending on the task being performed. Consistent with this, in the daily use data collected from stroke subjects we found a broad spread in the relationship between arm and hand use. These results suggest that analyzing the spread of the relationship between daily hand and arm use will give more insight into upper extremity recovery than wrist accelerometry or finger magnetometry alone, because the spread reflects the nature of the daily tasks performed as well as the amount of upper extremity use.

A novel device for studying weight supported, quadrupedal overground locomotion in spinal cord injured rats

BACKGROUND

Providing weight support facilitates locomotion in spinal cord injured animals. To control weight support, robotic systems have been developed for treadmill stepping and more recently for overground walking.

NEW METHOD

We developed a novel device, the body weight supported ambulatory rodent trainer (i.e. BART). It has a small pneumatic cylinder that moves along a linear track above the rat. When air is supplied to the cylinder, the rats are lifted as they perform overground walking. We tested the BART device in rats that received a moderate spinal cord contusion injury and in normal rats. Locomotor training with the BART device was not performed.

RESULTS

All of the rats learned to walk in the BART device. In the contused rats, significantly greater paw dragging and dorsal stepping occurred in the hindlimbs compared to normal. Providing weight support significantly raised hip position and significantly reduced locomotor deficits. Hindlimb stepping was tightly coupled to forelimb stepping but only when the contused rats stepped without weight support. Three weeks after the contused rats received a complete spinal cord transection, significantly fewer hindlimb steps were performed.

COMPARISON WITH EXISTING METHODS

Relative to rodent robotic systems, the BART device is a simpler system for studying overground locomotion. The BART device lacks sophisticated control and sensing capability, but it can be assembled relatively easily and cheaply.

CONCLUSIONS

These findings suggest that the BART device is a useful tool for assessing quadrupedal, overground locomotion which is a more natural form of locomotion relative to treadmill locomotion.