Movement Diversity and Complexity Increase as Arm Impairment Decreases After Stroke: Quality of Movement Experience as a Possible Target for Wearable Feedback.

Upper extremity (UE) impairment is common after stroke resulting in reduced arm use in daily life. A few studies have examined the use of wearable feedback of the quantity of arm movement to promote recovery, but with limited success. We posit that it may be more effective to encourage an increase in beneficial patterns of movement practice - i.e. the overall quality of the movement experience - rather than simply the overall amount of movement. As a first step toward testing this idea, here we sought to identify statistical features of the distributions of daily arm movements that become more prominent as arm impairment decreases, based on data obtained from a wrist IMU worn by 22 chronic stroke participants during their day. We identified several measures that increased as UE Fugl-Meyer (UEFM) score increased: the fraction of movements achieved at a higher speed, forearm postural diversity (quantified by kurtosis of the tilt-angle), and forearm postural complexity (quantified by sample entropy of tilt angle). Dividing participants into severe, moderate, and mild impairment groups, we found that forearm postural diversity and complexity were best able to distinguish the groups (Cohen's D =1.1, and 0.99, respectively) and were also the best subset of predictors for UEFM score. Based on these findings coupled with theories of motor learning that emphasize the importance of variety and challenge in practice, we suggest that using these measures of diversity and complexity in wearable rehabilitation could provide a basis to test whether the quality of the daily movement experience is therapeutic.

Real-Time Sensing of Upper Extremity Movement Diversity Using Kurtosis Implemented on a Smartwatch.

Wearable activity sensors typically count movement quantity, such as the number of steps taken or the number of upper extremity (UE) counts achieved. However, for some applications, such as neurologic rehabilitation, it may be of interest to quantify the quality of the movement experience (QOME), defined, for example, as how diverse or how complex movement epochs are. We previously found that individuals with UE impairment after stroke exhibited differences in their distributions of forearm postures across the day and that these differences could be quantified with kurtosis-an established statistical measure of the peakedness of distributions. In this paper, we describe further progress toward the goal of providing real-time feedback to try to help people learn to modulate their movement diversity. We first asked the following: to what extent do different movement activities induce different values of kurtosis? We recruited seven unimpaired individuals and evaluated a set of 12 therapeutic activities for their forearm postural diversity using kurtosis. We found that the different activities produced a wide range of kurtosis values, with conventional rehabilitation therapy exercises creating the most spread-out distribution and cup stacking the most peaked. Thus, asking people to attempt different activities can vary movement diversity, as measured with kurtosis. Next, since kurtosis is a computationally expensive calculation, we derived a novel recursive algorithm that enables the real-time calculation of kurtosis. We show that the algorithm reduces computation time by a factor of 200 compared to an optimized kurtosis calculation available in SciPy, across window sizes. Finally, we embedded the kurtosis algorithm on a commercial smartwatch and validated its accuracy using a robotic simulator that "wore" the smartwatch, emulating movement activities with known kurtosis. This work verifies that different movement tasks produce different values of kurtosis and provides a validated algorithm for the real-time calculation of kurtosis on a smartwatch. These are needed steps toward testing QOME-focused, wearable rehabilitation.

Using Large-Scale Sensor Data to Test Factors Predictive of Perseverance in Home Movement Rehabilitation: Early Exercise Frequency and Schedule Consistency.

Home-based exercises are an important component of stroke rehabilitation but are seldom fully completed. Past studies of exercise perseverance in the general public have suggested the importance of early exercise frequency and schedule consistency (in terms of which days of the week exercises are performed) because they encourage habit formation. To test whether these observations apply after a stroke, we leveraged data from 2,583 users of a sensor-based system (FitMi) developed to motivate movement exercises at home. We grouped users based on their early exercise frequency (defined across the initial 6 weeks of use) and calculated the evolution of habit score (defined as exercise frequency multiplied by exercise duration) across 6 months. We found that habit score decayed exponentially over time but with a slower decay constant for individuals with higher early frequency. Only the group with an early exercise frequency of 4 days/week or more had non-zero habit score at six months. Within each frequency group, dividing individuals into higher and lower consistency subgroups revealed that the higher consistency subgroups had significantly higher habit scores. These results are consistent with previous studies on habit formation in exercise and may help in designing effective home rehabilitation programs after stroke.

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.

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.

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.

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.

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.

Feasibility of home hand rehabilitation using musicglove after chronic spinal cord injury.

STUDY DESIGN: Randomized, controlled single-blind cross over study. This study was registered on ClinicalTrials.gov (NCT02473614). OBJECTIVES: Examine usership patterns and feasibility of MusicGlove for at home hand rehabilitation therapy following chronic spinal cord injury. SETTING: Homes of participants. METHODS: Ten participants with chronic spinal cord injury completed two baseline assessments of hand function. After a stable baseline was determined all participants were randomized into two groups: Experimental and Control. Each group was given a recommended therapy dosage. Following this participants switched interventions. RESULTS: On average participants had higher levels of compliance (6.1 ± 3.5 h.), and completed more grips (15,760 ± 9,590 grips) compared to participants in previous stroke studies using the same device. Participants modulated game parameters in a manner consistent with optimal challenge principles from motor learning theory. Participants in the experimental group increased their prehension ability (1 ± 1.4 MusicGlove, 0.2 ± 0.5 Control) and performance (1.4 ± 2.2 MusicGlove, 0.4 ± 0.55 Control) on the Graded and Redefined Assessment of Strength, Sensibility, and Prehension subtests. Increases in performance on the Box and Blocks Test also favored the experimental group compared to the conventional group at the end of therapy (4.2 ± 5.9, -1.0 ± 3.4 respectively). CONCLUSIONS: MusicGlove is a feasible option for hand therapy in the home-setting for individuals with chronic SCI. Participants completed nearly twice as many gripping movements compared to individuals from the sub-acute and chronic stroke populations, and a number far greater than the number of movements typically achieved during traditional rehabilitation.

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.

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.

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.

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.

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.