Recent technologic advancements have enabled the creation of portable, low-cost, and unobtrusive sensors with tremendous potential to alter the clinical practice of rehabilitation. The application of wearable sensors to track movement has emerged as a promising paradigm to enhance the care provided to patients with neurologic or musculoskeletal conditions. These sensors enable quantification of motor behavior across disparate patient populations and emerging research shows their potential for identifying motor biomarkers, differentiating between restitution and compensation motor recovery mechanisms, remote monitoring, telerehabilitation, and robotics. Moreover, the big data recorded across these applications serve as a pathway to personalized and precision medicine. This article presents state-of-the-art and next-generation wearable movement sensors, ranging from inertial measurement units to soft sensors. An overview of clinical applications is presented across a wide spectrum of conditions that have potential to benefit from wearable sensors, including stroke, movement disorders, knee osteoarthritis, and running injuries. Complementary applications enabled by next-generation sensors that will enable point-of-care monitoring of neural activity and muscle dynamics during movement also are discussed.

https://onlinelibrary.wiley.com/doi/pdf/10.1016/j.pmrj.2018.06.013

Wearable Movement Sensors for Rehabilitation: A Focused Review of Technological and Clinical Advances

The recovery of walking capacity is one of the main aims in stroke rehabilitation. Being able to predict if and when a patient is going to walk after stroke is of major interest in terms of management of the patients and their family’s expectations and in terms of discharge destination and timing previsions. This article reviews the recent literature regarding the predictive factors for gait recovery and the best recommendations in terms of gait rehabilitation in stroke patients. Trunk control and lower limb motor control (e.g. hip extensor muscle force) seem to be the best predictors of gait recovery as shown by the TWIST algorithm, which is a simple tool that can be applied in clinical practice at 1 week post-stroke. In terms of walking performance, the 6-min walking test is the best predictor of community ambulation. Various techniques are available for gait rehabilitation, including treadmill training with or without body weight support, robotic-assisted therapy, virtual reality, circuit class training and self-rehabilitation programmes. These techniques should be applied at specific timing during post-stroke rehabilitation, according to patient’s functional status.

Gait rehabilitation after stroke: review of the evidence of predictors, clinical outcomes and timing for interventions

Background and Purpose- Therapeutic strategies that capitalize on the intrinsic capacity for neurological recovery early poststroke to improve locomotion are uncertain. Emerging data suggest that task-specific stepping practice provided at higher cardiovascular intensities may be critical dosage parameters that could maximize locomotor recovery. The purpose of this investigation was to determine the comparative effectiveness of providing high-intensity training on locomotor capacity early poststroke as compared with usual care. Methods- A quasi-experimental design was used to compare changes in stepping activity (StepWatch), walking, and balance outcomes during usual care (n=56) versus high-intensity stepping intervention (n=54) in inpatient stroke patients. Primary outcomes assessed weekly included self-selected and fastest gait speed, 6-minute walk test, and the Berg Balance Scale, with secondary outcomes of Swedish Postural Assessment Scale for Stroke-Norwegian version, Functional Ambulation Category, 30-s sit-to-stand, strength (average manual muscle testing), and Barthel Index. Regression analyses identified relationships between demographics, baseline function, and training activities (steps per day; duration achieved, 70%-85% maximum heart rates) and primary outcomes at discharge. Results- Following implementation of high-intensity stepping, average steps per day (5777±2784) were significantly greater than during usual care (3917±2656; P<0.001). Statistically different and clinically meaningful changes in self-selected speed (0.39±0.28 versus 0.16±0.26 m/s) and fastest gait speed (0.47±0.41 versus 0.17±0.38 m/s; both P<0.001) were observed following high-intensity interventions versus usual care and at every assessment throughout the length of stay. Changes in Berg Balance Scale and 6-minute walk test were also statistically and clinically different between groups, while secondary measures of Functional Ambulation Category and strength were also different at discharge. Primary predictors of improved walking capacity were steps per day, baseline impairments, and age. Conclusions- Provision of high-intensity stepping training applied during inpatient rehabilitation resulted in significantly greater walking and balance outcomes. This training paradigm should be further tested in other contexts to determine the generalizability to real-world and community settings.

https://www.ahajournals.org/doi/pdf/10.1161/STROKEAHA.119.027450?download=true

Implementation of High-Intensity Stepping Training During Inpatient Stroke Rehabilitation Improves Functional Outcomes.

Objective: Soft robotic exosuits can improve the mechanics and energetics of walking after stroke. Building on this prior work, we evaluated the effects of the first prototype of a portable soft robotic exosuit. Methods: Exosuit-induced changes in the overground walking speed, distance, and energy expenditure of individuals post-stroke were evaluated statistically and compared to minimal clinically important difference scores. Results: Compared to walking without the exosuit worn, the $ 5 kg exosuit did not substantially modify speed, distance, or energy expenditure when worn unpowered. In contrast, when powered on to provide an average 22.87 $\pm$ 0.58 %bodyweight of paretic plantarflexor force assistance during stance phase and assist the paretic dorsiflexors during swing phase to reduce drop-foot, study participants walked a median 0.14 $\pm$ 0.06 m/s faster during the 10-meter walk test and traveled 32 $\pm$ 8 m farther during the six minute walk test ( $P ). Conclusions: Individuals post-stroke can leverage the paretic plantarflexor and dorsiflexor assistance provided by soft robotic exosuits to achieve clinically-meaningful increases in speed and distance.

Walking Faster and Farther With a Soft Robotic Exosuit: Implications for Post-Stroke Gait Assistance and Rehabilitation

Objective This retrospective cohort study identified inpatient rehabilitation admission variables that predict walking ability at discharge and established Berg Balance Scale cut-off scores to predict the extent of improvement in walking. Methods Participants (n=123) were assessed for various cognitive and physical outcomes at admission to inpatient stroke rehabilitation. Multivariate logistic regression identified admission predictors of regaining community ambulation (gait speed ≥0.8 m/s) or unassisted ambulation (no physical assistance) after 4 weeks. Receiver operating characteristic curve analysis identified cut-off admission Berg Balance Scale scores. Results Mini-Mental State Examination (odds ratio (OR) 1.60, 95% confidence interval (95% CI) 1.19-2.14) was a significant predictor when coupled with admission walking speed for regaining community ambulation speed; stroke type (haemorrhagic/ischaemic) was a significant predictor (OR=0.19, 95% CI 0.05-0.77) when coupled with Berg Balance Scale (OR 1.14, 95% CI 1.09-1.20). Only Berg Balance Scale was a significant predictor of regaining unassisted ambulation (OR 1.11, 95% CI 1.05-1.17). A cut-off Berg Balance Scale score of 29 on admission predicts that an individual will go on to achieve community walking speed (n=123, area under the curve (AUC)=0.88, 95% CI 0.81-0.95); a cut-off score of 12 predicts a non-ambulator to regain unassisted ambulation (n=84, AUC 0.73, 95% CI 0.62-0.84). Conclusion The Berg Balance Scale can be used at rehabilitation admission to predict the degree of improvement in walking for patients with stroke.

/pdf/berg-balance-scale-score-at-admission-can-predict-walking-r26tly0w3e.pdf

Berg Balance Scale score at admission can predict walking suitable for community ambulation at discharge from inpatient stroke rehabilitation.

Background and Purpose—Walking ability poststroke is commonly assessed using gait speed categories developed by Perry et al. The purpose of this study was to reexamine factors that predict home and...

Predicting Home and Community Walking Activity Poststroke

BACKGROUND AND PURPOSE The 6-minute walk test (6MWT) is commonly used in people with stroke. The purpose of this study was to estimate the minimal clinically important difference (MCID) of the 6MWT 2 months poststroke. METHODS We performed a secondary analysis of data from a rehabilitation trial. Participants underwent physical therapy between 2 and 6 months poststroke and the 6MWT was measured before and after. Two anchors of important change were used: the modified Rankin Scale (mRS) and the Stroke Impact Scale (SIS). The MCID for the 6MWT was estimated using receiver operating characteristic curves for the entire sample and for 2 subgroups: initial gait speed (IGS) <0.40 m/s and ≥0.40 m/s. RESULTS For the entire sample, the estimated MCID of the 6MWT was 71 m with the mRS as the anchor (area under the curve [AUC] = 0.66) and 65 m with the SIS as the anchor (AUC = 0.59). For participants with IGS <0.40 m/s, the estimated MCID was 44 m with the mRS as the anchor (AUC = 0.72) and 34 m with the SIS as the anchor (AUC = 0.62). For participants with IGS ≥0.40 m/s, the estimated MCID was 71 m with the mRS as the anchor (AUC = 0.59) and 130 m with the SIS as the anchor (AUC = 0.56). DISCUSSION AND CONCLUSIONS Between 2 and 6 months poststroke, people whose IGS is <0.40 m/s and experience a 44-m improvement in the 6MWT may exhibit meaningful improvement in disability. However, we were not able to estimate an accurate MCID for the 6MWT in people whose IGS was ≥0.40 m/s. MCID values should be estimated across different levels of function and anchors of importance.Video Abstract available for more insights from the authors (see Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A232).

Minimal Clinically Important Difference of the 6-Minute Walk Test in People With Stroke.

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Predicting Home and Community Walking Activity Poststroke

Minimal Clinically Important Difference of the 6-Minute Walk Test in People With Stroke.