Noninvasive spinal stimulation improves walking in chronic stroke survivors: a proof-of-concept case series.

BACKGROUND: After stroke, restoring safe, independent, and efficient walking is a top rehabilitation priority. However, in nearly 70% of stroke survivors asymmetrical walking patterns and reduced walking speed persist. This case series study aims to investigate the effectiveness of transcutaneous spinal cord stimulation (tSCS) in enhancing walking ability of persons with chronic stroke. METHODS: Eight participants with hemiparesis after a single, chronic stroke were enrolled. Each participant was assigned to either the Stim group (N = 4, gait training + tSCS) or Control group (N = 4, gait training alone). Each participant in the Stim group was matched to a participant in the Control group based on age, time since stroke, and self-selected gait speed. For the Stim group, tSCS was delivered during gait training via electrodes placed on the skin between the spinous processes of C5-C6, T11-T12, and L1-L2. Both groups received 24 sessions of gait training over 8 weeks with a physical therapist providing verbal cueing for improved gait symmetry. Gait speed (measured from 10 m walk test), endurance (measured from 6 min walk test), spatiotemporal gait symmetries (step length and swing time), as well as the neurophysiological outcomes (muscle synergy, resting motor thresholds via spinal motor evoked responses) were collected without tSCS at baseline, completion, and 3 month follow-up. RESULTS: All four Stim participants sustained spatiotemporal symmetry improvements at the 3 month follow-up (step length: 17.7%, swing time: 10.1%) compared to the Control group (step length: 1.1%, swing time 3.6%). Additionally, 3 of 4 Stim participants showed increased number of muscle synergies and/or lowered resting motor thresholds compared to the Control group. CONCLUSIONS: This study provides promising preliminary evidence that using tSCS as a therapeutic catalyst to gait training may increase the efficacy of gait rehabilitation in individuals with chronic stroke. Trial registration NCT03714282 (clinicaltrials.gov), registration date: 2018-10-18.

Early Prediction of Poststroke Rehabilitation Outcomes Using Wearable Sensors.

OBJECTIVE: Inpatient rehabilitation represents a critical setting for stroke treatment, providing intensive, targeted therapy and task-specific practice to minimize a patient's functional deficits and facilitate their reintegration into the community. However, impairment and recovery vary greatly after stroke, making it difficult to predict a patient's future outcomes or response to treatment. In this study, the authors examined the value of early-stage wearable sensor data to predict 3 functional outcomes (ambulation, independence, and risk of falling) at rehabilitation discharge. METHODS: Fifty-five individuals undergoing inpatient stroke rehabilitation participated in this study. Supervised machine learning classifiers were retrospectively trained to predict discharge outcomes using data collected at hospital admission, including patient information, functional assessment scores, and inertial sensor data from the lower limbs during gait and/or balance tasks. Model performance was compared across different data combinations and was benchmarked against a traditional model trained without sensor data. RESULTS: For patients who were ambulatory at admission, sensor data improved the predictions of ambulation and risk of falling (with weighted F1 scores increasing by 19.6% and 23.4%, respectively) and maintained similar performance for predictions of independence, compared to a benchmark model without sensor data. The best-performing sensor-based models predicted discharge ambulation (community vs household), independence (high vs low), and risk of falling (normal vs high) with accuracies of 84.4%, 68.8%, and 65.9%, respectively. Most misclassifications occurred with admission or discharge scores near the classification boundary. For patients who were nonambulatory at admission, sensor data recorded during simple balance tasks did not offer predictive value over the benchmark models. CONCLUSION: These findings support the continued investigation of wearable sensors as an accessible, easy-to-use tool to predict the functional recovery after stroke. IMPACT: Accurate, early prediction of poststroke rehabilitation outcomes from wearable sensors would improve our ability to deliver personalized, effective care and discharge planning in the inpatient setting and beyond.

Validity and reliability of a commercial wearable sensor system for measuring spatiotemporal gait parameters in a post-stroke population: the effects of walking speed and asymmetry.

Objective.Commercial wearable sensor systems are a promising alternative to costly laboratory equipment for clinical gait evaluation, but their accuracy for individuals with gait impairments is not well established. Therefore, we investigated the validity and reliability of the APDM Opal wearable sensor system to measure spatiotemporal gait parameters for healthy controls and individuals with chronic stroke.Approach.Participants completed the 10 m walk test over an instrumented mat three times in different speed conditions. We compared performance of Opal sensors to the mat across different walking speeds and levels of step length asymmetry in the two populations.Main results. Gait speed and stride length measures achieved excellent reliability, though they were systematically underestimated by 0.11 m s-1and 0.12 m, respectively. The stride and step time measures also achieved excellent reliability, with no significant errors (median absolute percentage error <6.00%,p> 0.05). Gait phase duration measures achieved moderate-to-excellent reliability, with relative errors ranging from 4.13%-21.59%. Across gait parameters, the relative error decreased by 0.57%-9.66% when walking faster than 1.30 m s-1; similar reductions occurred for step length symmetry indices lower than 0.10.Significance. This study supports the general use of Opal wearable sensors to obtain quantitative measures of post-stroke gait impairment. These measures should be interpreted cautiously for individuals with moderate-severe asymmetry or walking speeds slower than 0.80 m s-1.

Soft robotic exosuit augmented high intensity gait training on stroke survivors: a pilot study.

BACKGROUND: Stroke is a leading cause of serious gait impairments and restoring walking ability is a major goal of physical therapy interventions. Soft robotic exosuits are portable, lightweight, and unobtrusive assistive devices designed to improve the mobility of post-stroke individuals through facilitation of more natural paretic limb function during walking training. However, it is unknown whether long-term gait training using soft robotic exosuits will clinically impact gait function and quality of movement post-stroke. OBJECTIVE: The objective of this pilot study was to examine the therapeutic effects of soft robotic exosuit-augmented gait training on clinical and biomechanical gait outcomes in chronic post-stroke individuals. METHODS: Five post-stroke individuals received high intensity gait training augmented with a soft robotic exosuit, delivered in 18 sessions over 6-8 weeks. Performance based clinical outcomes and biomechanical gait quality parameters were measured at baseline, midpoint, and completion. RESULTS: Clinically meaningful improvements were observed in walking speed ([Formula: see text] < 0.05) and endurance ([Formula: see text] < 0.01) together with other traditional gait related outcomes. The gait quality measures including hip ([Formula: see text] < 0.01) and knee ([Formula: see text] < 0.05) flexion/extension exhibited an increase in range of motion in a symmetric manner ([Formula: see text] < 0.05). We also observed an increase in bilateral ankle angular velocities ([Formula: see text] < 0.05), suggesting biomechanical improvements in walking function. CONCLUSIONS: The results in this study offer preliminary evidence that a soft robotic exosuit can be a useful tool to augment high intensity gait training in a clinical setting. This study justifies more expanded research on soft exosuit technology with a larger post-stroke population for more reliable generalization. Trial registration This study is registered with ClinicalTrials.gov (ID: NCT04251091).

Relationship between gait quality measures and modular neuromuscular control parameters in chronic post-stroke individuals.

BACKGROUND: Recent evidence suggests that disinhibition and/or hyperexcitation of the brainstem descending pathways and intraspinal motor network diffuse spastic synergistic activation patterns after stroke. This results in simplified or merged muscle sets (i.e., muscle modules or synergies) compared to non-impaired individuals and this leads to poor walking performance. However, the relations of how these neuromuscular deficits influence gait quality (e.g., symmetry or natural walking patterns) are still unclear. The objective of this exploratory study was to investigate the relations of modular neuromuscular framework and gait quality measures in chronic stroke individuals. METHODS: Sixteen chronic post-stroke individuals participated in this study. Full lower body three-dimensional kinematics and electromyography (EMG) were concurrently measured during overground walking at a comfortable speed. We first examined changes in gait quality measures across the number of muscle modules using linear regression model. Then, a stepwise multiple regression was used to investigate the optimal combination of the neuromuscular parameters that associates with gait quality measures. RESULTS: We observed that subjects who had a lower number of muscle modules revealed reduced function (i.e., speed) and greater asymmetry in the kinematic parameters including limb length, footpath area, knee flexion/extension, and hip abduction/adduction (all p < 0.05). We also found that the combination of input variables from the modular neuromuscular control framework significantly associated with gait quality measures (average [Formula: see text]). Those variables included variability accounted for ([Formula: see text]) information from the muscle modules and area under the EMG envelope curves of the quadriceps (i.e., rectus femoris and vastus lateralis) and tibialis anterior muscles. CONCLUSIONS: The results suggest that there exists a significant correlation between the neuromuscular control framework and the gait quality measures. This study helps to understand the underlying mechanism of disturbances in gait quality and provides insight for a more comprehensive outcome measure to assess gait impairment after stroke.

Rapid Screening of Physiological Changes Associated With COVID-19 Using Soft-Wearables and Structured Activities: A Pilot Study.

OBJECTIVE: Controlling the spread of the COVID-19 pandemic largely depends on scaling up the testing infrastructure for identifying infected individuals. Consumer-grade wearables may present a solution to detect the presence of infections in the population, but the current paradigm requires collecting physiological data continuously and for long periods of time on each individual, which poses limitations in the context of rapid screening. Technology: Here, we propose a novel paradigm based on recording the physiological responses elicited by a short (~2 minutes) sequence of activities (i.e. "snapshot"), to detect symptoms associated with COVID-19. We employed a novel body-conforming soft wearable sensor placed on the suprasternal notch to capture data on physical activity, cardio-respiratory function, and cough sounds. RESULTS: We performed a pilot study in a cohort of individuals (n=14) who tested positive for COVID-19 and detected altered heart rate, respiration rate and heart rate variability, relative to a group of healthy individuals (n=14) with no known exposure. Logistic regression classifiers were trained on individual and combined sets of physiological features (heartbeat and respiration dynamics, walking cadence, and cough frequency spectrum) at discriminating COVID-positive participants from the healthy group. Combining features yielded an AUC of 0.94 (95% CI=[0.92, 0.96]) using a leave-one-subject-out cross validation scheme. Conclusions and Clinical Impact: These results, although preliminary, suggest that a sensor-based snapshot paradigm may be a promising approach for non-invasive and repeatable testing to alert individuals that need further screening.

Does kinematic gait quality improve with functional gait recovery? A longitudinal pilot study on early post-stroke individuals.

Typical clinical gait outcomes mostly focus on function; only sparse information exists on gait quality, i.e. symmetry or more natural gait patterns. It remains unclear whether functional gait recovery improves with gait quality, or whether these are two independent processes. The objective of this observational pilot study is to examine whether the gait quality improves with gait function (i.e. speed) over the course of early recovery. Full lower body gait kinematics were measured longitudinally in a clinical environment using wearable inertial measurement units. We recorded six individuals with subacute stroke (<1 month) for a total of 56 physical therapy sessions over the initial recovery stage of 12 weeks. We examined relations between gait symmetry in spatiotemporal, limb and joint kinematic parameters compared to gait function. We observed that overall gait symmetry improved with walking speed, but limb and joint kinematic parameters remained asymmetric at the maximum level of recovery (both p < 0.01). We also found that limb kinematic parameters (R2 = 41.9%) of the impaired side was preferentially associated with functional gait recovery over joint kinematics (R2 = 33.1%). These data suggest that our pilot cohort did not achieve "true" gait recovery despite achieving typical measures of recovery in gait speed and spatiotemporal symmetry. These initial results illustrate the multifaceted nature of recovery and justify further research on monitoring gait quality with a larger clinical study, providing insight for more effective training regimens.

Quantifying dosage of physical therapy using lower body kinematics: a longitudinal pilot study on early post-stroke individuals.

BACKGROUND: While therapy is an important part of the recovery process, there is a lack of quantitative data detailing the "dosage" of therapy received due to the limitations on in/outpatient accessibility and mobility. Advances in wearable sensor technology have allowed us to obtain an unprecedented glimpse into joint-level kinematics in an unobtrusive manner. The objective of this observational longitudinal pilot study was to evaluate the relations between lower body joint kinematics during therapy and functional gait recovery over the first three months after stroke. METHODS: Six individuals with subacute stroke (< 1 month) were monitored for a total of 59 one-hour physical therapy sessions including gait and non-gait activities. Participants donned a heart rate monitor and an inertial motion capture system to measure full lower body joint kinematics during each therapy session. Linear mixed regression models were used to examine relations between functional gait recovery (speed) and activity features including total joint displacements, defined as amount of motion (AoM), step number, change in heart rate (∆HR), and types of tasks performed. RESULTS: All activity features including AoM, step number, types of tasks performed (all p < 0.01), and ∆HR (p < 0.05) showed strong associations with gait speed. However, AoM (R2 = 32.1%) revealed the greatest explained variance followed by step number (R2 = 14.1%), types of tasks performed (R2 = 8.0%) and ∆HR (R2 = 5.8%). These relations included both gait and non-gait tasks. Contrary to our expectations, we did not observe a greater relation of functional recovery to motion in the impaired limb (R2 = 27.8%) compared to the unimpaired limb (R2 = 32.9%). CONCLUSIONS: This proof-of-concept study shows that recording joint kinematics during gait therapy longitudinally after stroke is feasible and yields important information for the recovery process. These initial results suggest that compared to step number, more holistic outcome measures such as joint motions may be more informative and help elucidate the dosage of therapy.

An Online Transition of Speed-dependent Reference Joint Trajectories for Robotic Gait Training.

Rehabilitation robots reduce the physical burden on therapists, quantify training and allow greater dose of therapy on individuals with neurological impairments. Robots are also capable of precisely customizing therapy based on the user's physiology and/or needs, for example, customizing a reference trajectory for gait training. While a number of methods for obtaining reference gait patterns have been proposed, these approaches lack the ability of altering the trajectories according to the varying walking speed in real-time. The objective of this paper is to develop an online algorithm that can provide a continuous, speed-dependent reference gait pattern for robotic gait training. We employed Fourier series and profile blending methods to generate natural transitions in gait patterns, and synchronized the gait cycle time according to the given arbitrary walking speed. The simulation results suggest that the algorithm can stably change the gait patterns with the given walking speed in a synchronous manner. We conclude that the method can provide online speed-dependent walking motion that can be used for general robotic gait training applications.

Sensitivity comparison of inertial to optical motion capture during gait: implications for tracking recovery.

Wearable sensors provide a foundation for development of wearable robotic technology to be used in clinical applications. Inertial motion capture (IMC) has emerged as a viable alternative to more cumbersome, non-portable optical methods. Previous work has validated the accuracy of IMC for gait compared to optical motion capture (OMC). However, it is unclear how well IMC can measure the small changes in gait function needed to gauge recovery. In this study, we evaluate the sensitivity of IMC compared to OMC to small changes in gait on a cohort of unimpaired individuals during treadmill walking. Eight individuals walked on a split-belt treadmill in three-minute trials with five randomized conditions: right belt speed decrementing at 0.05 m/s from 1.0 m/s, all with left belt held at 1.0 m/s, simulating recovery of hemiparetic gait. We extracted the root mean square deviation (RMSD) of joint kinematics between limbs and within the limb with modulated gait speed as the main outcome measure. We used linear mixed models to identify differences in sensitivity to changes in gait asymmetry and gait speed. Based on these models, we estimated the minimal detectible interval in gait parameters. We found that IMC was capable of measuring a difference in gait speed of 0.08 m/s, roughly the equivalent of two weeks recovery progress. Statistically we could not conclude a difference of sensitivity between IMC and OMC, although there is a strong trend that IMC is more sensitive to changes in gait. We conclude that IMC is a valid tool to measure progress in gait kinematics over the course of recovery.

Statistical method for prediction of gait kinematics with Gaussian process regression.

We propose a novel methodology for predicting human gait pattern kinematics based on a statistical and stochastic approach using a method called Gaussian process regression (GPR). We selected 14 body parameters that significantly affect the gait pattern and 14 joint motions that represent gait kinematics. The body parameter and gait kinematics data were recorded from 113 subjects by anthropometric measurements and a motion capture system. We generated a regression model with GPR for gait pattern prediction and built a stochastic function mapping from body parameters to gait kinematics based on the database and GPR, and validated the model with a cross validation method. The function can not only produce trajectories for the joint motions associated with gait kinematics, but can also estimate the associated uncertainties. Our approach results in a novel, low-cost and subject-specific method for predicting gait kinematics with only the subject's body parameters as the necessary input, and also enables a comprehensive understanding of the correlation and uncertainty between body parameters and gait kinematics.

Intentional Movement Performance Ability (IMPA): a method for robot-aided quantitative assessment of motor function.

The purpose of this paper is to propose a new assessment method for evaluating motor function of the patients who are suffering from physical weakness after stroke, incomplete spinal cord injury (iSCI) or other diseases. In this work, we use a robotic device to obtain the information of interaction occur between patient and robot, and use it as a measure for assessing the patients. The Intentional Movement Performance Ability (IMPA) is defined by the root mean square of the interactive torque, while the subject performs given periodic movement with the robot. IMPA is proposed to quantitatively determine the level of subject's impaired motor function. The method is indirectly tested by asking the healthy subjects to lift a barbell to disturb their motor function. The experimental result shows that the IMPA has a potential for providing a proper information of the subject's motor function level.