Background: Gait is often impaired in people after stroke, restricting personal independence and affecting quality of life. During stroke rehabilitation, walking capacity is conventionally assessed by measuring walking distance and speed. Gait features, such as asymmetry and variability, are not routinely determined, but may provide more specific insights into the patient’s walking capacity. Inertial measurement units offer a feasible and promising tool to determine these gait features. Objective: We examined the test–retest reliability of inertial measurement units-based gait features measured in a two-minute walking assessment in people after stroke and while in clinical rehabilitation. Method: Thirty-one people after stroke performed two assessments with a test–retest interval of 24 h. Each assessment consisted of a two-minute walking test on a 14-m walking path. Participants were equipped with three inertial measurement units, placed at both feet and at the low back. In total, 166 gait features were calculated for each assessment, consisting of spatio-temporal (56), frequency (26), complexity (63), and asymmetry (14) features. The reliability was determined using the intraclass correlation coefficient. Additionally, the minimal detectable change and the relative minimal detectable change were computed. Results: Overall, 107 gait features had good–excellent reliability, consisting of 50 spatio-temporal, 8 frequency, 36 complexity, and 13 symmetry features. The relative minimal detectable change of these features ranged between 0.5 and 1.5 standard deviations. Conclusion: Gait can reliably be assessed in people after stroke in clinical stroke rehabilitation using three inertial measurement units.

Balance is often affected after stroke, severely impacting activities of daily life. Conventional testing methods to assess balance provide limited information, as they are subjected to floor and ceiling effects. Instrumented tests, for instance using inertial measurement units, offer a feasible and promising alternative. We examined whether postural sway can reliably be measured in sitting and standing balance in people after stroke in clinical rehabilitation using a single inertial measurement unit. Additionally, we assessed to what extent averaging two measurements would improve test-retest reliability compared to a single measurement, and if sway features can potentially be used to monitor progression. Forty participants performed two assessments with a test-retest interval of 24 h. Each assessment consisted of one sitting and four standing balance conditions (eyes open, feet together, eyes closed and foam). The standing balance conditions were performed twice during both assessments. In total, 35 sway features were calculated for each condition. For the standing balance conditions, these were calculated for both single test-retest measurement and the average of the two test and retest measurements. We determined the reliability using the intraclass correlation coefficient for both single and averaged measurements. Additionally, the minimal detectable change and the relative minimal detectable change were computed. The single and averaged measurements resulted in 22 sitting, 30 & 32 eyes open, 27 & 22 feet together, 28 & 33 eyes closed and 23 & 13 foam sway features with good-excellent reliability. Overall, the difference between intraclass correlation coefficient values of the single and averaged measurements was small and inconsistent. The relative minimal detectable change ranged between 0.5 and 1.5 standard deviation. Sitting and standing balance can reliably be assessed in people after stroke in clinical rehabilitation with a single measurement using one inertial measurement unit.

Background: Variational AutoEncoders (VAE) might be utilized to extract relevant information from an IMU-based gait measurement by reducing the sensor data to a low-dimensional representation. The present study explored whether VAEs can reduce IMU-based gait data of people after stroke into a few latent features with minimal reconstruction error. Additionally, we evaluated the psychometric properties of the latent features in comparison to gait speed, by assessing 1) their reliability; 2) the difference in scores between people after stroke and healthy controls; and 3) their responsiveness during rehabilitation. Methods: We collected test-retest and longitudinal two-minute walk-test data using an IMU from people after stroke in clinical rehabilitation, as well as from a healthy control group. IMU data were segmented into 5-second epochs, which were reduced to 12 latent-feature scores using a VAE. The between-day test-retest reliability of the latent features was assessed using ICC-scores. The differences between the healthy and the stroke group were examined using an independent t-test. Lastly, the responsiveness was determined as the number of individuals who significantly changed during rehabilitation. Results: In total, 15,381 epochs from 107 people after stroke and 37 healthy controls were collected. The VAE achieved data reconstruction with minimal errors. Five latent features demonstrated good-to-excellent test-retest reliability. Seven latent features were significantly different between groups. We observed changes during rehabilitation for 21 and 20 individuals in latent-feature scores and gait speed, respectively. However, the direction of the change scores of the latent features was ambiguous. Only eleven individuals exhibited changes in both latent-feature scores and gait speed. Conclusion: VAEs can be used to effectively reduce IMU-based gait assessment to a concise set of latent features. Some latent features had a high test-retest reliability and differed significantly between healthy controls and people after stroke. Further research is needed to determine their clinical applicability.

Background: How gait changes during the early stages of stoke rehabilitation, and which patient characteristics are associated with these changes is still largely unknown. Objective: he first objective was to describe the changes in gait during stroke rehabilitation. Secondly, we determined how various patient characteristics were associated with the rate of change of gait over time. Methods: Participants were measured every 3 weeks during stroke rehabilitation. The assessment consisted of an inertial measurement unit (IMU) based 2-minute walk test (2MWT), 3 IMU-based balance tests, and standard clinical tests. In the 2MWT, participants were equipped with 3 IMUs, from which speed, variability, asymmetry, and smoothness were calculated. The changes in gait were examined from admission to discharge at an individual level. The effect of patient characteristics on the rate of change of the gait features over time was assessed with growth models. Results: A total of 81 Trajectories from 72 participants were analyzed. On an individual basis, speed increased in 32 trajectories. Only a few trajectories exhibited significant changes in variability, asymmetry, and smoothness over the clinical rehabilitation period. The growth models revealed a significant increase in speed and decrease in variability and smoothness. Only the Berg Balance Scale and gait speed at onset were (negatively) associated with the rates of change of speed and smoothness, respectively. Conclusion: We found a substantial variability in the gait-feature outcomes and their progression in individuals after stroke during clinical rehabilitation. The patient characteristics studied had limited associations with the rate of change of gait features over time.

Background: Gait speed is often used to estimate the walking ability in daily life in people after stroke. While measuring gait with inertial measurement units (IMUs) during clinical assessment yields additional information, it remains unclear if this information can improve the estimation of the walking ability in daily life beyond gait speed. Objective: We evaluated the additive value of IMU-based gait features over a simple gait-speed measurement in the estimation of walking ability in people after stroke. Methods: Longitudinal data during clinical stroke rehabilitation were collected. The assessment consisted of two parts and was administered every three weeks. In the first part, participants walked for two minutes (2MWT) on a fourteen-meter path with three IMUs attached to low back and feet, from which multiple gait features, including gait speed, were calculated. The dimensionality of the corresponding gait features was reduced with a principal component analysis. In the second part, gait was measured for two consecutive days using one ankle-mounted IMU. Next, three measures of walking ability in daily life were calculated, including the number of steps per day, and the average and maximal gait speed. A gait-speed-only Linear Mixed Model was used to estimate the association between gait speed and each of the three measures of walking ability. Next, the principal components (PC), derived from the 2MWT, were added to the gait-speed-only model to evaluate if they were confounders or effect modifiers. Results: Eighty-one participants were measured during rehabilitation, resulting in 198 2MWTs and 135 corresponding walking-performance measurements. 106 Gait features were reduced to nine PCs with 85.1% explained variance. The linear mixed models demonstrated that gait speed was weakly associated with the average and maximum gait speed in daily life and moderately associated with the number of steps per day. The PCs did not considerably improve the outcomes in comparison to the gait speed only models. Conclusions: Gait in people after stroke assessed in a clinical setting with IMUs differs from their walking ability in daily life. More research is needed to determine whether these discrepancies also occur in non-laboratory settings, and to identify additional non-gait factors that influence walking ability in daily life.

Introduction: A key element of personalized stroke rehabilitation is early prediction of an individual's potential to walk in the community.Objective: We aim to determine the predictive value of patient characteristics, clinical test results, and Inertial Measurement Units (IMU) based balance, clinical gait and daily-life measures, measured at admission and discharge in clinical stroke rehabilitation, for community walking 6 months after stroke.Methods: Data were collected from people after stroke during clinical rehabilitation and at 6 months post stroke. The assessment during rehabilitation consisted of an IMU-based 2-min walk test (2MWT), three IMU-based balance tests, an IMU-based measurement of gait in daily life, and several standard clinical tests, including the Berg Balance Scale, Barthel Index, Functional Ambulation Categories, Motricity Index (MI), and Trunk Control Test (TCT). At 6-months, gait in daily life was measured with an IMU for two consecutive days. From this measurement, three gait features were calculated, namely the strides per day, and average and maximum gait speed. We assessed the predictive value of IMU-based balance, gait, and daily-life measures, the clinical tests and patient characteristics at admission and discharge for predicting daily-life measures at 6 months after stroke with univariate ordinary least squares regression. Subsequently, significant predictors were included in a multivariate ordinary least squares regression.Results: Thirty-five individuals after stroke were included. Ordinary least squares regression analysis indicated that age, gait features and strides per day at admission and discharge had significant predictive value for the step count at 6 months. For the average and maximum gait speed in daily life at 6 months, the 2MWT gait speed, TCT, MI and the baseline average and maximum gait speed in daily life were significant predictors. Multivariate analysis indicated that the outcomes at admission had more predictive value than the outcomes at discharge, with adjusted R² values for the strides per day, average and maximum gait speed models of 0.60, 0.42, and 0.53, respectively.Conclusions: Age, trunk stability (TCT), affected leg strength (MI), and the clinical and daily-life gait had predictive value for community walking 6-months after stroke. Future research with a larger sample size is required to refine these findings.