Evaluation of postural instability in stroke patient during quiet standing

Automatic characterization of stroke patients' posturography based on probability density analysis

OBJECTIVE

The probability density analysis was applied to automatically characterize the center of pressure (COP) data for evaluation of the stroke patients' balance ability.

METHODS

The real-time COP coordinates of 38 stroke patients with eyes open and closed during quiet standing were obtained, respectively, from a precision force platform. The COP data were analyzed and characterized by the commonly used parameters: total sway length (SL), sway radius (SR), envelope sway area (EA), and the probability density analysis based parameters: projection area (PA), skewness (SK) and kurtosis (KT), and their statistical correlations were analyzed. The differences of both conventional parameters and probability density parameters under the conditions of eyes open (EO) and eyes closed (EC) were compared.

RESULTS

The PA from probability density analysis is strongly correlated with SL and SR. Both the traditional parameters and probability density parameters in the EC state are significantly different from those in the EO state. The obtained various statokinesigrams were calculated and categorized into typical sway types through probability density function for clinical evaluation of the balance ability of stroke patients.

CONCLUSIONS

The probability density analysis of COP data can be used to characterize the posturography for evaluation of the balance ability of stroke patients.

Characterization of Postural Control in Post-Stroke Patients by Musculoskeletal Simulation

An association is observed between the standing sway posture and falls in patients with stroke; hence, it is important to study their standing balance. Although there are studies on the standing balance in stroke patients, differences in control have not been adequately investigated. This study aims to propose a method to characterize the postural sway in standing stroke patients using a mathematical model. A musculoskeletal model and neural controller model were used to simulate ten stroke patients (five patients with cerebral hemorrhages and five patients with cerebral infarctions) and eight young healthy participants, and their data were monitored during quiet standing. The model parameters were adjusted by focusing on the maximum-minimum difference in sway, which was considered important in a previous study, and sway speed, which is frequently used in the analysis. The adjusted model parameters were subjected to dimension reduction using non-negative matrix factorization. Consequently, the sway characteristics of stroke patients were expressed as the magnitude of gain parameters related to the extension of the entire body. The results of this study demonstrated the possibility of representing the characteristics of postural sway as model parameters in stroke patients using a mathematical model. This characterization could lead to the design of individualized rehabilitation systems in the future.

Evaluation of Postural Sway in Post-stroke Patients by Dynamic Time Warping Clustering

Post-stroke complications are the second most frequent cause of death and the third leading cause of disability worldwide. The motor function of post-stroke patients is often assessed by measuring the postural sway in the patients during quiet standing, based on sway measures, such as sway area and velocity, which are obtained from temporal variations of the center of pressure. However, such approaches to establish a relationship between the sway measures and patients' demographic factors have hardly been successful (e.g., days after onset). This study instead evaluates the postural sway features of post-stroke patients using the clustering method of machine learning. First, we collected the stroke patients' multi-variable motion-capture standing-posture data and processed them into t s long data slots. Then, we clustered the t-s data slots into K cluster groups using the dynamic-time-warping partition-around-medoid (DTW-PAM) method. The DTW measures the similarity between two temporal sequences that may vary in speed, whereas PAM identifies the centroids for the DTW clustering method. Finally, we used a post-hoc test and found that the sway amplitudes of markers in the shoulder, hip, knee, and center-of-mass are more important than their sway frequencies. We separately plotted the marker amplitudes and frequencies in the medial-lateral direction during a 5-s data slot and found that the post-stroke patients' postural sway frequency lay within the bandwidth of 0.5–1.5 Hz. Additionally, with an increase in the onset days, the cluster index of cerebral hemorrhage patients gradually transits in a four-cluster solution. However, the cerebral infarction patients did not exhibit such pronounced transitions over time. Moreover, we found that the postural-sway amplitude increased in clusters 1, 3, and 4. However, the amplitude of cluster 2 did not follow this pattern, owing to age effects related to the postural sway changes with age. A rehabilitation doctor can utilize these findings as guidelines to direct the post-stroke patient training.

Effects of Robot-Aided Rehabilitation on the Ankle Joint Properties and Balance Function in Stroke Survivors: A Randomized Controlled Trial

Background: Stroke survivors with impaired control of the ankle due to stiff plantarflexors often experience abnormal posture control, which affects balance and locomotion. Forceful stretching may decrease ankle stiffness and improve balance. Recently, a robot-aided stretching device was developed to decrease ankle stiffness of patient post-stroke, however, their benefits compared to manual stretching exercises have not been done in a randomized controlled trial, and the correlations between the ankle joint biomechanical properties and balance are unclear.

Objective: To compare the effects of robot-aided to manual ankle stretching training in stroke survivors with the spastic ankle on the ankle joint properties and balance function post-stroke, and further explore the correlations between the ankle stiffness and balance.

Methods: Twenty inpatients post-stroke with ankle spasticity received 20 minutes of stretching training daily over two weeks. The experimental group used a robot-aided stretching device, and the control group received manual stretching. Outcome measures were evaluated before and after training. The primary outcome measure was ankle stiffness. The secondary outcome measures were passive dorsiflexion ranges of motion, dorsiflexor muscle strength, Modified Ashworth Scale (MAS), Fugl-Meyer Motor Assessment of Lower Extremity (FMA-LE), Berg Balance Scale (BBS), Modified Barthel Index (MBI), and the Pro-Kin balance test.

Results: After training, two groups showed significantly within-group improvements in dorsiflexor muscle strength, FMA-LE, BBS, MBI (P < 0.05). The between-group comparison showed no significant differences in all outcome measures (P > 0.0025). The experimental group significantly improved in the stiffness and passive range of motion of dorsiflexion, MAS. In the Pro-Kin test, the experimental group improved significantly with eyes closed and open (P < 0.05), but significant improvements were found in the control group only with eyes open (P < 0.05). Dorsiflexion stiffness was positively correlated with the Pro-Kin test results with eyes open and the MAS (P < 0.05).

Conclusions: The robot-aided and manual ankle stretching training provided similar significant improvements in the ankle properties and balance post-stroke. However, only the robot-aided stretching training improved spasticity and stiffness of dorsiflexion significantly. Ankle dorsiflexion stiffness was correlated with balance function.

Clinical Trial Registration:www.chictr.org.cn ChiCTR2000030108.

Immersive Virtual Reality in Stroke Patients as a New Approach for Reducing Postural Disabilities and Falls Risk: A Case Series

Stroke is a neurologic disorder considered the first cause of disability worldwide due to motor, cognitive, and sensorial sequels. Balance dysfunctions in stroke survivors increase the risk of falls and physiotherapeutic rehabilitation is essential to reduce it. Virtual reality (VR) seems to be an alternative to conventional physiotherapy (CT), providing virtual environments and multisensorial inputs to train balance in stroke patients. The aim of this study was to assess if immersive VR treatment is more effective than CT to improve balance after stroke. This study got the approval from the Ethics Committee of the University of Almeria. Three chronic ischemic stroke patients were selected. One patient who received 25 sessions of immersive VR intervention for two months was compared with another patient who received equivalent CT and a third patient with no intervention. Balance, gait, risk of falling, and vestibular and visual implications in the equilibrium were assessed. After the interventions, the two patients receiving any of the treatments showed an improvement in balance compared to the untreated patient. In comparison to CT, our results suggest a higher effect of immersive VR in the improvement of balance and a reduction of falls risk due to the active upright work during the VR intervention.