Features Of Postural Sway Research Articles

Principal components analysis of postural sway in persons with unilateral lower limb amputation: A wearable sensor approach

Standing sway assessments can detect sensory imbalances which compromise postural control. Persons with lower limb amputation (LLA) often demonstrate impaired postural control, increasing fall risk. Here, principal features of postural sway were identified in persons with unilateral LLA using a single, commercially available wearable sensor. Sixty-one persons with LLA (n = 44 transtibial; n = 17 transfemoral) stood on a firm surface with eyes open/closed while wearing a single accelerometer mounted over the sacrum. Common parameters quantified spatiotemporal and spectral features of sway in anterior-posterior (AP) and mediolateral (ML) directions. Principal component (PC) dimensionality reduction was applied and loadings inspected to identify a reduced, non-redundant set among 14 original variables capturing 90 % variance. Six PCs described ≥ 90 % variance, with the first 3 explaining 75 %. With eyes open and closed, PC1 was loaded by variables characterizing trajectory planar size: area, jerk (i.e., sway smoothness), AP/ML RMS path distance, and AP/ML path range. With eyes open, PC2 was loaded by variables characterizing direction and spectral features: ellipse rotation, AP centroidal frequency, and ML jerk. With eyes closed, PC2 spectral loadings increased: ML centroidal frequency, ML frequency dispersion, and AP centroidal frequency. With eyes open, PC3 was loaded by ellipse rotation, jerk, ML velocity, ML centroidal frequency. With eyes closed, PC3 was loaded by ellipse rotation, ML centroidal frequency, ML frequency dispersion, and AP path velocity, characterizing off-axis error/corrections. RMS of path distance, ellipse rotation, centroidal frequency, frequency dispersion, path velocity, and jerk are a concise parameter set, derived from an accelerometer, to capture principal sway features in persons with LLA during standing balance with visual perturbations.

Rambling and trembling trajectories in the analysis of postural sway prior to the self-paced and reaction time tasks

Objective: Previous studies suggested that center of pressure (COP) shifts occur before an expected perturbation in the form of early and anticipatory postural adjustments which operate in a short time scale. However, the effect of such perturbations on pre-existing postural set on a longer time scale remained uncovered. The purpose of this study was to investigate whether rambling and trembling components of the COP trajectories depend on postural task or phase of trial before a self-initiated perturbation. Materials and Methods: Twenty-four young healthy participants took part in the study. Subjects performed three postural tasks, namely, (i) quiet stance task: 60 seconds quiet stance, (ii) self-paced task: maximal vertical jump from quiet stance under the self-paced time condition, and (iii) reaction-time task: maximal vertical jump from quiet stance under the reaction-time condition. Postural sway features were examined in two phases, the first and last 20 seconds of the trials. Results: The features of rambling and trembling components of the COP trajectories were affected by postural task or phase of trial. The ellipse area of the COP and rambling trajectories were significantly different among postural tasks. The median frequency was significantly different between the phases of trials for the COP and rambling trajectories. Conclusion: This study indicated task-specific changes in postural sway features. Rambling and trembling trajectories, which would reflect two underlying human postural control mechanisms as maintaining the body's equilibrium with respect to a moving reference point and oscillating around the moving reference point respectively, were affected differently before a whole-body maximum-effort self-initiated perturbation.

Chest-Based Wearables and Individualized Distributions for Assessing Postural Sway in Persons with Multiple Sclerosis.

Typical assessments of balance impairment are subjective or require data from cumbersome and expensive force platforms. Researchers have utilized lower back (sacrum) accelerometers to enable more accessible, objective measurement of postural sway for use in balance assessment. However, new sensor patches are broadly being deployed on the chest for cardiac monitoring, opening a need to determine if measurements from these devices can similarly inform balance assessment. Our aim in this work is to validate postural sway measurements from a chest accelerometer. To establish concurrent validity, we considered data from 16 persons with multiple sclerosis (PwMS) asked to stand on a force platform while also wearing sensor patches on the sacrum and chest. We found five of 15 postural sway features derived from the chest and sacrum were significantly correlated with force platform-derived features, which is in line with prior sacrum-derived findings. Clinical significance was established using a sample of 39 PwMS who performed eyes-open, eyes-closed, and tandem standing tasks. This cohort was stratified by fall status and completed several patient-reported measures (PRM) of balance and mobility impairment. We also compared sway features derived from a single 30-second period to those derived from a one-minute period with a sliding window to create individualized distributions of each postural sway feature (ID method). We find traditional computation of sway features from the chest is sensitive to changes in PRMs and task differences. Distribution characteristics from the ID method establish additional relationships with PRMs, detect differences in more tasks, and distinguish between fall status groups. Overall, the chest was found to be a valid location to monitor postural sway and we recommend utilizing the ID method over single-observation analyses.

Effect of individualized cognitive and postural task difficulty levels on postural control during dual task condition

BackgroundPrevious dual task studies suggested that the difficulty of the concurrent cognitive and motor tasks may not be challenging to the same degree for each person. This study approaches this problem by setting individualized difficulty levels for tasks to examine the dual task interference. Research questionDo the features of postural sway depend on increased individualized difficulty levels of concurrent cognitive and postural activities? Methods20 young healthy participants (10 male, 10 female) took part in the study. Before the experiments, cognitive task difficulty (No-, Medium-, High) has been set individually. Subjects performed postural tasks (quiet stance, voluntary sway) concurrently with or without a cognitive task which based on simple arithmetic calculations. Postural sway features were examined. ResultsPostural sway features were affected by individualized difficulty level of concurrent cognitive and postural activities. In voluntary sway, as a more challenging postural task, higher reductions were observed for such sway features as COP velocity and range in AP direction. SignificanceThis study signaled task-specific changes in postural sway features. When the difficulty levels were set individually, the effect of motor and cognitive dual task was more apparent when the balance requirement of the primary motor task increased.

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.

Early Detection of Parkinson's Disease Using Center of Pressure Data and Machine Learning.

Parkinson's disease (PD) is a progressive neurodegenerative disorder resulting in abnormal body movements. Postural instability is one of the primary motor symptoms of PD and contributes to falls. Measurement of postural sway through center of pressure (COP) data might be an objective indicator of Parkinson's disease. The goal of this work is to use machine learning to evaluate if different features of postural sway can differentiate PD patients from healthy controls. Time domain, frequency domain, time-frequency, and structural features were extracted from COP data collected from 19 PD patients and 13 healthy controls (HC). The calculated parameters were input to various machine-learning models to classify PD and HC. Random Forest outperformed the rest of the classifiers in terms of accuracy, false negative rate, F1-score, and precision. Time domain features had the best performance in differentiating PD from HC compared to other feature groups.

Which features of postural sway are effective in distinguishing Parkinson's disease from controls? A systematic review

BackgroundPostural sway may be useful as an objective measure of Parkinson's disease (PD). Existing studies have analyzed many different features of sway using different experimental paradigms. We aimed to determine what features have been used to measure sway and then to assess which feature(s) best differentiate PD patients from controls. We also aimed to determine whether any refinements might improve discriminative power and so assist in standardizing experimental conditions and analysis of data.MethodsIn this systematic review of the literature, effect size (ES) was calculated for every feature reported by each article and then collapsed across articles where appropriate. The influence of clinical medication status, visual state, and sampling rate on ES was also assessed.ResultsFour hundred and forty‐three papers were retrieved. 25 contained enough information for further analysis. The most commonly used features were not the most effective (e.g., PathLength, used 14 times, had ES of 0.47, while TotalEnergy, used only once, had ES of 1.78). Increased sampling rate was associated with increased ES (PathLength ES increased to 1.12 at 100 Hz from 0.40 at 10 Hz). Measurement during “OFF” clinical status was associated with increased ES (PathLength ES was 0.83 OFF compared to 0.21 ON).ConclusionsThis review identified promising features for analysis of postural sway in PD, recommending a sampling rate of 100 Hz and studying patients when OFF to maximize ES. ES complements statistical significance as it is clinically relevant and is easily compared across experiments. We suggest that machine learning is a promising tool for the future analysis of postural sway in PD.

Automated Classification of Postural Control for Individuals With Parkinson's Disease Using a Machine Learning Approach: A Preliminary Study.

The purposes of the study were (1)to compare postural sway between participants with Parkinson's disease (PD) and healthy controls and (2)to develop and validate an automated classification of PD postural control patterns using a machine learning approach. A total of 9 participants in the early stage of PD and 12 healthy controls were recruited. Participants were instructed to stand on a force plate and maintain stillness for 2 minutes with eyes open and eyes closed. The center of pressure data were collected at 50 Hz. Linear displacements, standard deviations, total distances, sway areas, and multiscale entropy of center of pressure were calculated and compared using mixed-model analysis of variance. Five supervised machine learning algorithms (ie,logistic regression, K-nearest neighbors, Naïve Bayes, decision trees, and random forest) were used to classify PD postural control patterns. Participants with PD exhibited greater center of pressure sway and variability compared with controls. The K-nearest neighbor method exhibited the best prediction performance with an accuracy rate of up to 0.86. In conclusion, participants with PD exhibited impaired postural stability and their postural sway features could be identified by machine learning algorithms.

Examining the specificity of postural control deficits in children with Autism Spectrum Disorder using a cross-syndrome approach

BackgroundPostural control deficits are commonly reported in children with Autism Spectrum Disorder (ASD). However, identification of specific postural sway features that differentiate ASD from other neurodevelopmental disorders has not been examined. The current study employs a cross-syndrome approach by comparing postural sway area and direction-specific features of sway magnitude, sway velocity, and sway complexity between children with ASD, Fetal Alcohol Spectrum Disorder (FASD), Attention-Deficit Hyperactivity Disorder (ADHD), and typically developing (TD) controls. MethodEighty children (7–17 years old) participated in this study. Postural sway was measured on a force plate during 30-s of bilateral quiet stance and balance was assessed using a timed unipedal stance test. ResultsResults showed that (1) postural sway area and mediolateral (ML) sway magnitude were significantly greater in children with ASD vs. all groups (p < 0.05); (2) anteroposterior (AP) sway magnitude and velocity were significantly greater in children with ASD vs. TD controls-only (p < 0.05); and (3) static balance was similarly impaired in all clinical groups compared to TD children (p < 0.05). ConclusionsASD-specific postural sway features were identified using a cross-syndrome design. Identifying ASD-specific motor impairments can be useful to understanding the neurological underpinnings of ASD.

Are static and functional balance abilities related in individuals with Multiple Sclerosis?

BackgroundIn people with Multiple Sclerosis (pwMS), balance assessment is essential in estimating the risk of falls, monitoring disease progression and verifying the effectiveness of rehabilitative treatment. Clinical tools and instrumental techniques are available for testing static and dynamic balance, but the relationship between such abilities is still not clear. Having information about this link would be important in properly planning the type and number of tests to administer. MethodsOne hundred and six pwMS (Expanded Disability Status Scale, EDSS 0−6.5) stratified in three sub-groups (Class 1 EDSS 0–1.5, Class 2 EDSS 2–4 and Class 3 EDSS 4.5–6.5) and 42 healthy controls (HC) participated in the study. All underwent static posturography and instrumented Timed-Up-and-Go (TUG) performed using a wearable inertial sensor. Raw data were processed to extract postural sway features, overall duration of TUG and its main sub-phases (i.e. sit-to-stand, 180° turns and stand-to-sit). ResultsAll sway parameters of pwMS of Classes 2 and 3, as well as total TUG duration and time necessary to perform 180° turns, were found significantly higher than HC and Class 1 participants. However, poor correlations were found between sway and TUG parameters. When pwMS are grouped, small/moderate correlations (in the range 0.20–0.41) were found between all sway parameters and total TUG duration. ConclusionsStatic and dynamic balance in pwMS appear scarcely correlated, although both worsen as disability increases. This implies that they should be separately assessed using specific tests to have a complete view of postural control performance in MS.

Directional measures of postural sway as predictors of balance instability and accidental falls.

Despite the obvious advantages and popularity of static posturography, universal standards for posturographic tests have not been developed thus far. Most of the center-of-foot pressure (COP) indices are strongly dependent on an individual experimental design, and are susceptible to distortions, which makes results of their analysis incomparable. In this research, we present a novel approach to the analysis of the COP trajectory based on the directional features of postural sway. Our novel output measures: the sway directional indices (DI) and sway vector (SV) were applied to assess the postural stability in the group of young able-bodied subjects. Towards this aim, the COP trajectories were recorded in 100 students standing still for 60 s, with eyes open (EO) and then, with eyes closed (EC). Each record was subdivided then into 20, 30 and 60 s samples. Interclass correlation coefficients were calculated from the samples. The controlled variables (visual conditions) uniquely affected the output measures, but only in case of proper signal pretreatment (low-pass filtering). In filtering below 6 Hz, the DI and SV provided a unique set of descriptors for postural control. Both sway measures were highly independent of the trial length and the sampling frequency, and were unaffected by the sampling noise. Directional indices of COP filtered at 6 Hz showed high to very high reliability, with ICC range of 0.7-0.9. Results of a single 60 s trial are sufficient to reach acceptable reliability for both DI and SV. In conclusion, the directional sway measures may be recommended as the primary standard in static posturography.

Recurrence Quantitative Analysis of Postural Sway using Force Plate and Smartphone

Although modern medicine and new medical technologies offer enormous potential to improve diagnosis and treatment of many diseases, mortalities from fall accidents are steadily on the rise for the elderly. Since postural stability characteristics are considered to be important in maintaining functional independence free of falls and healthy life style especially for the growing elderly population, there is an imminent need in inexpensive and portable device that can assess balance. While inertial sensors embedded in smartphone are seen as an alternative to force plate (ground truth) to unobtrusively assess postural stability in home environments, no study has yet reported the non-linear physiological information captured by smartphone affixed at pelvic region. By using recurrence quantitative analysis (RQA), this study investigates non-linear dynamical features of postural sway measured from force plate and smartphone. The resultant position vector of postural sway from the two systems was highly coherent and was used for non-linear analysis. Even though most of RQA measures collected from the projected postural sway using the smartphone were significantly different than measures collected using the force plate, deterministic characteristics of postural sway were not found significantly different. This study opens new prospects of easy clinical testing using postural variables that may be relevant for assessing fall risks at home and patient environment in future.

Navigating sensory conflict in dynamic environments using adaptive state estimation

Most conventional robots rely on controlling the location of the center of pressure to maintain balance, relying mainly on foot pressure sensors for information. By contrast,humans rely on sensory data from multiple sources, including proprioceptive, visual, and vestibular sources. Several models have been developed to explain how humans reconcile information from disparate sources to form a stable sense of balance. These models may be useful for developing robots that are able to maintain dynamic balance more readily using multiple sensory sources. Since these information sources may conflict, reliance by the nervous system on any one channel can lead to ambiguity in the system state. In humans, experiments that create conflicts between different sensory channels by moving the visual field or the support surface indicate that sensory information is adaptively reweighted. Unreliable information is rapidly down-weighted,then gradually up-weighted when it becomes valid again.Human balance can also be studied by building robots that model features of human bodies and testing them under similar experimental conditions. We implement a sensory reweighting model based on an adaptive Kalman filter in abipedal robot, and subject it to sensory tests similar to those used on human subjects. Unlike other implementations of sensory reweighting in robots, our implementation includes vision, by using optic flow to calculate forward rotation using a camera (visual modality), as well as a three-axis gyro to represent the vestibular system (non-visual modality), and foot pressure sensors (proprioceptive modality). Our model estimates measurement noise in real time, which is then used to recompute the Kalman gain on each iteration, improving the ability of the robot to dynamically balance. We observe that we can duplicate many important features of postural sw ay in humans, including automatic sensory reweighting,effects, constant phase with respect to amplitude, and a temporal asymmetry in the reweighting gains.

Mechanisms of Postural Control in Alcoholic Men and Women: Biomechanical Analysis of Musculoskeletal Coordination During Quiet Standing

Excessive sway during quiet standing is a common sequela of chronic alcoholism even with prolonged sobriety. Whether alcoholic men and women who have remained abstinent from alcohol for weeks to months differ from each other in the degree of residual postural instability and biomechanical control mechanisms has not been directly tested. We used a force platform to characterize center-of-pressure biomechanical features of postural sway, with and without stabilizing conditions from touch, vision, and stance, in 34 alcoholic men, 15 alcoholic women, 22 control men, and 29 control women. Groups were matched in age (49.4 years), general intelligence, socioeconomic status, and handedness. Each alcoholic group was sober for an average of 75 days. Analysis of postural sway when using all 3 stabilizing conditions versus none revealed diagnosis and sex differences in ability to balance. Alcoholics had significantly longer sway paths, especially in the anterior-posterior direction, than controls when maintaining erect posture without balance aids. With stabilizing conditions the sway paths of all groups shortened significantly, especially those of alcoholic men, who demonstrated a 3.1-fold improvement in sway path difference between the easiest and most challenging conditions; the remaining 3 groups, each showed a approximately 2.4-fold improvement. Application of a mechanical model to partition sway paths into open-loop and closed-loop postural control systems revealed that the sway paths of the alcoholic men but not alcoholic women were characterized by greater short-term (open-loop) diffusion coefficients without aids, often associated with muscle stiffening response. With stabilizing factors, all 4 groups showed similar long-term (closed loop) postural control. Correlations between cognitive abilities and closed-loop sway indices were more robust in alcoholic men than alcoholic women. Reduction in sway and closed-loop activity during quiet standing with stabilizing factors shows some differential expression in men and women with histories of alcohol dependence. Nonetheless, enduring deficits in postural instability of both alcoholic men and alcoholic women suggest persisting liability for falling.

A Quiet Standing Index for Testing the Postural Sway of Healthy and Diabetic Adults Across a Range of Ages

A quiet standing index is developed for tracking the postural sway of healthy and diabetic adults over a range of ages. Several postural sway features are combined into a single composite feature C that increases with age a. Sway features are ranked based on the r(2)-values of their linear regression models, and the composite feature is a weighted sum of selected sway features with optimal weighting coefficients determined using principal component analysis. A performance index based on both reliability and sensitivity is used to determine the optimal number of features. The features used to form C include power and distance metrics. The quiet standing index is a scalar that compares the composite feature C to a linear regression model f(a) using C(')(a) = C/f(a). For a motionless subject, C(') = 0, and when the composite feature exactly matches the healthy control (HC) model, C(') = 1. Values of C(') >> 1 represent excessive postural sway and may indicate impaired postural control. Diabetic neurologically intact subjects, nondiabetic peripheral neuropathy subjects (PN), and diabetic PN subjects (DPN) were evaluated. The quiet standing indexes of the PN and DPN groups showed statistically significant increases over the HC group. Changes in the quiet standing index over time may be useful in identifying people with impaired balance who may be at an increased risk of falling.