On the choice of multiscale entropy algorithm for quantification of complexity in gait data

The present study aimed at identifying a suitable multiscale entropy (MSE) algorithm for assessment of complexity in a stride-to-stride time interval time series. Five different algorithms were included (the original MSE, refine composite multiscale entropy (RCMSE), multiscale fuzzy entropy, generalized multiscale entropy and intrinsic mode entropy) and applied to twenty iterations of white noise, pink noise, or a sine wave with added white noise. Based on their ability to differentiate the level of complexity in the three different generated signal types, and their sensitivity and parameter consistency, MSE and RCMSE were deemed most appropriate. These two algorithms were applied to stride-to-stride time interval time series recorded from fourteen healthy subjects during one hour of overground and treadmill walking. In general, acceptable sensitivity and good parameter consistency were observed for both algorithms; however, they were not able to differentiate the complexity of the stride-to-stride time interval time series between the two walking conditions. Thus, the present study recommends the use of either MSE or RCMSE for quantification of complexity in stride-to-stride time interval time series

Lower extremity injury in female basketball players is related to a large difference in peak eversion torque between barefoot and shod conditions

AbstractBackgroundThe majority of injuries reported in female basketball players are ankle sprains and mechanisms leading to injury have been debated. Investigations into muscular imbalances in barefoot versus shod conditions and their relationship with injury severity have not been performed. The purpose of this study was to investigate the effects of wearing athletic shoes on muscular strength and its relationship to lower extremity injuries, specifically female basketball players due to the high incidence of ankle injuries in this population.MethodsDuring pre-season, 11 female collegiate basketball players underwent inversion and eversion muscle strength testing using an isokinetic dynamometer in both a barefoot and shod conditions. The difference between conditions was calculated for inversion and eversion peak torque, time to peak torque as well as eversion-to-inversion peak torque percent strength ratio for both conditions. Lower extremity injuries were documented and ranked in severity. The ranked difference between barefoot and shod conditions for peak torque and time to peak torque as well as percent strength ratio was correlated with injury ranking using a Spearman rho correlation (ρ) with an α level of 0.05.ResultsThe ranked differences in barefoot and shod for peak eversion and inversion torque at 120°/s were correlated with their injury ranking. Ranking of the athletes based on the severity of injuries that were sustained during the season was found to have a strong, positive relationship with the difference in peak eversion torque between barefoot and shod (ρ = 0.78; p = 0.02).ConclusionIt is possible that a large discrepancy between strength in barefoot and shod conditions can predispose an athlete to injury. Narrowing the difference in peak eversion torque between barefoot and shod could decrease propensity to injury. Future work should investigate the effect of restoration of muscular strength during barefoot and shod exercise on injury rates

On the Calculation of Sample Entropy Using Continuous and Discrete Human Gait Data

Sample entropy (SE) has relative consistency using biologically-derived, discrete data \u3e500 data points. For certain populations, collecting this quantity is not feasible and continuous data has been used. The effect of using continuous versus discrete data on SE is unknown, nor are the relative effects of sampling rate and input parameters m (comparison vector length) and r(tolerance). Eleven subjects walked for 10-minutes and continuous joint angles (480 Hz) were calculated for each lower-extremity joint. Data were downsampled (240, 120, 60 Hz) and discrete range-of-motion was calculated. SE was quantified for angles and range-of-motion at all sampling rates and multiple combinations of parameters. A differential relationship between joints was observed between range-of-motion and joint angles. Range-of-motion SE showed no difference; whereas, joint angle SE significantly decreased from ankle to knee to hip. To confirm findings from biological data, continuous signals with manipulations to frequency, amplitude, and both were generated and underwent similar analysis to the biological data. In general, changes to m, r, and sampling rate had a greater effect on continuous compared to discrete data. Discrete data was robust to sampling rate and m. It is recommended that different data types not be compared and discrete data be used for SE

Sampling frequency influences sample entropy of kinematics during walking

Sample entropy (SaEn) has been used to assess the regularity of lower limb joint angles during walking. However, changing sampling frequency and the number of included strides can potentially affect the sample entropy. The present study investigated the effect of sample frequency and the number of included strides on the calculations of SaEn in joint angle signals recorded during treadmill walking. Eleven subjects walked at their preferred walking speed for 10 minutes, and SaEn was calculated on sagittal plane hip, knee and ankle angle signals extracted from 50, 100, 200, 300 and 400 strides at sampling frequencies of 60, 120, 240 and 480Hz. Increase in sampling frequency decreased the SaEn significantly for the three joints. The number of included strides had no effect on the SaEn calculated on the hip joint angle and only limited effect on the SaEn calculated on the knee and ankle joint signals. The present study suggests that the number of data points within each stride to a greater extent determines the size of the SaEn compared to the number of strides and emphasizes the use of a fixed number of data points within each stride when applying SaEn to lower limb joint angles during walking

Measuring Coupling of Rhythmical Time Series Using Cross Sample Entropy and Cross Recurrence Quantification Analysis

The aim of this investigation was to compare and contrast the use of cross sample entropy (xSE) and cross recurrence quantification analysis (cRQA) measures for the assessment of coupling of rhythmical patterns. Measures were assessed using simulated signals with regular, chaotic, and random fluctuations in frequency, amplitude, and a combination of both. Biological data were studied as models of normal and abnormal locomotor-respiratory coupling. Nine signal types were generated for seven frequency ratios. Fifteen patients with COPD (abnormal coupling) and twenty-one healthy controls (normal coupling) walked on a treadmill at three speeds while breathing and walking were recorded. xSE and the cRQA measures of percent determinism,maximum line,mean line, and entropy were quantified for both the simulated and experimental data. In the simulated data, xSE, percent determinism, and entropy were influenced by the frequency manipulation. The 1 : 1 frequency ratio was different than other frequency ratios for almost all measures and/or manipulations. The patients with COPD used a 2 : 3 ratio more often and xSE, percent determinism,maximum line, mean line, and cRQA entropy were able to discriminate between the groups. Analysis of the effects of walking speed indicated that all measures were able to discriminate between speeds

Updated Perspectives on the Role of Biomechanics in COPD: Considerations for the Clinician

Patients with chronic obstructive pulmonary disease (COPD) demonstrate extra-pulmonary functional decline such as an increased prevalence of falls. Biomechanics offers insight into functional decline by examining mechanics of abnormal movement patterns. This review discusses biomechanics of functional outcomes, muscle mechanics, and breathing mechanics in patients with COPD as well as future directions and clinical perspectives. Patients with COPD demonstrate changes in their postural sway during quiet standing compared to controls, and these deficits are exacerbated when sensory information (eg, eyes closed) is manipulated. If standing balance is disrupted with a perturbation, patients with COPD are slower to return to baseline and their muscle activity is differential from controls. When walking, patients with COPD appear to adopt a gait pattern that may increase stability (eg, shorter and wider steps, decreased gait speed) in addition to altered gait variability. Biomechanical muscle mechanics (ie, tension, extensibility, elasticity, and irritability) alterations with COPD are not well documented, with relatively few articles investigating these properties. On the other hand, dyssynchronous motion of the abdomen and rib cage while breathing is well documented in patients with COPD. Newer biomechanical technologies have allowed for estimation of regional, compartmental, lung volumes during activity such as exercise, as well as respiratory muscle activation during breathing. Future directions of biomechanical analyses in COPD are trending toward wearable sensors, big data, and cloud computing. Each of these offers unique opportunities as well as challenges. Advanced analytics of sensor data can offer insight into the health of a system by quantifying complexity or fluctuations in patterns of movement, as healthy systems demonstrate flexibility and are thus adaptable to changing conditions. Biomechanics may offer clinical utility in prediction of 30-day readmissions, identifying disease severity, and patient monitoring. Biomechanics is complementary to other assessments, capturing what patients do, as well as their capability

Patients with Chronic Obstructive Pulmonary Disease Walk with Altered Step Time and Step Width Variability as Compared with Healthy Control Subjects

Rationale: Compared with control subjects, patients with chronic obstructive pulmonary disease (COPD) have an increased incidence of falls and demonstrate balance deficits and alterations in mediolateral trunk acceleration while walking. Measures of gait variability have been implicated as indicators of fall risk, fear of falling, and future falls. Objectives: To investigate whether alterations in gait variability are found in patients with COPD as compared with healthy control subjects. Methods: Twenty patients with COPD (16 males; mean age, 63.6 ± 9.7 yr; FEV1/FVC, 0.52 ± 0.12) and 20 control subjects (9 males; mean age, 62.5 ± 8.2 yr) walked for 3 minutes on a treadmill while their gait was recorded. The amount (SD and coefficient of variation) and structure of variability (sample entropy, a measure of regularity) were quantified for step length, time, and width at three walking speeds (self-selected and ±20% of self-selected speed). Generalized linear mixed models were used to compare dependent variables. Results: Patients with COPD demonstrated increased mean and SD step time across all speed conditions as compared with control subjects. They also walked with a narrower step width that increased with increasing speed, whereas the healthy control subjects walked with a wider step width that decreased as speed increased. Further, patients with COPD demonstrated less variability in step width, with decreased SD, compared with control subjects at all three speed conditions. No differences in regularity of gait patterns were found between groups. Conclusions: Patients with COPD walk with increased duration of time between steps, and this timing is more variable than that of control subjects. They also walk with a narrower step width in which the variability of the step widths from step to step is decreased. Changes in these parameters have been related to increased risk of falling in aging research. This provides a mechanism that could explain the increased prevalence of falls in patients with COPD

Walking speed and spatiotemporal step mean measures are reliable during feedback-controlled treadmill walking; however, spatiotemporal step variability is not reliable

The purpose of the study was to compare the effects of a feedback-controlled treadmill (FeedbackTM) to a traditional fixed-speed treadmill (FixedTM) on spatiotemporal gait means, variability, and dynamics. The study also examined inter-session reliability when using the FeedbackTM. Ten young adults walked on the FeedbackTM for a 5-minute familiarization followed by a 16-minute experimental trial. They returned within one week and completed a 5-minute familiarization followed by a 16-minute experimental trial each for FeedbackTM and FixedTM conditions. Mean walking speed and step time, length, width, and speed means and coefficient of variation were calculated from all experimental conditions. Step time, length, width, and speed gait dynamics were analyzed using detrended fluctuation analysis. Mean differences between experimental trials were determined using ANOVAs and reliability between FeedbackTM sessions was determined by intraclass correlation coefficient. No difference was found in mean walking speed nor spatiotemporal variables, with the exception of step width, between the experimental trials. All mean spatiotemporal variables demonstrated good to excellent reliability between sessions, while coefficient of variation was not reliable. Gait dynamics of step time, length, width, and speed were significantly more persistent during the FeedbackTM condition compared to FixedTM, especially step speed. However, gait dynamics demonstrated fair to poor reliability between FeedbackTM sessions. When walking on the FeedbackTM, users maintain a consistent set point, yet the gait dynamics around the mean are different when compared to walking on a FixedTM. In addition, spatiotemporal gait dynamics and variability may not be consistent across separate days when using the FeedbackTM

Optic flow improves step width and length in older adults while performing dual task

Background Dual-task paradigms are used to investigate gait and cognitive declines in older adults (OA). Optic-flow is a virtual reality environment where the scene flows past the subject while walking on a treadmill, mimicking real-life locomotion. Aims To investigate cost of environment (no optic-flow v. optic-flow) while completing single- and dual-task walking and dual-task costs (DTC; single- v. dual-task) in optic-flow and no optic-flow environments. Methods Twenty OA and seven younger adults (YA) walked on a self-paced treadmill in 3-min segments per task and both environments. Five task conditions included: no task, semantic fluency (category), phonemic fluency (letters), word reading, and serial-subtraction. Results OAs had a benefit of optic-flow compared to no optic-flow for step width (p = 0.015) and step length (p = 0.045) during letters compared to the YA. During letters, OA experienced improvement in step width DTC; whereas YA had a decrement in step width DTC from no optic-flow to optic-flow (p = 0.038). During serial-subtraction, OA had less step width DTC when compared to YA in both environments (p = 0.02). Discussion During letters, step width and step length improved in OA while walking in optic-flow. Also, step width DTC differed between the two groups. Sensory information from optic-flow appears to benefit OA. Letters relies more on verbal ability and word knowledge, which are preserved in aging. However, YA use a complex speech style during dual tasking, searching for complex words and an increased speed of speech. Conclusions OA can benefit from optic-flow by improving spatial gait parameters, specifically, step width, during dual-task walking