318 research outputs found
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
A Nonlinear Analysis Software Toolkit for Biomechanical Data
In this paper, we present a nonlinear analysis software toolkit, which can
help in biomechanical gait data analysis by implementing various nonlinear
statistical analysis algorithms. The toolkit is proposed to tackle the need for
an easy-to-use and friendly analyzer for gait data where algorithms seem
complex to implement in software and execute. With the availability of our
toolkit, people without programming knowledge can run the analysis to receive
human gait data analysis results. Our toolkit includes the implementation of
several nonlinear analysis algorithms, while it is also possible for users with
programming experience to expand its scope by implementing and adding more
algorithms to the toolkit. Currently, the toolkit supports MatLab bindings
while being developed in Python. The toolkit can seamlessly run as a background
process to analyze hundreds of different gait data and produce analysis
outcomes and figures that illustrate these results
Multiscale Entropy Algorithms to Analyze Complexity and Variability of Trunk Accelerations Time Series in Subjects with Parkinson’s Disease
The aim of this study was to assess the ability of multiscale sample entropy (MSE), refined composite multiscale entropy (RCMSE), and complexity index (CI) to characterize gait complexity through trunk acceleration patterns in subjects with Parkinson's disease (swPD) and healthy subjects, regardless of age or gait speed. The trunk acceleration patterns of 51 swPD and 50 healthy subjects (HS) were acquired using a lumbar-mounted magneto-inertial measurement unit during their walking. MSE, RCMSE, and CI were calculated on 2000 data points, using scale factors (t) 1-6. Differences between swPD and HS were calculated at each t, and the area under the receiver operating characteristics, optimal cutoff points, post-test probabilities, and diagnostic odds ratios were calculated. MSE, RCMSE, and CIs showed to differentiate swPD from HS. MSE in the anteroposterior direction at t4 and t5, and MSE in the ML direction at t4 showed to characterize the gait disorders of swPD with the best trade-off between positive and negative posttest probabilities and correlated with the motor disability, pelvic kinematics, and stance phase. Using a time series of 2000 data points, a scale factor of 4 or 5 in the MSE procedure can yield the best trade-off in terms of post-test probabilities when compared to other scale factors for detecting gait variability and complexity in swPD
Estimating fall risk with inertial sensors using gait stability measures that do not require step detection
Falls have major consequences both at societal (health-care and economy) and individual (physical and psychological) levels. Questionnaires to assess fall risk are commonly used in the clinic, but their predictive value is limited. Objective methods, suitable for clinical application, are hence needed to obtain a quantitative assessment of individual fall risk. Falls in older adults often occur during walking and trunk position is known to play a critical role in balance control. Therefore, analysis of trunk kinematics during gait could present a viable approach to the development of such methods. In this study, nonlinear measures such as harmonic ratio (HR), index of harmonicity (IH), multiscale entropy (MSE) and recurrence quantification analysis (RQA) of trunk accelerations were calculated. These measures are not dependent on step detection, a potentially critical source of error. The aim of the present study was to investigate the association between the aforementioned measures and fall history in a large sample of subjects (42 fallers and 89 non-fallers) aged 50 or older. Univariate associations with fall history were found for MSE and RQA parameters in the AP direction; the best classification results were obtained for MSE with scale factor τ = 2 and for maximum length of diagonals in RQA (72.5% and 71% correct classifications, respectively). MSE and RQA were found to be positively associated with fall history and could hence represent useful tools in the identification of subjects for fall prevention programs. © 2013 Elsevier B.V
Gait analysis under the lens of statistical physics
Human gait; Irreversibility; Multi-fractal analysisMarcha humana; Irreversibilidad; Análisis multifractalMarxa humana; Irreversibilitat; Anà lisi multifractalHuman gait is a fundamental activity, essential for the survival of the individual, and an emergent property of the interactions between complex physical and cognitive processes. Gait is altered in many situations, due both to external constraints, as e.g. paced walk, and to physical and neurological pathologies. Its study is therefore important as a way of improving the quality of life of patients, but also as a door to understanding the inner working of the human nervous system. In this review we explore how four statistical physics concepts have been used to characterise normal and pathological gait: entropy, maximum Lyapunov exponent, multi-fractal analysis and irreversibility. Beyond some basic definitions, we present the main results that have been obtained in this field, as well as a discussion of the main limitations researchers have dealt and will have to deal with. We finally conclude with some biomedical considerations and avenues for further development.This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 851255). M.Z. and F.O. acknowledges the Spanish State Research Agency through Grant MDM-2017–0711 funded by MCIN/AEI/10.13039/501100011033. Authors acknowledge support from the Escuela Universitaria de Fisioterapia de la ONCE
Comparing postural stability entropy analyses to differentiate fallers and non-fallers
The health and financial cost of falls has spurred research to differentiate the characteristics of fallers and non-fallers. Postural stability has received much of the attention with recent studies exploring various measures of entropy. This study compared the discriminatory ability of several entropy methods at differentiating two paradigms in the center-of-pressure of elderly individuals: (1) eyes open (EO) vs. eyes closed (EC) and (2) fallers (F) vs. non-fallers (NF). Methods were compared using the area under the curve (AUC) of the receiver-operating characteristic curves developed from logistic regression models. Overall, multiscale entropy (MSE) and composite multiscale entropy (CompMSE) performed the best with AUCs of 0.71 for EO/EC and 0.77 for F/NF. When methods were combined together to maximize the AUC, the entropy classifier had an AUC of for 0.91 the F/NF comparison. These results suggest researchers and clinicians attempting to create clinical tests to identify fallers should consider a combination of every entropy method when creating a classifying test. Additionally, MSE and CompMSE classifiers using polar coordinate data outperformed rectangular coordinate data, encouraging more research into the most appropriate time series for postural stability entropy analysis
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QUANTIFYING GAIT ADAPTABILITY: FRACTALITY, COMPLEXITY, AND STABILITY DURING ASYMMETRIC WALKING
Successful walking necessitates modifying locomotor patterns when encountering organism, task, or environmental constraints. The structure of stride-to-stride variance (fractal dynamics) may represent the adaptive capacity of the locomotor system. To date, however, fractal dynamics have been assessed during unperturbed walking. Quantifying gait adaptability requires tasks that compel locomotor patterns to adapt. The purpose of this dissertation was to determine the potential relationship between fractal dynamics and gait adaptability. The studies presented herein represent a necessary endeavor to incorporate both an analysis of gait fractal dynamics and a task requiring adaptation of locomotor patterns. The adaptation task involved walking asymmetrically on a split-belt treadmill, whereby individuals adapted the relative phasing between legs. This experimental design provided a better understanding of the prospective relationship between fractal dynamics and adaptive capacity. Results from the first study indicated there was no association between unperturbed walking fractal dynamics and gait adaptability in young, healthy adults. However, there was an emergent relationship between asymmetric walking fractal dynamics and gait adaptability. Moreover, fractal dynamics increased during asymmetric walking. The second study investigated fractal dynamics and gait adaptability in healthy, active young and older adults. The findings from study 2 showed no differences between young and older adults regarding unperturbed or asymmetric walking fractal dynamics, or gait adaptability performance. The second study provided further evidence for the lack of association between unperturbed fractal dynamics and gait adaptability. Furthermore, study 2 delivered additional support that asymmetric walking not only yields increased fractal scaling values, but also associates with adaptive gait performance in older adults. Finally, while the first two studies explored stride time monofractality during various walking tasks, the third study aimed to understand the potential multifractality, i.e. temporal evolution of fractal dynamics, of unperturbed and asymmetric walking. The results suggest that unperturbed walking is monofractal in nature, while more challenging asymmetric walking reveals multifractal characteristics, and that multifractality does not associate with adaptive gait performance. This dissertation provides preliminary evidence for the lack of relationship between gait adaptability and unperturbed fractal dynamics, and the emergent association between adaptive gait and asymmetric walking fractality
Human Gait Analysis using Spatiotemporal Data Obtained from Gait Videos
Mit der Entwicklung von Deep-Learning-Techniken sind Deep-acNN-basierte Methoden
zum Standard fĂĽr Bildverarbeitungsaufgaben geworden, wie z. B. die Verfolgung menschlicher
Bewegungen und Posenschätzung, die Erkennung menschlicher Aktivitäten und
die Erkennung von Gesichtern. Deep-Learning-Techniken haben den Entwurf, die Implementierung
und den Einsatz komplexer und vielfältiger Anwendungen verbessert, die nun
in einer Vielzahl von Bereichen, einschlieĂźlich der Biomedizintechnik, eingesetzt werden.
Die Anwendung von Computer-Vision-Techniken auf die medizinische Bild- und Videoanalyse
hat zu bemerkenswerten Ergebnissen bei der Erkennung von Ereignissen gefĂĽhrt. Die
eingebaute Fähigkeit von convolutional neural network (CNN), Merkmale aus komplexen
medizinischen Bildern zu extrahieren, hat in Verbindung mit der Fähigkeit von long short
term memory network (LSTM), die zeitlichen Informationen zwischen Ereignissen zu erhalten,
viele neue Horizonte fĂĽr die medizinische Forschung geschaffen. Der Gang ist einer der
kritischen physiologischen Bereiche, der viele Störungen im Zusammenhang mit Alterung
und Neurodegeneration widerspiegeln kann. Eine umfassende und genaue Ganganalyse
kann Einblicke in die physiologischen Bedingungen des Menschen geben. Bestehende
Ganganalyseverfahren erfordern eine spezielle Umgebung, komplexe medizinische Geräte
und geschultes Personal fĂĽr die Erfassung der Gangdaten. Im Falle von tragbaren Systemen
kann ein solches System die kognitiven Fähigkeiten beeinträchtigen und für die Patienten
unangenehm sein.
Außerdem wurde berichtet, dass die Patienten in der Regel versuchen, während des
Labortests bessere Leistungen zu erbringen, was möglicherweise nicht ihrem tatsächlichen
Gang entspricht. Trotz technologischer Fortschritte stoĂźen wir bei der Messung des menschlichen
Gehens in klinischen und Laborumgebungen nach wie vor an Grenzen. Der Einsatz
aktueller Ganganalyseverfahren ist nach wie vor teuer und zeitaufwändig und erschwert den
Zugang zu Spezialgeräten und Fachwissen.
Daher ist es zwingend erforderlich, ĂĽber Methoden zu verfĂĽgen, die langfristige Daten
ĂĽber den Gesundheitszustand des Patienten liefern, ohne doppelte kognitive Aufgaben oder
Unannehmlichkeiten bei der Verwendung tragbarer Sensoren. In dieser Arbeit wird daher eine einfache, leicht zu implementierende und kostengĂĽnstige Methode zur Erfassung von
Gangdaten vorgeschlagen. Diese Methode basiert auf der Aufnahme von Gehvideos mit
einer Smartphone-Kamera in einer häuslichen Umgebung unter freien Bedingungen. Deep
neural network (NN) verarbeitet dann diese Videos, um die Gangereignisse zu extrahieren.
Die erkannten Ereignisse werden dann weiter verwendet, um verschiedene räumlich-zeitliche
Parameter des Gangs zu quantifizieren, die fĂĽr jedes Ganganalysesystem wichtig sind.
In dieser Arbeit wurden Gangvideos verwendet, die mit einer Smartphone-Kamera mit
geringer Auflösung außerhalb der Laborumgebung aufgenommen wurden. Viele Deep-
Learning-basierte NNs wurden implementiert, um die grundlegenden Gangereignisse wie
die FuĂźposition in Bezug auf den Boden aus diesen Videos zu erkennen. In der ersten
Studie wurde die Architektur von AlexNet verwendet, um das Modell anhand von Gehvideos
und öffentlich verfügbaren Datensätzen von Grund auf zu trainieren. Mit diesem Modell
wurde eine Gesamtgenauigkeit von 74% erreicht. Im nächsten Schritt wurde jedoch die
LSTM-Schicht in dieselbe Architektur integriert. Die eingebaute Fähigkeit von LSTM in
Bezug auf die zeitliche Information fĂĽhrte zu einer verbesserten Vorhersage der Etiketten
fĂĽr die FuĂźposition, und es wurde eine Genauigkeit von 91% erreicht. Allerdings gibt es
Schwierigkeiten bei der Vorhersage der richtigen Bezeichnungen in der letzten Phase des
Schwungs und der Standphase jedes FuĂźes.
Im nächsten Schritt wird das Transfer-Lernen eingesetzt, um die Vorteile von bereits
trainierten tiefen NNs zu nutzen, indem vortrainierte Gewichte verwendet werden. Zwei
bekannte Modelle, inceptionresnetv2 (IRNV-2) und densenet201 (DN-201), wurden mit
ihren gelernten Gewichten fĂĽr das erneute Training des NN auf neuen Daten verwendet. Das
auf Transfer-Lernen basierende vortrainierte NN verbesserte die Vorhersage von Kennzeichnungen
fĂĽr verschiedene FuĂźpositionen. Es reduzierte insbesondere die Schwankungen
in den Vorhersagen in der letzten Phase des Gangschwungs und der Standphase. Bei der
Vorhersage der Klassenbezeichnungen der Testdaten wurde eine Genauigkeit von 94% erreicht.
Da die Abweichung bei der Vorhersage des wahren Labels hauptsächlich ein Bild
betrug, konnte sie bei einer Bildrate von 30 Bildern pro Sekunde ignoriert werden.
Die vorhergesagten Markierungen wurden verwendet, um verschiedene räumlich-zeitliche
Parameter des Gangs zu extrahieren, die fĂĽr jedes Ganganalysesystem entscheidend sind.
Insgesamt wurden 12 Gangparameter quantifiziert und mit der durch Beobachtungsmethoden
gewonnenen Grundwahrheit verglichen. Die NN-basierten räumlich-zeitlichen Parameter
zeigten eine hohe Korrelation mit der Grundwahrheit, und in einigen Fällen wurde eine sehr
hohe Korrelation erzielt. Die Ergebnisse belegen die NĂĽtzlichkeit der vorgeschlagenen Methode.
DerWert des Parameters ĂĽber die Zeit ergab eine Zeitreihe, eine langfristige Darstellung des Ganges. Diese Zeitreihe konnte mit verschiedenen mathematischen Methoden weiter
analysiert werden.
Als dritter Beitrag in dieser Dissertation wurden Verbesserungen an den bestehenden
mathematischen Methoden der Zeitreihenanalyse von zeitlichen Gangdaten vorgeschlagen.
Zu diesem Zweck werden zwei Verfeinerungen bestehender entropiebasierter Methoden
zur Analyse von Schrittintervall-Zeitreihen vorgeschlagen. Diese Verfeinerungen wurden
an Schrittintervall-Zeitseriendaten von normalen und neurodegenerativen Erkrankungen
validiert, die aus der öffentlich zugänglichen Datenbank PhysioNet heruntergeladen wurden.
Die Ergebnisse zeigten, dass die von uns vorgeschlagene Methode eine klare Trennung
zwischen gesunden und kranken Gruppen ermöglicht.
In Zukunft könnten fortschrittliche medizinische Unterstützungssysteme, die künstliche
Intelligenz nutzen und von den hier vorgestellten Methoden abgeleitet sind, Ă„rzte bei der
Diagnose und langfristigen Ăśberwachung des Gangs von Patienten unterstĂĽtzen und so die
klinische Arbeitsbelastung verringern und die Patientensicherheit verbessern
The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets
Approximate entropy (ApEn) and sample entropy (SampEn) are mathematical algorithms created to measure the repeatability or predictability within a time series. Both algorithms are extremely sensitive to their input parameters: m (length of the data segment being compared), r (similarity criterion), and N (length of data). There is no established consensus on parameter selection in short data sets, especially for biological data. Therefore, the purpose of this research was to examine the robustness of these two entropy algorithms by exploring the effect of changing parameter values on short data sets. Data with known theoretical entropy qualities as well as experimental data from both healthy young and older adults was utilized. Our results demonstrate that both ApEn and SampEn are extremely sensitive to parameter choices, especially for very short data sets, N ≤ 200. We suggest using N larger than 200, an m of 2 and examine several r values before selecting your parameters. Extreme caution should be used when choosing parameters for experimental studies with both algorithms. Based on our current findings, it appears that SampEn is more reliable for short data sets. SampEn was less sensitive to changes in data length and demonstrated fewer problems with relative consistency
Ann Biomed Eng
The health and financial cost of falls has spurred research to differentiate the characteristics of fallers and non-fallers. Postural stability has received much of the attention with recent studies exploring various measures of entropy. This study compared the discriminatory ability of several entropy methods at differentiating two paradigms in the center-of-pressure of elderly individuals: (1) eyes open (EO) vs. eyes closed (EC) and (2) fallers (F) vs. non-fallers (NF). Methods were compared using the area under the curve (AUC) of the receiver-operating characteristic curves developed from logistic regression models. Overall, multiscale entropy (MSE) and composite multiscale entropy (CompMSE) performed the best with AUCs of 0.71 for EO/EC and 0.77 for F/NF. When methods were combined together to maximize the AUC, the entropy classifier had an AUC of for 0.91 the F/NF comparison. These results suggest researchers and clinicians attempting to create clinical tests to identify fallers should consider a combination of every entropy method when creating a classifying test. Additionally, MSE and CompMSE classifiers using polar coordinate data outperformed rectangular coordinate data, encouraging more research into the most appropriate time series for postural stability entropy analysis.L30 AG022963/AG/NIA NIH HHS/United StatesR01 OH009222/OH/NIOSH CDC HHS/United States2017-05-01T00:00:00Z26464267PMC483370
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