Gait analysis under the lens of statistical physics

Human gait; Irreversibility; Multi-fractal analysisMarcha humana; Irreversibilidad; Análisis multifractalMarxa humana; Irreversibilitat; Anàlisi 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

Review and classification of variability analysis techniques with clinical applications

Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis

Characterizing psychological dimensions in non-pathological subjects through autonomic nervous system dynamics

The objective assessment of psychological traits of healthy subjects and psychiatric patients has been growing interest in clinical and bioengineering research fields during the last decade. Several experimental evidences strongly suggest that a link between Autonomic Nervous System (ANS) dynamics and specific dimensions such as anxiety, social phobia, stress, and emotional regulation might exist. Nevertheless, an extensive investigation on a wide range of psycho-cognitive scales and ANS non-invasive markers gathered from standard and non-linear analysis still needs to be addressed. In this study, we analyzed the discerning and correlation capabilities of a comprehensive set of ANS features and psycho-cognitive scales in 29 non-pathological subjects monitored during resting conditions. In particular, the state of the art of standard and non-linear analysis was performed on Heart Rate Variability, InterBreath Interval series, and InterBeat Respiration series, which were considered as monovariate and multivariate measurements. Experimental results show that each ANS feature is linked to specific psychological traits. Moreover, non-linear analysis outperforms the psychological assessment with respect to standard analysis. Considering that the current clinical practice relies only on subjective scores from interviews and questionnaires, this study provides objective tools for the assessment of psychological dimensions

Entropy Measures in Machine Fault Diagnosis: Insights and Applications

Entropy, as a complexity measure, has been widely applied for time series analysis. One preeminent example is the design of machine condition monitoring and industrial fault diagnostic systems. The occurrence of failures in a machine will typically lead to non-linear characteristics in the measurements, caused by instantaneous variations, which can increase the complexity in the system response. Entropy measures are suitable to quantify such dynamic changes in the underlying process, distinguishing between different system conditions. However, notions of entropy are defined differently in various contexts (e.g., information theory and dynamical systems theory), which may confound researchers in the applied sciences. In this paper, we have systematically reviewed the theoretical development of some fundamental entropy measures and clarified the relations among them. Then, typical entropy-based applications of machine fault diagnostic systems are summarized. Further, insights into possible applications of the entropy measures are explained, as to where and how these measures can be useful towards future data-driven fault diagnosis methodologies. Finally, potential research trends in this area are discussed, with the intent of improving online entropy estimation and expanding its applicability to a wider range of intelligent fault diagnostic systems

Amplitude- and Fluctuation-based Dispersion Entropy

Dispersion entropy (DispEn) is a recently introduced entropy metric to quantify the uncertainty of time series. It is fast and, so far, it has demonstrated very good performance in the characterisation of time series. It includes a mapping step, but the effect of different mappings has not been studied yet. Here, we investigate the effect of linear and nonlinear mapping approaches in DispEn. We also inspect the sensitivity of different parameters of DispEn to noise. Moreover, we develop fluctuation-based DispEn (FDispEn) as a measure to deal with only the fluctuations of time series. Furthermore, the original and fluctuation-based forbidden dispersion patterns are introduced to discriminate deterministic from stochastic time series. Finally, we compare the performance of DispEn, FDispEn, permutation entropy, sample entropy, and Lempel–Ziv complexity on two physiological datasets. The results show that DispEn is the most consistent technique to distinguish various dynamics of the biomedical signals. Due to their advantages over existing entropy methods, DispEn and FDispEn are expected to be broadly used for the characterization of a wide variety of real-world time series. The MATLAB codes used in this paper are freely available at http://dx.doi.org/10.7488/ds/2326

Human Gait Analysis using Spatiotemporal Data Obtained from Gait Videos

Mit der Entwicklung von Deep-Learning-Techniken sind Deep-acNN-basierte Methoden zum Standard für 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, einschließlich 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 geführt. 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 für 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 für 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 stoßen 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, über Methoden zu verfügen, die langfristige Daten über 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 kostengünstige 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 für 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 Fußposition 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 führte zu einer verbesserten Vorhersage der Etiketten für die Fußposition, 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 Fußes. 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 für das erneute Training des NN auf neuen Daten verwendet. Das auf Transfer-Lernen basierende vortrainierte NN verbesserte die Vorhersage von Kennzeichnungen für verschiedene Fußpositionen. 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 für 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 Nützlichkeit der vorgeschlagenen Methode. DerWert des Parameters über 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, Ärzte bei der Diagnose und langfristigen Überwachung des Gangs von Patienten unterstützen und so die klinische Arbeitsbelastung verringern und die Patientensicherheit verbessern

Characteristics and coupling of cardiac and locomotor rhythms during treadmill walking tasks

Studying the variability of physiological subsystems (e.g., cardiac and locomotor control systems) has been insightful in understanding how functional and dysfunctional patterns emerge within their behaviors. The coupling of these subsystems (termed cardiolocomotor coupling) is believed to be important to maintain healthy functioning in the diverse conditions in which individuals must operate. Aging and pathology result in alterations to both the patterns of individual systems, as well as to how those systems couple to each other. By examining cardiac and locomotor rhythms concurrently during treadmill walking, it is possible to ascertain how these two rhythms relate to each other in different populations (i.e., younger and older adults) and with varying task constraints (i.e., a gait synchronization task or fast walking task). The purpose of this research was to simultaneously document the characteristics of cardiac and gait rhythms in younger (18-35 yrs) and older (63-80 yrs) healthy adults while walking and to establish the extent to which changes in these systems are coupled when gait is constrained. This study consisted of two repeated-measures experiments that participants completed on two separate days. Both experiments consisted of three 15-minute phases. During the first (baseline) and third (retention) phases of both experiments, participants walked with no cues or specific instructions at their preferred walking speed. During the second phase, participants were asked to synchronize their step falls to the timing of a visual cue (experiment 1) or to walk at 125% of their preferred walking speed (experiment 2). Fifty-one healthy adults (26 older, 67.67±4.70 yrs, 1.72±0.09 m, 70.13±14.30 kg; 25 younger, 24.57±4.29 yrs, 1.76±0.09 m, 73.34±15.35 kg) participated in this study. Participants’ cardiac rhythms (R-R interval time series) and locomotor rhythms (stride interval, step width, and step length time series) were measured while walking on a treadmill. Characteristics of variability in cardiac and locomotor rhythms and the coupling between cardiac and gait rhythms were measured. Results revealed that younger and older healthy adults alter gait patterns similarly when presented with a gait synchronization or fast walking task and that these tasks also alter cardiac patterns. Likewise, both groups exhibited enhanced cardiolocomotor coupling when tasked with a step timing constraint or increased speed during treadmill walking. Combined, these findings suggest that walking tasks likely alter both locomotor and cardiac dynamics and the coupling of physiological subsystems could be insightful in understanding the diverse effects aging and pathology have on individuals

Assessment of Time and Frequency Domain Entropies to Detect Sleep Apnoea in Heart Rate Variability Recordings from Men and Women

Producción CientíficaHeart rate variability (HRV) provides useful information about heart dynamics both under healthy and pathological conditions. Entropy measures have shown their utility to characterize these dynamics. In this paper, we assess the ability of spectral entropy (SE) and multiscale entropy (MsE) to characterize the sleep apnoea-hypopnea syndrome (SAHS) in HRV recordings from 188 subjects. Additionally, we evaluate eventual differences in these analyses depending on the gender. We found that the SE computed from the very low frequency band and the low frequency band showed ability to characterize SAHS regardless the gender; and that MsE features may be able to distinguish gender specificities. SE and MsE showed complementarity to detect SAHS, since several features from both analyses were automatically selected by the forward-selection backward-elimination algorithm. Finally, SAHS was modelled through logistic regression (LR) by using optimum sets of selected features. Modelling SAHS by genders reached significant higher performance than doing it in a jointly way. The highest diagnostic ability was reached by modelling SAHS in women. The LR classifier achieved 85.2% accuracy (Acc) and 0.951 area under the ROC curve (AROC). LR for men reached 77.6% Acc and 0.895 AROC, whereas LR for the whole set reached 72.3% Acc and 0.885 AROC. Our results show the usefulness of the SE and MsE analyses of HRV to detect SAHS, as well as suggest that, when using HRV, SAHS may be more accurately modelled if data are separated by gender.Ministerio de Economía, Industria y Competitividad (TEC2011-22987)Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. VA059U13

Gait analyses of parkinson’s disease patients using multiscale entropy

Copyright: © 2021 by the author(s). Parkinson’s disease (PD) is a type of neurodegenerative diseases. PD influences gait in many aspects: reduced gait speed and step length, increased axial rigidity, and impaired rhythmicity. Gait-related data used in this study are from PhysioNet. Twenty-one PD patients and five healthy controls (CO) were sorted into four groups: PD without task (PDw), PD with dual task (PDd), control without task (COw), and control with dual task (COd). Since dual task actions are attention demanding, either gait or cognitive function may be affected. To quantify the used walking data, eight pressure sensors installed in each insole are used to measure the vertical ground reaction force. Thus, quantitative measurement analysis is performed utilizing multiscale entropy (MSE) and complexity index (CI) to analyze and differentiate between the ground reaction force of the four different groups. Results show that the CI of patients with PD is higher than that of CO and 11 of the sensor signals are statistically significant (p < 0.05). The COd group has larger CI values at the beginning (p = 0.021) but they get lower at the end of the test (p = 0.000) compared to that in the COw group. The end-of-test CI for the PDw group is lower in one of the feet sensor signals, and in the right total ground reaction force compared to the PDd group counterparts. In conclusion, when people start to adjust their gait due to pathology or stress, CI may increase first and reach a peak, but it decreases afterward when stress or pathology is further increased

Multivariate multiscale complexity analysis

Established dynamical complexity analysis measures operate at a single scale and thus fail to quantify inherent long-range correlations in real world data, a key feature of complex systems. They are designed for scalar time series, however, multivariate observations are common in modern real world scenarios and their simultaneous analysis is a prerequisite for the understanding of the underlying signal generating model. To that end, this thesis first introduces a notion of multivariate sample entropy and thus extends the current univariate complexity analysis to the multivariate case. The proposed multivariate multiscale entropy (MMSE) algorithm is shown to be capable of addressing the dynamical complexity of such data directly in the domain where they reside, and at multiple temporal scales, thus making full use of all the available information, both within and across the multiple data channels. Next, the intrinsic multivariate scales of the input data are generated adaptively via the multivariate empirical mode decomposition (MEMD) algorithm. This allows for both generating comparable scales from multiple data channels, and for temporal scales of same length as the length of input signal, thus, removing the critical limitation on input data length in current complexity analysis methods. The resulting MEMD-enhanced MMSE method is also shown to be suitable for non-stationary multivariate data analysis owing to the data-driven nature of MEMD algorithm, as non-stationarity is the biggest obstacle for meaningful complexity analysis. This thesis presents a quantum step forward in this area, by introducing robust and physically meaningful complexity estimates of real-world systems, which are typically multivariate, finite in duration, and of noisy and heterogeneous natures. This also allows us to gain better understanding of the complexity of the underlying multivariate model and more degrees of freedom and rigor in the analysis. Simulations on both synthetic and real world multivariate data sets support the analysis