Using Wavelets for Gait and Arm Swing Analysis

The human walking pattern can be affected by different factors such as accidents, transplants, or diseases, like Parkinson’s disease, which affects motor and mental functions. In motor terms, this disease can generate alterations such as tremors, festination, rigidity, unbalance, slowness, and freezing of gait. Additionally, it is estimated that for the year 2040, the number of people with Parkinson’s in the world will be between 12.9 and 14.2 million people. These alarming figures make Parkinson’s disease an important focus of attention. In this chapter, we present contributions that suggest wavelet techniques as a useful tool to perform a gait and arm swing analysis; this represents an important approximation that can contribute to describe and differentiate people with Parkinson’s disease in early stages of the disease

Gait Based Vertical Ground Reaction Force Analysis for Parkinson's Disease Diagnosis Using Self Organizing Map

ABSTRACT The aim of this work is to use Self Organizing Map (SOM) for clustering of locomotion kinetic characteristics in normal and Parkinson's disease. The classification and analysis of the kinematic characteristics of human locomotion has been greatly increased by the use of artificial neural networks in recent years. The proposed methodology aims at overcoming the constraints of traditional analysis methods and to find new clinical ways for observing the large amount of information obtained in a gait lab. Self organizing maps (SOM) also called Kohonen maps are a special kind of neural networks that can be used for clustering tasks. The results are shown in the terms of sensitivity, specificity, accuracy, error rate from the two groups of features which are the Mean Coefficient of Variation and Mean Sum of Variation and Mean Max and Mean Standard deviation of the Ground Reaction Force. Results showing the potential of this technique for distinguishing between population of individuals with normal gait and with gait disorders of different causes of disease

Automatic recognition of gait patterns in human motor disorders using machine learning: A review

Background: automatic recognition of human movement is an effective strategy to assess abnormal gait patterns. Machine learning approaches are mainly applied due to their ability to work with multidimensional nonlinear features. Purpose: to compare several machine learning algorithms employed for gait pattern recognition in motor disorders using discriminant features extracted from gait dynamics. Additionally, this work highlights procedures that improve gait recognition performance. Methods: we conducted an electronic literature search on Web of Science, IEEE, and Scopus, using “human recognition”, “gait patterns’’, and “feature selection methods” as relevant keywords. Results: analysis of the literature showed that kernel principal component analysis and genetic algorithms are efficient at reducing dimensional features due to their ability to process nonlinear data and converge to global optimum. Comparative analysis of machine learning performance showed that support vector machines (SVMs) exhibited higher accuracy and proper generalization for new instances. Conclusions: automatic recognition by combining dimensional data reduction, cross-validation and normalization techniques with SVMs may offer an objective and rapid tool for investigating the subject's clinical status. Future directions comprise the real-time application of these tools to drive powered assistive devices in free-living conditions.This work was supported by the FCT - Fundação para a Ciência e Tecnologia - with the reference scholarship SFRH/BD/108309/2015, and the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalização (POCI) - with the reference project POCI-01-0145-FEDER-006941. Also, this work was partially supported by grant RYC-2014-16613 by Spanish Ministry of Economy and Competitiveness

Review—Emerging Portable Technologies for Gait Analysis in Neurological Disorders

The understanding of locomotion in neurological disorders requires technologies for quantitative gait analysis. Numerous modalities are available today to objectively capture spatiotemporal gait and postural control features. Nevertheless, many obstacles prevent the application of these technologies to their full potential in neurological research and especially clinical practice. These include the required expert knowledge, time for data collection, and missing standards for data analysis and reporting. Here, we provide a technological review of wearable and vision-based portable motion analysis tools that emerged in the last decade with recent applications in neurological disorders such as Parkinson's disease and Multiple Sclerosis. The goal is to enable the reader to understand the available technologies with their individual strengths and limitations in order to make an informed decision for own investigations and clinical applications. We foresee that ongoing developments toward user-friendly automated devices will allow for closed-loop applications, long-term monitoring, and telemedical consulting in real-life environments.DFG, 424778381, Behandlung motorischer Netzwerkstörungen mittels Neuromodulatio

Designing free-living quantitative reports for Parkinson’s

Tese de mestrado, Engenharia Informática (Sistemas de Informação) Universidade de Lisboa, Faculdade de Ciências, 2018A doença de Parkinson (DP) é um distúrbio neurodegenerativo frequente e progressivo, afetando cerca de 1 % da população mundial. O envelhecimento da população poderá aumentar o número de pessoas que vivem com esta doença nos próximos anos. A DP é caracterizada por tremores, rigidez do tronco e membros. Estes sintomas manifestam-se pela redução dos níveis de dopamina, devido à morte das células cerebrais que a produzem, que ocorre apenas se mais de setenta ou oitenta por cento dessas células morrerem [12] [24] [10] [19]. Embora cada paciente tenha os seus próprios sintomas, esta doença tem, geralmente, como episódio inicial um leve tremor na mão, braço ou perna. A progressão da doença pode provocar instabilidade postural, criando dificuldades nas tarefas de sentar, andar (os passos tendem a ficar mais lentos, arrastados e o normal movimento pendular dos braços não ocorre) e estar de pé. Uma das características da DP ´e ser altamente variável. Os sintomas, juntamente com o grau de incapacidade, tendem a variar bastante ao longo do dia [24] [19]. Apesar de não existir uma cura, existem algumas intervenções farmacológicas para melhorar a qualidade de vida do paciente, contudo tem de ser aplicadas de acordo com o estado da doença em que o paciente se encontra. Parte dos medicamentos estimula a libertação de dopamina, caso existam células produtoras, caso contrário, é administrada levodopa, que é posteriormente convertida em dopamina [12]. Desafios para a prática clínica incluem a compreensão da progressão da doença, a resposta a intervenções farmacológicas e não farmacológicas, as flutuações diárias e suas possíveis explicações. No entanto, a quantidade de informação disponível para um clínico perceber estas alterações é escassa. Por exemplo, as avaliações são feitas durante consultas clínicas que são espaçadas no tempo e provavelmente as flutuações que ocorrem ao longo do dia não irão ficar registadas [16]. Em ambiente clínico, existem diversos testes que são realizados pelos pacientes: controle postural, locomoção, resistência, sit-to-stand-to-sit e TUG (Time up and go), que ajudam os clínicos a obter dados objectivos sobre o estado clínico dos pacientes. Estudos recentes também mostram que os acelerómetros podem ser usados para obter estes dados. Na clínica existem diversas formas de avaliar os pacientes. Embora as flutuações fora deste ambiente sejam perdidas, pois é em que provavelmente acontecem [11] [7] [4]. Para perceber o que ocorre com os pacientes fora de um ambiente controlado, os clínicos fazem perguntas aos pacientes, contudo é provável que exista menos rigor do que o necessário, pois nem sempre é fácil para os pacientes se recordarem do que aconteceu [15]. Assim, o uso de diários para ajudar os pacientes a resumir o seu dia e fornecer informações úteis aos clínicos ´e uma alternativa. Diários em papel preenchidos ao longo do dia por pacientes fora da consulta podem ajudar a recolher mais dados em ambiente não controlado. No entanto, existe um problema de “compliance” no uso de diários. Um teste mostrou que diários em papel podem ser não corresponder ao que realmente ocorre na maioria das vezes, pois não são preenchidos no tempo em que deveriam, o que poder à levar a possíveis omissões de eventos. Os diários eletrónicos (DE) podem ajudar a minimizar este problema, aplicando medidas de controlo que garantam a resposta dos pacientes no momento certo ou que registem quando tal não ocorre. No entanto, DE também tem problemas relacionados com o possível esquecimento de preencher o diário, apesar de existirem formas de alertar as pessoas para preenchê-lo. Podem por exemplo não estar perto para detetar os alarmes [15]. Mais recentemente, estudos mostram que amétricas obtidas apenas em laboratório também podem ser usadas em ambiente não controlado com a ajuda de sensores inerciais. Como tal, exista agora forma de complementar os dados subjetivos obtidos pelos diários dos pacientes, utilizando os dados objetivos (energia, sono, atividade física) recolhidos com a ajuda de sensores[6] [5]. Durante a avaliação de um paciente, o clínico tem de realizar múltiplas tarefas, incluindo a recolha de dados dos testes em laboratório e perceber o que ocorreu com o paciente fora do ambiente da avaliação, fazendo perguntas aos pacientes. Os clínicos têm tempo limitado para cada paciente, portanto, introduzir uma nova tarefa pode ser um desafio [16]. No entanto, a consulta orientada aos dados pode ajudar a obter uma visão geral mais objetiva. Com o auxílio dos dados objetivos os clínicos dispõem de mais ferramentas para entender melhor as necessidades dos pacientes [16]. Ainda existem algumas dificuldades em como introduzir as novas ferramentas sem comprometer a forma como a relação entre pacientes e clínicos ocorre. [20]. Esta tese de mestrado foi desenvolvida no LASIGE e faz parte de um projeto que pretende dar mais dados que completem a informação que os clínicos dispões sobre os pacientes. O projeto é composto por três partes, cada uma independente entre si e desenvolvida por diferentes membros da equipa, tendo, contudo, partes em comum. Isso permitiu realizar, em conjunto, entrevistas e grupos de foco sempre que assim se justificou, dando mais contexto sobre todo o projeto e cada parte em específico durante as entrevistas. O principal objetivo do projeto é criar uma plataforma que ajude os clínicos e os pacientes. Esta plataforma segue uma abordagem baseada em dados para fornecer uma maneira mais fácil, rápida e engenhosa de obter mais dados sobre os pacientes. Esta tese foca-se apenas em ambiente não controlado, tendo como objetivo perceber o que acontece no dia a dia dos pacientes. Para fornecer dados das atividades diárias de atividade física e análise do sono, é necessário recolher, processar e analisar dados. No entanto, o principal desafio aqui não é como os dados irão ser obtidos, mas sim como devem ser visualizados pelos clínicos para que realmente possam fazer a diferença e ter um impacto sobre como os clínicos interagem com os pacientes. É importante considerar a perspetiva dos clínicos de como os dados devem ser apresentados, mas também o ponto de vista do paciente para obter uma visão mais abrangente de como a plataforma deve ser construída. Os pacientes são o centro da pesquisa, o principal objetivo aqui é tentar melhorar a sua qualidade de vida, ajudando os clínicos a tomar decisões mais informadas e serem capazes de fornecer uma explicação mais compreensível sobre o que ocorre fora do contexto clínico. Existem três sub-objetivos: caracterizar as práticas de avaliação atuais e as suas limitações, pesquisar o estado de arte do sobre o uso de sensores inerciais e desenhar e avaliar uma plataforma utilizável baseada em dados. O primeiro objetivo pretender dar uma visão geral das práticas atuais da avaliação clínica e as suas limitações, além de mostrar as oportunidades da introdução de uma abordagem baseada em dados no processo. O segundo objetivo leva a um resumo do que já está a ser feito em termos de pesquisa relacionada com o uso de sensores inerciais. Isso permite entender o que foi validado clinicamente e as limitações que existem e que podem levar a novas oportunidades de pesquisa. A consulta baseada em dados ´e um processo que pode levar a uma melhor compreensão dos pacientes por parte dos clínicos. No entanto, não existe uma abordagem que funcione em todos os ambientes possíveis. Na minha pesquisa eu tento perceber se esta metodologia pode ser utilizada e caso seja possível qual será a melhor abordagem para a aplicar. Com esta plataforma, espero que a qualidade de vida dos pacientes melhore, criando para os clínicos uma nova plataforma que pode proporcionar uma maneira mais fácil de saber qual o estado do paciente fora de ambientes controlados e promover a relação entre pacientes e clínicos. O DataPark é uma aplicação web capaz de gerar relatórios contendo dados visuais e textuais com base em dados de acelerometria. Os dados ”raw” são processados e analisados pelo nosso sistema com o auxílio de algoritmos. Os dados obtidos são de: energia gasta, atividade física e sono. Para validar a nossa abordagem foram realizados dois estudos. O primeiro teve como objetivo perceber se esta metodologia pode ser aplicada. O segundo consistiu num uso prolongado da plataforma para perceber quais os benefícios e limitações da mesma. Ambos os estudos permitiram concluir que o DataPark pode ser útil para os clínicos, sendo ainda necessário realizar estudos com um maior grau de profundidade para adequar as ferramentas as necessidades dos clínicos.Parkinson’s disease (PD) is a frequent and progressive neurodegenerative disorder, affecting about 1% of the world population. PD is characterized by tremors, rigidity of the trunk and limbs and low movements. With the progression of the disease, the postural instability and the difficulty in walking can be very disabling, making daily tasks more difficult. Challenges for clinical practice include understanding the progression of the disease, the response to pharmacological and non-pharmacological interventions, and the fluctuations the patient goes through alongside their explanations. However, the amount of information available for a clinician to understand these phenomena is scarce. This thesis proposes a data-driven approach to improve the amount of information that clinicians have about their patients. The focus is collecting objective data from free-living environment and show it in an proper way for enriching the knowledge of clinicians about their patients. This is part of a larger platform which holds data retrieved in laboratory context and subjective data in free-living. DataPark allows to generate personalized reports build by clinicians that can adjust according to the needs of each patient. The primary areas of analysis include physical activity and sleep. There is an ongoing collaboration with CNS (Campus Neurológico Sénior) which grants access to patients’ data. A preliminary study was performed to understand what are the relevant points of analysis that clinicians want to have. To validate the use and how DataPark influence the actual process a final study in a real environment was performed where participants could use the platform without any interventions from the research team

A modular approach for modeling, detecting, and tracking freezing of gait in Parkinson disease using inertial sensors

Parkinson disease, the second most common neurodegenerative disorder, is caused by the loss of dopaminergic subcortical neurons. Approximately 50% of people with Parkinson disease experience freezing of gait (FOG), a brief, episodic absence or marked reduction of forward progression of the feet despite the intention to walk. FOG causes falls and is resistant to medication in more than 50% of cases. FOG episodes can often be interrupted by mechanical interventions (e.g., a verbal reminder to march), but it is often not practical to apply these interventions on demand (e.g., there is not usually another person to detect an FOG episode and provide the reminder).Wearable sensors offer the possibility of detecting FOG episodes in real time and thus developing a “closed-loop” treatment: real-time detection can be coupled with on-demand interventions. Objective evaluation methods using wearable sensor technology to monitor and assess FOG have met with varying success. They do not use a signal model that captures FOG patterns explicitly, and they are of limited help in understanding the underlying mechanisms in the structure of the sensor data captured during FOG. In this dissertation, we first develop physically-based signal models for the sensor data, design statistical signal processing methods to detect FOG based on its patterns, and compute the probability of FOG. Then, we proceed to validate the system, using data from experimental gait assessment in a group of people with Parkinson disease.We further develop a modular approach to model, detect, and track FOG in Parkinson disease, using four modules, namely the detection, navigation, validation, and filtering modules. To capture the gait motion, we use an inertial measurement unit (IMU) consisting of a three-axis accelerometer and a three-axis gyroscope. We first build physically-based signal models that describe “no movement” and “trembling motion” during FOG events. In the detection module, we design a generalized likelihood ratio test framework to develop a two-stage detector for determining the zero-velocity event intervals (ZVEI) and trembling event intervals (TREI) that are associated with FOG. However, not all the detected TREI are associated with FOG. Therefore, to filter out the TREI which are not associated with FOG, we consider the fact that the alternating trembling motion in FOG is associated with low foot speeds and small pitch angles. Next, to estimate these gait parameters, we employ a zero-velocity aided inertial navigation system (ZV-INS) in the navigation module. The ZV-INS uses the ZVEI as pseudo measurements, along with a Kalman filter, to estimate the position, velocity, and orientation angles of the foot.To track the degradation of the gait parameters prior to the incidence of FOG, we detect valid gait cycles in the validation module. We first identify the non-stationary segments of the gyroscope signal in the sagittal plane, using ZVEI. Next, we preprocess the non-stationary segments by scaling and interpolating the signal. Finally, we validate the preprocessed non-stationary segment of the gyroscope signal in the sagittal plane as a valid gait cycle, using an optimization framework called sparsity-assisted wavelet denoising (SAWD). In the SAWD algorithm, we simultaneously combine low-pass filtering, multiresolution representations (wavelets), and a sparsity-inducing norm to obtain a sparse representation of the gyroscope signal in the sagittal plane for valid gait cycles, in the form of a discrete wavelet transform coefficient vector. We compute the root-mean-square error between the generated template and the sparse representation of the non-stationary segment of the gyroscope data in the sagittal plane, obtained using the SAWD algorithm. If the root-mean-square error is less than a fixed threshold, then the gait cycle is considered valid.Finally, to detect the onset and duration of FOG, we develop a point-process filter that computes the probability of FOG (pFOG). We model the edges of the TREI as a point-process, then assign weights to the edges, which depend on a participant-specific tunable parameter and the average value of the gait parameters observed in the bin containing the edge. To compute pFOG, we develop a Bayesian recursive filter and integrate the weights assigned to the edges of the TREI over a time window. To adaptively adjust the participant-specific tunable parameter, we develop two novel approaches that assign weights to the edges of the TREI based on the gait parameters extracted from the last valid gait cycle and the foot motion dynamics. We validate the performance of the modular system design using real data obtained from people with Parkinson disease who performed a battery of gait tasks known to trigger FOG. The results indicate improved performance, with an average accuracy greater than 85% and an average false positive rate of less than 14%. Altogether, we not only improve the accuracy of FOG detection but also open new avenues towards the development of low-cost remote health monitoring systems, which will help provide insights into the frequency and patterns of FOG that affect the quality of daily life in people with Parkinson disease

Identificación de marcadores clínicos cognitivos y motores en pacientes con enfermedad de Parkinson en estadios tempranos: evaluación clínica complementada con un dispositivo de análisis del movimiento [recurso electrónico]

La enfermedad de Parkinson (EP) es una enfermedad neurodegenerativa que se caracteriza por la presencia de síntomas motores y no motores que pueden aparecer sutilmente de forma gradual en los estadios tempranos. La detección de estos síntomas es un punto esencial en la toma de decisiones por lo que el uso de dispositivos tecnológicos dentro del entorno clínico para objetivar las medidas es una herramienta eficaz para hacer un seguimiento, definir un pronóstico y desarrollar un manejo personalizado de la enfermedad

Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice

This Special Issue shows a range of potential opportunities for the application of wearable movement sensors in motor rehabilitation. However, the papers surely do not cover the whole field of physical behavior monitoring in motor rehabilitation. Most studies in this Special Issue focused on the technical validation of wearable sensors and the development of algorithms. Clinical validation studies, studies applying wearable sensors for the monitoring of physical behavior in daily life conditions, and papers about the implementation of wearable sensors in motor rehabilitation are under-represented in this Special Issue. Studies investigating the usability and feasibility of wearable movement sensors in clinical populations were lacking. We encourage researchers to investigate the usability, acceptance, feasibility, reliability, and clinical validity of wearable sensors in clinical populations to facilitate the application of wearable movement sensors in motor rehabilitation

Characterising postural sway fluctuations in humans using linear and nonlinear methods

Introduction: Postural control is a prerequisite to many everyday and sporting activities which requires the interaction of multiple sensorimotor processes. As long as we have no balance disorders, the maintenance of an erect standing position is taken for granted with automatic running control processes. It is well known that with increasing age or disease balance problems occur which often cause fall-related injuries. To assess balance performance, posturography is widely applied in which body sway is traditionally viewed as a manifestation of random fluctuations. Thus, the amount of sway is solely used as an index of postural stability, that is, less sway is an indication of better control. But, traditional measures of variability fail to account for the temporal organisation of postural sway. The concept of nonlinear dynamics suggests that variability in the motor output is not random but structured. It provides the stimulus to reveal the functionality of postural sway. This thesis evaluates nonlinear analysis tools in addition to classic linear methods in terms of age-related modifications of postural control and under different standing conditions in order to broaden the existing knowledge of postural control processes. Methods: Static posturographic analyses were conducted which included the recording of centre of pressure (COP) time series by means of a force plate. Linear and nonlinear methods were used to quantify postural sway variability in order to evaluate both the amount and structure of sway. Classic time and frequency domain COP parameters were computed. In addition, wavelet transform (WT), multiscale entropy, detrended fluctuation analysis, and scaled windowed variance method were applied to COP signals in order to derive structural COP parameters. Two experiments were performed. 1) 16 young (26.1 ± 6.7 years), healthy subjects were asked to adopt a bipedal stance under single- and dual-task conditions. Three trials were conduced each with a different sampling duration: 30, 60, and 300 seconds [s]. 2) 26 young (28.15 ± 5.86 years) and 13 elderly (72 ± 7 years) subjects stood quietly for 60 s on five different surfaces which imposed different biomechanical constraints: level ground (LG), one foot on a step (ST), uphill (UH), downhill (DH), and slope (SL). Additional to COP recordings, limb load symmetry was assessed via foot pressure insoles. Results: We found a higher sensitivity of structural COP parameters to modulations of postural control and partly an improved evaluation of sway dynamics in longer COP recordings. WT revealed a reweighing of frequency bands in response to altered standing conditions. Scaling exponents and entropy values of COP signals were task-dependent. Higher entropy values were found under the dual-task and condition ST. The time scales affected under the altered standing positions differed between groups and sway directions. Mainly larger posturograms were found in the elderly. Age effects were especially revealed in position ST and concerning medial-lateral COP signals. Load asymmetry was stronger in elderly subjects for LG, UH, and DH positions. Discussion: Modifications of multiple time scales corresponds to an interplay of control subsystems to cope with the altered task demands. The affected time scales are age-dependent suggesting a change of control processes. Higher irregularity under the dual-task indicates a more complex motor output which is interpreted as less attentional investment into postural control. Larger complexity is evident for ST in contrast to LG position. ST obviously challenges lateral sway which is counteracted differently between groups. Load asymmetry suggests that especially elderly subjects adopt a step-initiation strategy. Conclusion: A continued application of nonlinear methods is necessary to broaden the understanding of postural control mechanisms and to identify classifiers for balance dysfunctions. Structural COP parameters provide a more comprehensive indication of postural control system properties between groups and task demands. COP recordings of at least 60 s are recommended to adequately quantify COP signal structure. The analysis of postural strategies in everyday activities increases the ecological validity of postural control studies and can provide valuable information regarding the development of effective rehabilitation programs.Die posturale Kontrolle ist eine Voraussetzung für viele Alltagsaktivitäten und sportliche Bewegungen. Man weiß heute, dass den Kontrollmechanismen eine komplexe Interaktion sensomotorischer Prozesse unterliegt (Horak and Mcpherson, 1996; Oie et al., 2002). Solange keine Gleichgewichtsdefizite vorliegen, nehmen wir es als selbstverständlich wahr aufrecht Stehen zu können, ohne uns der Komplexität posturaler Kontrollmechanismen bewusst zu sein. Studien haben gezeigt, dass es mit zunehmendem Alter zu Defiziten in der posturalen Kontrolle kommt (Pasquier et al., 2003; Woollacott, 1993). Oftmals ist ein erhöhtes Sturzrisiko die Folge, welches unter anderem mit Verletzungen, einer eingeschränkten Mobilitätsowie einer verminderten Lebensqualität einhergehen kann (Era et al., 1997; Frank and Patla, 2003). Seit vielen Jahren schon werden posturographische Untersuchungen durchgeführt mit dem Ziel, posturale Kontrollmechanismen abzuleiten undDysfunktionen im posturalen System zu diagnostizieren (Piirtola and Era, 2006). Jedoch sind die Mechanismen, die der posturalen Kontrolle unterliegen, bis heute nicht eindeutig verstanden. Neue Erkenntnisse konnten in den letzten Jahrenvor allem durch ein erweitertes Verständnis von Bewegungsvariabilität gewonnen werden (Stergiou and Decker, 2011; Lippens and Nagel, 2009). Traditionell werden posturale Analysen unter der Annahme durchgeführt und interpretiert, dass Variabilität eine Art “Rauschen” (white noise) ist und somit Ausdruck eines Fehlers. Posturale Schwankungen werden als zufällige, nicht intendierte Abweichungen gesehen (Loosch, 1997). Der Parameter “Schwankungsausmaß” wird zur Diagnostik des statischen Gleichgewichts herangezogen und bei einer größeren Schwankung wird eine schlechtere posturale Kontrolle diagnostiziert. Im Gegensatz dazu weist der systemdynamische Modellansatz auf die funktionale Rolle der Variabilität hin (van Emmerik and van Wegen, 2002). Variabilität ist Ausdruck der Anpassung und Flexibilität und somit notwendig, um auf ständige Umweltveränderungen reagieren zu können. Ein erhöhtes Schwankungsausmaß ist demnach nicht ausschließlich ein Zeichen für Instabilität (Newell et al., 1993). Eine größere Variabilität posturaler Schwankungen kann auch positiv im Sinne von mehr Umweltexploration interpretiert werden (Lacour et al., 2008). So konnte gezeigt werden, dass posturale Schwankungen nicht zufällig sind, sondern eine Struktur enthalten (Duarte and Zatsiorsky, 2000), dessen Charakterisierung zusätzliche Informationen über die Organisation des posturalen Kontrollsystems liefert (Stergiou and Decker, 2011). Die vorliegende Arbeit evaluiert nichtlineare Methoden unter dem systemdynamischen Ansatz zusätzlich zu den traditionell eingesetzten linearen Methoden. Ziel ist es, neben der Quantifizierung des Ausmaßes posturaler Schwankungen ihre Struktur zu charakterisieren, um das Verständnis für posturale Kontrollmechanismen zu erweitern. Die Evaluierung erfolgt zunächst über den Vergleich von Stehen mit und ohne kognitiver Zusatzaufgabe, wo Studien erste Hinweise auf eine veränderte COP1 Signalstruktur geben (Cavanaugh et al., 2007; Donker et al., 2007; Stins et al., 2009). Durch das Betrachten unterschiedlicher Signallängen und eines umfangreichen Methodenspektrums sollen Anhaltspunkte für die Applikation vonnichtlinearen in Kombination mit linearen Analyseverfahren abgeleitet werden. In einer zweiten Untersuchung werden diese dann in einem angewandten Studiendesign umgesetzt. Dabei wird die Veränderung posturaler Kontrollstrategien bei unterschiedlichen Standpositionen untersucht, welche alltägliche Situationen simulieren, unter Berücksichtigung altersbedingter Effekte. Dies ist ein erster Ansatz zur Erreichung einer hohen ökologischen Validität posturaler Studien (Frank and Patla, 2003; Visser et al., 2008). Erst kürzlich wurde gezeigt, dass bei älteren Menschen meist interne Auslöser (z.B. Gewichtsverlagerungen) ursächlich für Stürze sind (Robinovitch et al., 2013). Zudem haben ältere Personen größere Schwierigkeiten auf Umgebungsveränderungen zu reagieren (Nardone and Schieppati, 2010). Es ist jedoch bisher unbekannt, wie sich Defizite in der Gleichgewichtskontrolle älterer Menschen auf die Struktur posturaler Schwankungen auswirken. ..