Figure 1 - uploaded by Ateke Goshvarpour
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The gait cycle extended from heel strike to heel strike of one leg. It consists of stance phase and swing phase.
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Nonlinear dynamics has been introduced to the analysis of biological data and increasingly recognized to be functionally relevant. The aim of this study is to evaluate nonlinear and chaotic dynamics of gait signals. For this purpose, we analyzed gait data in ten healthy subjects who walked for an hour at their usual, slow and fast paces. Poincare p...
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Citations
... Animal locomotion is a result of complex interactions among musculoskeletal system, sensory systems and the environment (Biewener and Daniel, 2010). While gait always follows a regular pattern, animal locomotion is actually nonperiodic with small and subtle stride-to-stride variations (Goshvarpour and Goshvarpour, 2012). Nonperiodic locomotion can be quasi-periodic, deterministic chaotic, or stochastic. ...
Tranquilization of horses with acepromazine has been used to suppress erratic head movements and increase the accuracy of a lameness examination. Some equine clinicians believe that tranquilization with acepromazine will make lameness more evident by causing the horse to focus on adjusting its gait to avoid limb pain rather than its surroundings. The aim of this study was to investigate the effect of acepromazine on the Lyapunov exponents of lame horses. Ten lame horses were trotted in a straight line for a minimum of 25 strides. Kinematic data created by head movement were analyzed. Nonlinear analysis methods were applied to lame horse locomotion. The effect of acepromazine on the largest Lyapunov exponents of the lame horses were investigated. There was no statistically significant effect of acepromazine on the maximum value of Lyapunov exponents. The nonlinear dynamic methods can be used to analyze the gait in horses. Local stability of horse gait remains unchanged after the administration of acepromazine.
... The dynamics of the human gait has been studied in order to establish its chaotic behavior based on simple nonlinear time-series analysis methods [1]. This analysis is performed in order to study early diagnose common gait pathologies through unconstrained slow, normal, and fast paces [2,3]. ...
... The lateral time-series are responsible for 38-40% of gait cycle (swing) [2], which is shown in Fig. 1. This time-series was obtained from experimental data from an accelerometer unit placed at a person waist in order to obtain the Center of Mass (CoM) and therefore, capture the human gait system dynamic behavior. ...
... In order to compute the Lyapunov exponent, two points are considered within the phase plane: and , such as [2]: (1) In this manner, is obtained from expression: (2) Chaotic Analysis on Human Gait Time-Series Signals D. Gomez Rivera, A. D. Palomino Merino, M. A D. Vargas Treviño, G. Etcheverry If any of the Lyapunov exponents is positive, the system is said chaotic. This means that any pair of neighbor points within initial state separate abruptly and the system is sensitive to initial conditions, which is one of the main aspects of a chaotic system. ...
Human Gait analysis is an important subject given its application to the study of pathologies of the human locomotor system. The study of the chaotic behavior of this complex system can help to understand in deep the variability of the human gait patterns. This work explains how to develop an acquisition and analysis tool in a twofold manner: first, a simple and practical setup is implemented in order to achieve the measurement of a
person Center of Mass (CoM) when walking; second, an improved method for estimating Lyapunov exponents is described in order to analyze the recorded time-series chaotic behavior.
... During the past decades, the development of gait science has produced new terminology and ideas relating to gait analysis. Within the domain of human movement science, the complexity of human walking has attracted many researchers [201][202][203][204][205][206][207][208][209][210][211][212] . The recognition that physiological time-series contains valuable hidden information has stimulated research in nonlinear dynamics—chaos, fractals and nonlinear time-series analysis. ...
... For evaluation and interpretation of gait data, different approaches e.g., fractal analysis and entropy calculations have been employed [204, 205, 213] . But few researchers evaluated chaotic invariants for human gait [201, 203, 210] . We calculated chaotic invariants for different walking gaits. ...
... We analyzed fluctuations in the locomotion gaits to help comprehend the neural control of walking. During the last two decades, many studies showed that the dynamics of these stride-to-stride fluctuations contain valuable informa- tion [180, 201, 203, 228] . The development of techniques of nonlinear time-series analysis has opened new vistas for understanding these fluctuations (variations). ...
... The gait analysis for human identification is beneficial as it helps to remotely recognize people [1]. Within the domain of human movement science, the complexity of human walking has attracted many researchers [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]. ...
... For evaluation and interpretation of gait data, different approaches e.g., fractal analysis and entropy calculations have been employed [5] [6] [13] [14]. But few researchers evaluated chaotic invariants for human gait [2] [4] [11]. We calculated chaotic invariants for different walking gaits. ...
Human locomotion is a complex nonlinear process, which researchers have started to analyze using nonlinear dynamics analysis. This study investigates and quantifies the chaotic dynamics of walking gaits using different tools of nonlinear time-series analysis. The application of nonlinear dynamics techniques to gait cycle sequences may unveil the complexity of the human walking. For the walking gaits, calculated Lyapunov exponents and fractal dimensions suggested chaotic dynamics, which are intrinsic to human locomotor system. Such studies may open new avenues for research in gait pathologies and highlight the application of chaotic time-series analysis for examining physiological data.
... During the last two decades, many studies showed that the dynamics of these stride-to-stride fluctuations contain valuable information [4] [5] [6] [7]. The development of techniques of nonlinear time-series analysis has opened new vistas for understanding these fluctuations (variations). ...
... All the gaits exhibited chaotic dynamics since they showed positive Lyapunov exponents and fractal dimensions. It has also been demonstrated that fluctuations in the gait cycle duration (the stride-interval) of healthy young subjects exhibited fractal dimensions and positive Lyapunov exponents [4] [5] [6]. Deterministic chaos seems to be intrinsic to human locomotor system. ...
In this paper, we will study dynamics of an important physiological control system—human gait in disease and aging. The investigation of fluctuations overlying periodic motion in human walking may provide valuable information about neuromuscular system generating both normal and pathological walking patterns. Using nonlinear dynamics analysis, we analyzed walking gaits of the young, elderly and aged subjects with Parkinson's disease. This inquiry demonstrates that nonlinear time-series analysis methods based on time-delay embedding may provide useful insight into the neuromuscular control of human locomotion.
... During the last two decades, many studies showed that the dynamics of these stride-to-stride fluctuations contain valuable information [4] [5] [6] [7]. The development of techniques of nonlinear time-series analysis has opened new vistas for understanding these fluctuations (variations). ...
... All the gaits exhibited chaotic dynamics since they showed positive Lyapunov exponents and fractal dimensions. It has also been demonstrated that fluctuations in the gait cycle duration (the stride-interval) of healthy young subjects exhibited fractal dimensions and positive Lyapunov exponents [4] [5] [6]. Deterministic chaos seems to be intrinsic to human locomotor system. ...
In this paper, we will study dynamics of an important physiological control system—human gait in disease and aging. The investigation of fluctuations overlying periodic motion in human walking may provide valuable information about neuromuscular system generating both normal and pathological walking patterns. Using nonlinear dynamics analysis, we analyzed walking gaits of the young, elderly and aged subjects with Parkinson's disease. This inquiry demonstrates that nonlinear time-series analysis methods based on time-delay embedding may provide useful insight into the neuromuscular control of human locomotion.
Background:
Gait can be affected by diseases such as Parkinson's disease (PD), which lead to alterations like shuffle gait or loss of balance. PD diagnosis is based on subjective measures to generate a score using the Unified Parkinson's Disease Rating Scale (UPDRS). To improve clinical assessment accuracy, gait analysis can utilise linear and nonlinear methods. A nonlinear method called the Lyapunov exponent (LE) is being used to identify chaos in dynamic systems. This article presents an application of LE for diagnosing PD.
Objective:
The objectives were to use the largest Lyapunov exponents (LaLyEx), sample entropy (SampEn) and root mean square (RMS) to assess the gait of subjects diagnosed with PD; to verify the applicability of these parameters to distinguish between people with PD and healthy controls (CO); and to differentiate subjects within the PD group according to the UPDRS assessment.
Methods:
The subjects were divided into the CO group (n= 12) and the PD group (n= 14). The PD group was also divided according to the UPDRS score: UPDRS 0 (n= 7) and UPDRS 1 (n= 7). Kinematic data of lower limbs were measured using inertial measurement units (IMU) and nonlinear parameters (LaLyEx, SampEn and RMS) were calculated.
Results:
There were significant differences between the CO and PD groups for RMS, SampEn and the LaLyEx. After dividing the PD group according to the UPDRS score, there were significant differences in LaLyEx and RMS.
Conclusions:
The selected parameters can be used to distinguish people with PD from CO subjects, and separate people with PD according to the UPDRS score.
The investigation of traffic properties of modern networks requires new approaches, the use of adequate types of distributions of traffic components, and measurement errors should be also taken into account. The models of the request flow are approximated by different distributions with “light tails” (Gaussian, Poisson distributions) as well as “heavy tails” (Pareto, Weibull, log-normal distributions). Self-similar traffic models are widely used to describe traffic in packet-switched networks. The degree of self-similarity of traffic can be determined by various methods, one of them is the estimation of the Hurst index. In the paper, new approaches in simulation of self-similar traffic and theoretical estimation of Hurst index with measurement errors are studied, the statistical simulation of main needed distributions with heavy tails is also considered.
Gait, balance, and coordination are important in the development of chronic disease, but the ability to accurately assess these in the daily lives of patients may be limited by traditional biased assessment tools. Wearable sensors offer the possibility of minimizing the main limitations of traditional assessment tools by generating quantitative data on a regular basis, which can greatly improve the home monitoring of patients. However, these commercial sensors must be validated in this context with rigorous validation methods. This scoping review summarizes the state-of-the-art between 2010 and 2020 in terms of the use of commercial wearable devices for gait monitoring in patients. For this specific period, 10 databases were searched and 564 records were retrieved from the associated search. This scoping review included 70 studies investigating one or more wearable sensors used to automatically track patient gait in the field. The majority of studies (95%) utilized accelerometers either by itself (N = 17 of 70) or embedded into a device (N = 57 of 70) and/or gyroscopes (51%) to automatically monitor gait via wearable sensors. All of the studies (N = 70) used one or more validation methods in which “ground truth” data were reported. Regarding the validation of wearable sensors, studies using machine learning have become more numerous since 2010, at 17% of included studies. This scoping review highlights the current state of the ability of commercial sensors to enhance traditional methods of gait assessment by passively monitoring gait in daily life, over long periods of time, and with minimal user interaction. Considering our review of the last 10 years in this field, machine learning approaches are algorithms to be considered for the future. These are in fact data-based approaches which, as long as the data collected are numerous, annotated, and representative, allow for the training of an effective model. In this context, commercial wearable sensors allowing for increased data collection and good patient adherence through efforts of miniaturization, energy consumption, and comfort will contribute to its future success.
