Time-motion analysis on Chinese male field hockey players

The aim of the study was to evaluate the match work-rate of Chinese field hockey players by analyzing the distance covered at different intensities pooled by specific positions during different periods of matches. Thirty-eight players from twenty-four male field hockey matches at the 11th Chinese National Games were filmed and analyzed.

Time-motion analysis on Chinese male field hockey players

The aim of the study was to evaluate the match work-rate of Chinese field hockey players by analyzing the distance covered at different intensities pooled by specific positions during different periods of matches. Thirty-eight players from twenty-four male field hockey matches at the 11th Chinese National Games were filmed and analyzed.mas

Time-motion analysis in women’s team handball: importance of aerobic performance

Women’s handball is a sport, which has seen an accelerated development over the last decade. Data on movement patterns in combination with physiological demands are nearly nonexistent in the literature. The aim of this study was twofold: first, to analyze the horizontal movement pattern, including the sprint acceleration profiles, of individual female elite handball players and the corresponding heart rates (HRs) during a match and secondly to determine underlying correlations with individual aerobic performance. Players from one German First League team (n = 11) and the Norwegian National Team (n = 14) were studied during one match using the Sagit system for movement analysis and Polar HR monitoring for analysis of physiological demands. Mean HR during the match was 86 % of maximum HR (HRmax). With the exception of the goalkeepers (GKs, 78 % of HRmax), no position-specific differences could be detected. Total distance covered during the match was 4614 m (2066 m in GKs and 5251 m in field players (FPs)). Total distance consisted of 9.2 % sprinting, 26.7 % fast running, 28.8 % slow running, and 35.5 % walking. Mean velocity varied between 1.9 km/h (0.52 m/s) (GKs) and 4.2 km/h (1.17 m/s) (FPs, no position effect). Field players with a higher level of maximum oxygen uptake (V̇O2max) executed run activities with a higher velocity but comparable percentage of HRmax as compared to players with lower aerobic performance, independent of FP position. Acceleration profile depended on aerobic performance and the field player’s position. In conclusion, a high V̇O2max appears to be important in top-level international women’s handball. Sprint and endurance training should be conducted according to the specific demands of the player’s position.

Time motion analysis of 2001 and 2002 super 12 rugby

The aim of this study was to quantify movements of Super 12 rugby players in competition because information on elite rugby players' movements is unavailable. Players were categorized into forwards [front (n = 16) and back row (n = 15)] and backs [inside (n = 9) and outside backs (n = 7)] and their movements analysed by video-based time motion analysis. Movements were classified as rest (standing, walking and jogging) and work (striding, sprinting, static exertion, jumping, lifting or tackling). The total time, number and duration of individual activities were assessed, with differences between groups evaluated using independent sample t-tests (unequal variances), while differences between halves were assessed with paired sample t-tests. Forwards had 7:47 min:s (95% confidence limits: 6:39 to 8:55 min:s, P

Time-frequency analysis of human motion during rhythmic exercises

Biomechanical signals due to human movements during exercise are represented in time-frequency domain using Wigner Distribution Function (WDF). Analysis based on WDF reveals instantaneous spectral and power changes during a rhythmic exercise. Investigations were carried out on 11 healthy subjects who performed 5 cycles of sun salutation, with a body-mounted Inertial Measurement Unit (IMU) as a motion sensor. Variance of Instantaneous Frequency (I.F) and Instantaneous Power (I.P) for performance analysis of the subject is estimated using one-way ANOVA model. Results reveal that joint Time-Frequency analysis of biomechanical signals during motion facilitates a better understanding of grace and consistency during rhythmic exercise.

Real-Time Bayesian Analysis of Ground Motion Envelopes for Earthquake Early Warning

Current earthquake early warning systems usually make magnitude and location predictions and send out a warning to the users based on those predictions. We describe an algorithm that assesses the validity of the predictions in real-time. Our algorithm monitors the envelopes of horizontal and vertical acceleration, velocity, and displacement. We compare the observed envelopes with the ones predicted by Cua & Heaton's envelope ground motion prediction equations (Cua 2005). We define a "test function" as the logarithm of the ratio between observed and predicted envelopes at every second in real-time. Once the envelopes deviate beyond an acceptable threshold, we declare a misfit. Kurtosis and skewness of a time evolving test function are used to rapidly identify a misfit. Real-time kurtosis and skewness calculations are also inputs to both probabilistic (Logistic Regression and Bayesian Logistic Regression) and nonprobabilistic (Least Squares and Linear Discriminant Analysis) models that ultimately decide if there is an unacceptable level of misfit. This algorithm is designed to work at a wide range of amplitude scales. When tested with synthetic and actual seismic signals from past events, it works for both small and large events.

A survey of human motion analysis using depth imagery

Analysis of human behaviour through visual information has been a highly active research topic in the computer vision community. This was previously achieved via images from a conventional camera, but recently depth sensors have made a new type of data available. This survey starts by explaining the advantages of depth imagery, then describes the new sensors that are available to obtain it. In particular, the Microsoft Kinect has made high-resolution real-time depth cheaply available. The main published research on the use of depth imagery for analysing human activity is reviewed. Much of the existing work focuses on body part detection and pose estimation. A growing research area addresses the recognition of human actions. The publicly available datasets that include depth imagery are listed, as are the software libraries that can acquire it from a sensor. This survey concludes by summarising the current state of work on this topic, and pointing out promising future research directions.

Time series analysis for minority game simulations of financial markets

The minority game (MG) model introduced recently provides promising insights into the understanding of the evolution of prices, indices and rates in the financial markets. In this paper we perform a time series analysis of the model employing tools from statistics, dynamical systems theory and stochastic processes. Using benchmark systems and a financial index for comparison, several conclusions are obtained about the generating mechanism for this kind of evolution. The motion is deterministic, driven by occasional random external perturbation. When the interval between two successive perturbations is sufficiently large, one can find low dimensional chaos in this regime. However, the full motion of the MG model is found to be similar to that of the first differences of the SP500 index: stochastic, nonlinear and (unit root) stationary. (C) 2002 Elsevier B.V. B.V. All rights reserved.

A comparison of time-motion performance between age groups in judo matches

The aim of this study was to compare time-motion indicators during judo matches performed by athletes from different age groups. The following age groups were analysed: Pre-Juvenile (13-14 years, n=522), Juvenile (15-16 years, n 353); Junior (19 years, n = 349) and Senior (>20 years, n = 587). The time-motion indicators included: Total Combat Time, Standing Combat Time, Displacement Without Contact, Gripping Time, Groundwork Combat Time and Pause Time. Analysis of variance (ANOVA) one-way and the Tukey test, as well as the Kruskal-Wallis test and Mann-Whitney (for non-parametric data), were conducted, using P < 0.05 as significance level. The results showed that all analysed groups obtained a median of 7 (first quantile - 3, third quantile - 12) sequences of combat/pause cycles. In total time of combat, the result was: for Total Combat Time, Standing Combat Time and Gripping Time: Pre-Juvenile and Senior were significantly longer than Juvenile and Junior. Considering Displacement Without Contact, Junior was significantly longer than all other age groups. For Groundwork Combat Time, Senior was significantly longer than all other age groups and Pre-Juvenile was longer than Junior. These results can be used to improve the physiological performance in intermittent practices, as well as technicaltactical training during judo sessions.

Non-Markovian stochastic processes and their applications: from anomalous diffusion to time series analysis

This work provides a forward step in the study and comprehension of the relationships between stochastic processes and a certain class of integral-partial differential equation, which can be used in order to model anomalous diffusion and transport in statistical physics. In the first part, we brought the reader through the fundamental notions of probability and stochastic processes, stochastic integration and stochastic differential equations as well. In particular, within the study of H-sssi processes, we focused on fractional Brownian motion (fBm) and its discrete-time increment process, the fractional Gaussian noise (fGn), which provide examples of non-Markovian Gaussian processes. The fGn, together with stationary FARIMA processes, is widely used in the modeling and estimation of long-memory, or long-range dependence (LRD). Time series manifesting long-range dependence, are often observed in nature especially in physics, meteorology, climatology, but also in hydrology, geophysics, economy and many others. We deepely studied LRD, giving many real data examples, providing statistical analysis and introducing parametric methods of estimation. Then, we introduced the theory of fractional integrals and derivatives, which indeed turns out to be very appropriate for studying and modeling systems with long-memory properties. After having introduced the basics concepts, we provided many examples and applications. For instance, we investigated the relaxation equation with distributed order time-fractional derivatives, which describes models characterized by a strong memory component and can be used to model relaxation in complex systems, which deviates from the classical exponential Debye pattern. Then, we focused in the study of generalizations of the standard diffusion equation, by passing through the preliminary study of the fractional forward drift equation. Such generalizations have been obtained by using fractional integrals and derivatives of distributed orders. In order to find a connection between the anomalous diffusion described by these equations and the long-range dependence, we introduced and studied the generalized grey Brownian motion (ggBm), which is actually a parametric class of H-sssi processes, which have indeed marginal probability density function evolving in time according to a partial integro-differential equation of fractional type. The ggBm is of course Non-Markovian. All around the work, we have remarked many times that, starting from a master equation of a probability density function f(x,t), it is always possible to define an equivalence class of stochastic processes with the same marginal density function f(x,t). All these processes provide suitable stochastic models for the starting equation. Studying the ggBm, we just focused on a subclass made up of processes with stationary increments. The ggBm has been defined canonically in the so called grey noise space. However, we have been able to provide a characterization notwithstanding the underline probability space. We also pointed out that that the generalized grey Brownian motion is a direct generalization of a Gaussian process and in particular it generalizes Brownain motion and fractional Brownain motion as well. Finally, we introduced and analyzed a more general class of diffusion type equations related to certain non-Markovian stochastic processes. We started from the forward drift equation, which have been made non-local in time by the introduction of a suitable chosen memory kernel K(t). The resulting non-Markovian equation has been interpreted in a natural way as the evolution equation of the marginal density function of a random time process l(t). We then consider the subordinated process Y(t)=X(l(t)) where X(t) is a Markovian diffusion. The corresponding time-evolution of the marginal density function of Y(t) is governed by a non-Markovian Fokker-Planck equation which involves the same memory kernel K(t). We developed several applications and derived the exact solutions. Moreover, we considered different stochastic models for the given equations, providing path simulations.

Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features

Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach.

Real time motion estimation using a neural architecture implemented on GPUs

This work describes a neural network based architecture that represents and estimates object motion in videos. This architecture addresses multiple computer vision tasks such as image segmentation, object representation or characterization, motion analysis and tracking. The use of a neural network architecture allows for the simultaneous estimation of global and local motion and the representation of deformable objects. This architecture also avoids the problem of finding corresponding features while tracking moving objects. Due to the parallel nature of neural networks, the architecture has been implemented on GPUs that allows the system to meet a set of requirements such as: time constraints management, robustness, high processing speed and re-configurability. Experiments are presented that demonstrate the validity of our architecture to solve problems of mobile agents tracking and motion analysis.

Motion analysis of right ventricular wall based on an electromechanical biventricular model

Based on our previously developed electrical heart model, an electromechanical biventricular model, which couples the electrical property and mechanical property of the heart, was constructed and the right ventricular wall motion and deformation was simulated using this model. The model was developed on the basis of composite material theory and finite element method. The excitation propagation was simulated by electrical heart model, and the resultant active forces were used to study the ventricular wall motion during systole. The simulation results show that: (1) The right ventricular free wall moves towards the septum, and at the same time, the base and middle of free wall move towards the apex, which reduce the volume of right ventricle; (2) The minimum principle strain (E3) is largest at the apex, then at the middle of free wall, and its direction is in the approximate direction of epicardial muscle fibers. These results are in good accordance with solutions obtained from MR tagging images. It suggests that such electromechanical biventricular model can be used to assess the mechanical function of two ventricles.