Endurance markers are related with local neuromuscular response for the intact but not for the ACL reconstructed leg during high intensity running

AIM: It has been demonstrated that the local neuromuscular response during high intensity exercise has a strong relationship with endurance markers. However, a diminished neuromuscular response has been reported for the operated leg in athletes having undergone anterior cruciate ligament reconstruction (ACLR). The purpose of the present study was to examine the relationships between endurance markers and the EMG response during high intensity running in ACLR athletes. METHODS: Fourteen ACLR soccer players underwent a GXT test to volitional exhaustion and a 10-min bout of high intensity running. During the 10-min bout, EMG data were recorded at the 3rd and 10th minute from the vastus lateralis bilaterally using a telemetric system. The final EMG levels were expressed as a percentage of the initial values. Pearson moment product correlations were used to assess the relationship between the endurance markers of VO2max, velocity at lactate threshold (vLT), velocity at 4mM (V4) and the final EMG levels. RESULTS: Final EMG levels for the intact leg had a very strong relationship with vLT (r=0.77, P=0.001) and a strong relationship with V4 (r=0.68, P=0.008). Final EMG levels for the reconstructed leg had moderate relationship with vLT (r=0.47, P=0.09) and V4 (r=0.52, P=0.06). CONCLUSION: The neuromuscular response of the intact leg during high intensity running shows strong to very strong relationships with endurance markers. Failure of the ACLR leg to present relationships of similar strength may indicate that chronic perturbations modify the ability of the local muscular environment to tolerate sustained high intensity efforts

Graph Analysis and Visualization for Brain Function Characterization Using EEG Data

Over the past few years, there has been an increased interest in studying the underlying neural mechanism of cognitive brain activity as well as in diagnosing certain pathologies. Noninvasive imaging modalities such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and dynamic signal acquisition techniques such as quantitative electroencephalography (EEG) have been vastly used to estimate cortical connectivity and identify functional interdependencies among synchronized brain lobes. In this area, graph-theoretic concepts and tools are used to describe large scale brain networks while performing cognitive tasks or to characterize certain neuropathologies. Such tools can be of particular value in basic neuroscience and can be potential candidates for future inclusion in a clinical setting. This paper discusses the application of the high time resolution EEG to resolve interdependence patterns using both linear and nonlinear techniques. The network formed by the statistical dependencies between the activations of distinct and often well separated neuronal populations is further analyzed using a number of graph theoretic measures capable of capturing and quantifying its structure and summarizing the information that it contains. Finally, graph visualization reveals the hidden structure of the networks and amplifies human understanding. A number of possible applications of the graph theoretic approach are also listed. A freely available standalone brain visualization tool to benefit the healthcare engineering community is also provided (http://www.ics.forth.gr/bmi/tools.html)

Linear dynamical models in speech synthesis

Summarization: Hidden Markov models (HMMs) are becoming the dominant approach for text-to-speech synthesis (TTS). HMMs provide an attractive acoustic modeling scheme which has been exhaustively investigated and developed for many years. Modern HMM-based speech synthesizers have approached the quality of the best state-of-the-art unit selection systems. However, we believe that statistical parametric speech synthesis has not reached its potential, since HMMs are limited by several assumptions which do not apply to the properties of speech. We, therefore, propose in this paper to use Lin-ear Dynamical Models (LDMs) instead of HMMs. LDMs can better model the dynamics of speech and can produce a naturally smoother trajectory of the synthesized speech. We perform a series of experiments using different system configurations to check on the performance of LDMs for speech synthesis. We show that LDM-based synthesizers can outperform HMM-based ones in terms of cepstral distance and are a very promising acoustic modeling alternative for statistical parametric TTS.Παρουσιάστηκε στο: IEEE International Conference on Acoustic, Speech and Signal Processin

Global variance in speech synthesis with linear dynamical models

Summarization: Linear Dynamical Models (LDMs) have been used in speech synthesis recently as an alternative to hidden Markov models (HMMs). Among the advantages of LDMs are the ability to capture the dynamics of speech and the achievement of synthesized speech quality similar to HMM-based speech systems on a smaller footprint. However, such as in the HMM case, LDMs produce over-smoothed trajectories of speech parameters, resulting in muffled quality of synthetic speech. Inspired by a similar problem found in HMM-based speech synthesis, where the naturalness of the synthesized speech is greatly improved when the global variance (GV) is compensated, this paper proposes a novel speech parameter generation algorithm that considers GV in LDM-based speech synthesis. Experimental results show that the application of GV during parameter generation significantly improves speech quality.Presented on: IEEE Signal Processing Letter

ClockWork-RNN based architectures for slot filling

Summarization: A prevalent and challenging task in spoken language understanding is slot filling. Currently, the best approaches in this domain are based on recurrent neural networks (RNNs). However, in their simplest form, RNNs cannot learn long-term dependencies in the data. In this paper, we propose the use of ClockWork recurrent neural network (CW-RNN) architectures in the slot-filling domain. CW-RNN is a multi-timescale implementation of the simple RNN architecture, which has proven to be powerful since it maintains relatively small model complexity. In addition, CW-RNN exhibits a great ability to model long-term memory inherently. In our experiments on the ATIS benchmark data set, we also evaluate several novel variants of CW-RNN and we find that they significantly outperform simple RNNs and they achieve results among the state-of-the-art, while retaining smaller complexity.Παρουσιάστηκε στο: 18th Annual Conference of the International Speech Communication Associatio