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Beatriz López-Garrido

Bio: Beatriz López-Garrido is an academic researcher from University of Alcalá. The author has contributed to research in topics: Signal processing & Obstetrics and gynaecology. The author has an hindex of 2, co-authored 3 publications receiving 12 citations.

Papers
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Book ChapterDOI
01 Jan 2015
TL;DR: The suppression of the Discrete Cosine Transform from the feature extraction process is suitable, and the use of the most significative bit instead of the logarithm also supposes a considerable reduction in the computational load while obtaining comparable results in terms of error rate.
Abstract: Hearing aids have to work at low clock rates in order to minimize the power consumption and maximize battery life. The implementation of signal processing techniques on hearing aids is strongly constrained by the small number of instructions per second to implement the algorithms in the digital signal processor the hearing aid is based on. In this respect, the objective of this paper is the proposal of a set of approximations in order to optimize the implementation of standard Mel Frequency Cepstral Coefficient based sound environment classifiers in real hearing aids. After a theoretical analysis of these coefficients and a set of experiments under different classification schemes, we demonstrate that the suppression of the Discrete Cosine Transform from the feature extraction process is suitable, since its use does not suppose an improvement in terms of error rate, and it supposes a high computational load. Furthermore, the use of the most significative bit instead of the logarithm also supposes a considerable reduction in the computational load while obtaining comparable results in terms of error rate.

8 citations

Journal ArticleDOI
TL;DR: Results confirm that acupuncture at the Ren Mai 6 point can decrease the time to placental expulsion and represent a simple, safe, and inexpensive way of decreasing the duration of the third stage of labor.

4 citations

Journal ArticleDOI
TL;DR: A cross-sectional survey carried out to assess and compare knowledge, attitudes, and beliefs of the obstetrician-gynecologists and midwives, regarding a set of complementary and alternative therapies in the area of the Corredor del Henares in Spain shows a high percentage of acceptance.
Abstract: The objective of this article is to present the results from a cross-sectional survey carried out to assess and compare knowledge, attitudes, and beliefs of the obstetrician-gynecologists and midwives, regarding a set of complementary and alternative therapies in the area of the Corredor del Henares in Spain. The results show a high percentage of acceptance regarding complementary and alternative therapies in the field of obstetrics and gynecology, and more than half of the Spanish professionals of reproductive health would like to learn more about these therapies.

2 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: Compared with bed rest, acupuncture might be an effective and acceptable strategy to relieve symptoms of postpartum sciatica.

10 citations

Journal ArticleDOI
TL;DR: Inter-subject prediction accuracy suggested training and testing data to be based on a particular subject or large collection of subjects to improve fatigue prediction capacity.
Abstract: Patients with spinal cord injury (SCI) benefit from muscle training with functional electrical stimulation (FES). For safety reasons and to optimize training outcome, the fatigue state of the target muscle must be monitored. Detection of muscle fatigue from mel frequency cepstral coefficient (MFCC) feature of mechanomyographic (MMG) signal using support vector machine (SVM) classifier is a promising new approach. Five individuals with SCI performed FES cycling exercises for 30 min. MMG signals were recorded on the quadriceps muscle group (rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM)) and categorized into non-fatigued and fatigued muscle contractions for the first and last 10 min of the cycling session. For each subject, a total of 1800 contraction-related MMG signals were used to train the SVM classifier and another 300 signals were used for testing. The average classification accuracy (4-fold) of non-fatigued and fatigued state was 90.7% using MFCC feature, 74.5% using root mean square (RMS), and 88.8% with combined MFCC and RMS features. Inter-subject prediction accuracy suggested training and testing data to be based on a particular subject or large collection of subjects to improve fatigue prediction capacity.

10 citations

Journal ArticleDOI
TL;DR: Results demonstrate the viability of the system, thanks to the low cost that some violence features require, making feasible the implementation of the proposed method in a nowadays low power microprocessor.
Abstract: Violence detection represents an important issue to take into account in the design of intelligent algorithms for smart environments. This paper proposes an energy-efficient system capable of acoustically detecting violence. In our solution, genetic algorithms are used to select the best subset of features with a constrained computational cost. Results demonstrate the viability of the system, thanks to the low cost that some violence features require, making feasible the implementation of the proposed method in a nowadays low power microprocessor.

9 citations

Book ChapterDOI
29 Nov 2016
TL;DR: Results derived from the experiments show that MFCCs are the best features for violence detection, and others like pitch or short time energy have also a good performance, in other words, features that can distinguish between voiced and unvoiced frames seem to be a good election for violence Detection in real environments.
Abstract: Violence continues being an important problem in the society. Thousands of people suffer its effects every day and statistics show this number has maintained or almost increased recently. In the modern environment of smart cities there is a necessity to develop a system capable of detecting if a violent situation is taking place or not. In this paper we present an automatic acoustic violence detection system for smart cities, integrating both signal processing and pattern recognition techniques. The proposed software has been implemented in three steps: feature extraction in time and frequency domain, genetic algorithm implementation in order to select the best features, and classification to take a binary decision. Results derived from the experiments show that MFCCs are the best features for violence detection, and others like pitch or short time energy have also a good performance. In other words, features that can distinguish between voiced and unvoiced frames seem to be a good election for violence detection in real environments.

9 citations