The purpose of this study was to improve the individual starting technique of eight top junior sprinters using biomechanical feedback training. Three technical parameters (blocks spacing to the start line, knee angle of the front leg and proportion of body mass falling on the hands) were varied. The horizontal velocity at take-off and the time to 10 m were the criteria used to evaluate the effect of the technical changes. All of the parameters were measured simultaneously and the athletes were provided with feedback immediately after each attempt. Seven of eight sprinters showed a statistically significant improvement in starting performance after modifying the position of their blocks. The improvement in 10-m time and horizontal velocity at take-off did not correlate significantly. The power exerted during the starting action correlated significantly with the time to 10 m. Therefore, it was concluded that effective biomechanical feedback during the training of the sprint start should use power exerted as the principal criterion because horizontal velocity shows an intra-individual optimal trend in improvement.

There are many studies on the biomechanics of the long jump, but few researchers have investigated how the athlete has to perform the last strides in order to prepare for takeoff. In this investigation, a pattern recognition approach was applied to analyze the movement structure during the last strides of the approach run and the jump. Time-continuous kinematic data of 57 trials (4.45-6.84 m) was analyzed. Cluster analysis identified at coarse level different movement patterns for each flight and support phase. Above these structural differences, individual movement patterns were diagnosed, especially for the jump. Further, the contribution of single variables on the differences of the complex movement patterns was determined by discriminant analysis. Based on the results, conclusions were drawn concerning the long jump and individuality in training. Overall, the applied pattern recognition method allows for the identification of structural changes of movement patterns as well as individual movement styles. This offers a wide range of application in various areas like sports training and rehabilitation.

Abstracthis paper describes the results obtained from recording, processing and classification of words in spoken Spanish by means of analysis of subvocal speech signals. The processed database has six words (forward, backward, right, left, start and stop), In this article, the signals are sensed with surface electrodes (placed on the surface of the throat) and acquired at a sampling frequency of 50 kHz. The signal conditioning consists of a couple of steps, namely the location of area of interest, using energy analysis; and a filtering stage, using Discrete Wavelet Transform. Finally, feature extraction is achieved in the time-frequency domain using Wavelet Packet and statistical techniques for windowing. Classification is carried out with a back propagation neural network whose training is performed with 70% of the database obtained. The correct classification rate was 75%±2.Keywords: Electromyography, subvocal speech, Wavelet, neuronal network. Los autores declaran que no tienen conflicto de interés. TResumen n este trabajo, se describen los resultados obtenidos a partir de los registros, procesamiento y clasificación de las palabras en el idioma español a través del análisis de las señales de voz subvocal. La base de datos procesada tiene seis palabras (adelante, atrás, derecha, izquierda, arranque y parar), en este artículo, las señales fueron detectadas con electrodos superficiales colocados sobre la superficie de la garganta y adquiridas con una frecuencia de muestreo de 50 kHz. El acondicionamiento de la señal consiste en: la ubicación del área de interés mediante el análisis de la energía, y la filtración usando Transformada Discreta Wavelet. Por último, la extracción de características se realiza en el dominio de tiempofrecuencia utilizando Wavelet Packet y técnicas estadísticas para ventanas. La clasificación se llevó a cabo con una red neuronal de retropropagación cuya formación se llevó a cabo con 70% de la base de datos obtenida. La tasa de clasificación correcta fue de 75% ± 2.Palabras clave: Electromiografía, habla subvocal, Wavelet, red neuronal.

Introduction Control of triatomine infestation is a key strategy for the prevention of Chagas disease (CD). To promote this strategy, it is important to know which antecedents to behavioral change are the best to emphasize when promoting prevention. Objective The aim of this study was to determine predictors for intention to prevent home infestation based on the Health Belief Model (HBM), a commonly used health intervention planning theory. Materials & methods A cross-sectional study was conducted with 112 heads of household in six communities with endemic and high rates of triatomine infestation in Loja province, Ecuador. The data was collected by a questionnaire including perceived severity, susceptibility, benefits to action, barriers to action, and self-efficacy. These data were also used to predict actual infestation of homes. Results Community members reported strong intentions to prevent home infestation. HBM constructs predicted about 14% of the observed variance in intentions. Perceived susceptibility and severity did not predict behavioral intention well; perceived barriers to small-scale action that reduce likelihood of infestation and self-efficacy in participating in surveillance systems