Browsing by Author "Weinstein, Alejandro"

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    Understanding the effect of window length and overlap for assessing sEMG in dynamic fatiguing contractions: A non-linear dimensionality reduction and clustering
    (2021) De la Fuente, Carlos; Martinez-Valdes, Eduardo; Ignacio Priego-Quesada, Jose; Weinstein, Alejandro; Valencia, Oscar; Kunzler, Marcos R.; Alvarez-Ruf, Joel; Carpes, Felipe P.
    The Short-Time Fourier transform (STFT) is a helpful tool to identify muscle fatigue with clinical and sports applications. However, the choice of STFT parameters may affect the estimation of myoelectrical manifestations of fatigue. Here, we determine the effect of window length and overlap selections on the frequency slope and the coefficient of variation from EMG spectrum features in fatiguing contractions. We also determine whether STFT parameters affect the relationship between frequency slopes and task failure. Eighty-eight healthy adult men performed one-leg heel-rise until exhaustion. A factorial design with a window length of 50, 100, 250, 500, and 1000 ms with 0, 25, 50, 75, and 90% of overlap was used. The frequency slope was non-linearly fitted as a task failure function, followed by a dimensionality reduction and clustering analysis. The STFT parameters elicited five patterns. A small window length produced a higher slope frequency for the peak frequency (p < 0.001). The contrary was found for the mean and median frequency (p < 0.001). A larger window length elicited a higher slope frequency for the mean and peak frequencies. The largest frequency slope and dispersion was found for a window length of 50 ms without overlap using peak frequency. A combination of 250 ms with 50% of overlap reduced the dispersion both for peak, median, and mean frequency, but decreased the slope frequency. Therefore, the selection of STFT parameters during dynamic contractions should be accompanied by a mechanical measure of the task failure, and its parameters should be adjusted according to the experiment's requirements.
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    Use of accelerometers for automatic regional chest movement recognition during tidal breathing in healthy subjects
    (2019) De la Fuente, Carlos; Weinstein, Alejandro; Guzman-Venegas, Rodrigo; Arenas, Juan; Cartes, Jorge; Soto, Marcos; Carpes, Felipe P.
    Recognition of breathing patterns helps clinicians to understand acute and chronic adaptations during exercise and pathological conditions. Wearable technologies combined with a proper data analysis provide a low cost option to monitor chest and abdominal wall movements. Here we set out to determine the feasibility of using accelerometry and machine learning to detect chest-abdominal wall movement patterns during tidal breathing. Furthermore, we determined the accelerometer positions included in the clusters, considering principal component domains. Eleven healthy participants (age: 21 +/- 0.2 y, BMI: 23.4 +/- 0.7 kg/m(2), FEV1: 4.1 +/- 0.3 L, VO2: 4.6 +/- 0.2 mL/min kg) were included in this cross-sectional study. Spirometry and ergospirometry assessments were performed with participants seated with 13 accelerometers placed over the thorax. Data collection lasted 10 min. Following signal pre-processing, principal components and clustering analyses were performed. The Euclidean distances in respect to centroids were compared between the clusters (p < 0.05), identifying two clusters (p < 0.001). The first cluster included sensors located at the right and left second rib midline, body of sternum, left fourth rib midline, right and left second thoracic vertebra midline, and fifth thoracic vertebra. The second cluster included sensors at the fourth right rib midline, right and left seventh ribs, abdomen at linea alba, and right and left tenth thoracic vertebra midline. Costal-superior and costal-abdominal patterns were also recognized. We conclude that accelerometers placed on the chest and abdominal wall permit the identification of two clusters of movements regarding respiration biomechanics.