Nuevos métodos de cálculo de la profundidad de las compresiones torácicas a partir de la señal de aceleración.

[EN]These feedback devices are used to improve the quality of chest compressions while performing CPR technique, as they provide real time information to guide the rescuer during resuscitation attempts. Most feedback systems on the market are based on accelerometers and additional sensors or reference signals, used for calculating the displacement of the chest from the acceleration signal. This makes them expensive and complex devices. With the aim of optimizing these feedback systems and overcoming their limitations, in this document we propose three alternative methods for calculating the depth of chest compressions. These methods differ from the ones existing so far in that they use exclusively the chest acceleration signal to compute the displacement. With their implementation, it would be possible to develop systems to provide accurate feedback more easily and economically. In this context, this document details the design and implementation of the three methods and the development of a software environment to analyze the accuracy of each of them and compare the results by means of a detailed calculation of errors. Furthermore, in order to evaluate the methods a database is required, and it can be compiled using a sensorized manikin to record the acceleration signal and the gold standard chest compression depth. The database generated will be used for other studies related to the estimation of the compression depth, because the signals obtained in the manikin platform are very similar to those recorded during a real resuscitation episode.

Estimation of the growth curve parameters in Macrobrachium rosenbergii

Growth is one of the most important characteristics of cultured species. The objective of this study was to determine the fitness of linear, log linear, polynomial, exponential and Logistic functions to the growth curves of Macrobrachium rosenbergii obtained by using weekly records of live weight, total length, head length, claw length, and last segment length from 20 to 192 days of age. The models were evaluated according to the coefficient of determination (R2), and error sum off square (ESS) and helps in formulating breeders in selective breeding programs. Twenty full-sib families consisting 400 PLs each were stocked in 20 different hapas and reared till 8 weeks after which a total of 1200 animals were transferred to earthen ponds and reared up to 192 days. The R2 values of the models ranged from 56 – 96 in case of overall body weight with logistic model being the highest. The R2 value for total length ranged from 62 to 90 with logistic model being the highest. In case of head length, the R2 value ranged between 55 and 95 with logistic model being the highest. The R2 value for claw length ranged from 44 to 94 with logistic model being the highest. For last segment length, R2 value ranged from 55 – 80 with polynomial model being the highest. However, the log linear model registered low ESS value followed by linear model for overall body weight while exponential model showed low ESS value followed by log linear model in case of head length. For total length the low ESS value was given by log linear model followed by logistic model and for claw length exponential model showed low ESS value followed by log linear model. In case of last segment length, linear model showed lowest ESS value followed by log linear model. Since, the model that shows highest R2 value with low ESS value is generally considered as the best fit model. Among the five models tested, logistic model, log linear model and linear models were found to be the best models for overall body weight, total length and head length respectively. For claw length and last segment length, log linear model was found to be the best model. These models can be used to predict growth rates in M. rosenbergii. However, further studies need to be conducted with more growth traits taken into consideration