This paper presents a dataset of high-density surface EMG signals (HD-sEMG) designed to study patterns of sEMG spatial distribution over upper limb muscles during voluntary isometric contractions. Twelve healthy subjects performed four different isometric tasks at different effort levels associated with movements of the forearm. Three 2-D electrode arrays were used for recording the myoelectric activity from five upper limb muscles: biceps brachii, triceps brachii, anconeus, brachioradialis, and pronator teres. Technical validation comprised a signals quality assessment from outlier detection algorithms based on supervised and non-supervised classification methods. About 6% of the total number of signals were identified as “bad” channels demonstrating the high quality of the recordings. In addition, spatial and intensity features of HD-sEMG maps for identification of effort type and level, have been formulated in the framework of this database, demonstrating better performance than the traditional time-domain features. The presented database can be used for pattern recognition and MUAP identification among other uses.

This paper presents a new cardiorespiratory model focused on the prediction of the healthy human response under rest and aerobic dynamic exercise conditions. Simulation results of cardiorespiratory variables are compared against experimental data and two of the most current and complete cardiorespiratory models.

Respiratory system modeling has been extensively studied in steady-state conditions to simulate sleep disorders, to predict its behavior under ventilatory diseases or stimuli and to simulate its interaction with mechanical ventilation. Nevertheless, the studies focused on the instantaneous response are limited, which restricts its application in clinical practice. The aim of this study is double: firstly, to analyze both dynamic and static responses of two known respiratory models under exercise stimuli by using an incremental exercise stimulus sequence (to analyze the model responses when step inputs are applied) and experimental data (to assess prediction capability of each model). Secondly, to propose changes in the models' structures to improve their transient and stationary responses. The versatility of the resulting model vs. the other two is shown according to the ability to simulate ventilatory stimuli, like exercise, with a proper regulation of the arterial blood gases, suitable constant times and a better adjustment to experimental data. The proposed model adjusts the breathing pattern every respiratory cycle using an optimization criterion based on minimization of work of breathing through regulation of respiratory frequency.

The respiratory control system is one of the most complex physiological systems, whose controller has been extensively modeled based on the criterion of minimizing work of breathing (WOB). However, cost functions and their related parameters are still an open topic. The aim of this paper is to evaluate different estimates of WOB for given ventilatory demands. Two known computations of mechanical work are compared with regard to an estimate proposed in this paper. Appropriate values for these parameters were found based on the best fitting of the breathing pattern obtained from experimental data during increased hypercapnia. The comparison among them was carried out from their breathing pattern predictability and the physiological viewpoint. For this purpose, two nested optimization processes were performed: minimization of prediction error, with regard to the experimental response, in order to obtain the best estimating parameters; identification of a breathing pattern that minimizes the mechanical WOB. The former was performed using Covariance Matrix Adaptation Evolution Strategy and the latter using sequential quadratic programming. Results show that the estimate proposed in this paper is less sensitive to their parameters and reaches a significantly lower prediction error of breathing pattern during increased ventilatory effort and a better interpretation from a physiological viewpoint.

Applying complex mathematical models of physiological systems is challenging due to the large number of parameters. Identifying these parameters through experimentation is difficult, and although procedures for fitting and validating models are reported, no integrated strategy exists. Additionally, the complexity of optimization is generally neglected when the number of experimental observations is restricted, obtaining multiple solutions or results without physiological justification. This work proposes a fitting and validation strategy for physiological models with many parameters under various populations, stimuli, and experimental conditions. A cardiorespiratory system model is used as a case study, and the strategy, model, computational implementation, and data analysis are described. Using optimized parameter values, model simulations are compared to those obtained using nominal values, with experimental data as a reference. Overall, a reduction in prediction error is achieved compared to that reported for model building. Furthermore, the behavior and accuracy of all the predictions in the steady state were improved. The results validate the fitted model and provide evidence of the proposed strategy’s usefulness.