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.

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.

This paper shows the design of a wearable device for measuring vital signs, oriented to monitoring applications and home health care. In order to improve the living conditions of the patient, device allows that the patient can perform their daily activities while their health is monitored. The designed device allows measuring electrocardiogram, blood oxygen saturation, non-invasive blood pressure and heart rate. This paper proposes a novel technique to reduce motion artifacts based on signals measurement redundantly, also the importance at clinic level of measuring these variables is shown. The device transmits the information wirelessly to a proprietary application for viewing, the result of used technique for reducing artifacts is shown and a prototype of the device is presented. Finally, the advantages and future improvements of the wearable monitor are discussed.

The subject of respiratory mechanics has complex characteristics, functions, and interactions that can be difficult to understand in training and medical education contexts. As such, education strategies based on computational simulations comprise useful tools, but their application in the medical area requires stricter validation processes. This paper shows a statistical and a Delphi validation for two modules of a web application used for respiratory system learning: (I) “Anatomy and Physiology” and (II) “Work of Breathing Indexes”. For statistical validation, population and individual analyses were made using a database of healthy men to compare experimental and model-predicted data. For both modules, the predicted values followed the trend marked by the experimental data in the population analysis, while in the individual analysis, the predicted errors were 9.54% and 25.38% for maximal tidal volume and airflow, respectively, and 6.55%, 9.33%, and 11.77% for rapid shallow breathing index, work of breathing, and maximal inspiratory pressure, respectively. For the Delphi validation, an average higher than 4 was obtained after health professionals evaluated both modules from 1 to 5. In conclusion, both modules are good tools for respiratory system learning processes. The studied parameters behaved consistently with the expressions that describe ventilatory dynamics and were correlated with experimental data; furthermore, they had great acceptance by specialists.

There is a death risk that increases with the required time to transport a critical patient to a hospital by ambulance. Effectiveness of medical attention and therefore patient's safety will strongly depend on the quickness to determine appropriate actions to be performed when patient arrives to the hospital. It is evident the need of establishing a communication link between monitoring equipment inside the ambulance and the healthcare center. This paper presents a system that automatically integrates, organizes, records and wirelessly transmits patient's vital signs in real-time. Physicians and specialists could use this information to make a pre-diagnosis of patient's current condition, anticipate possible complications and medical treatment planning before his arrival to the care center.

The use of mathematical models of physiological systems in medicine has allowed the development of diagnostic, treatment, and medical educational tools, but their application for predictive, preventive, and personalized purposes is restricted by their complexity. Although there are strategies that reduce the complexity of applying models by fitting techniques, they focus on a single instant of time, neglecting the effect of the system's temporal evolution. The aim of this work is to propose a dynamic fitting strategy of physiological models with large number of parameters and a constrained amount of experimental data, focused on obtaining better predictions based on the system's temporal trend and useful to predict future states. It was applied in a cardiorespiratory model as a case study. Experimental data from a longitudinal study of healthy adult subjects under aerobic exercise were used for fitting and validation. The model predictions obtained at steady-state using the proposed strategy and the nominal values of the parameters were compared. The best results corresponded mostly to the proposed strategy, mainly regarding the overall prediction error. The results evidenced the usefulness of the dynamic fitting strategy, highlighting its use for predictive, preventive, and personalized applications.