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Mechanical ventilation systems require a device for measuring the air flow provided to a patient in order to monitor and ensure the correct quantity of air proportionated to the patient, this device is the air flow sensor. At the beginning of the COVID-19 pandemic, flow sensors were not available in Peru because of the international supply shortage. In this context, a novel air flow sensor based on an orifice plate and an intelligent transducer was developed to form an integrated device. The proposed design was focused on simple manufacturing requirements for mass production in a developing country. CAD and CAE techniques were used in the design stage, and a mathematical model of the device was proposed and calibrated experimentally for the measured data transduction. The device was tested in its real working conditions and was therefore implemented in a breathing circuit connected to a low-cost mechanical ventilation system. Results indicate that the designed air flow sensor/transducer is a low-cost complete medical device for mechanical ventilators that is able to provide all the ventilation parameters by an equivalent electrical signal to directly display the following factors: air flow, pressure and volume over time. The evaluation of the designed sensor transducer was performed according to sundry transducer parameters such as geometrical parameters, material parameters and adaptive coefficients in the main transduction algorithm; in effect, the variety of the described results were achieved by the faster response time and robustness proportionated by transducers of nanostructures based on Anodic Aluminum Oxide (AAO), which enhanced the designed sensor/transducer (ST) during operation in intricate geographic places, such as the Andes mountains of Peru.

This article mainly analyzes the correlation between the carrier energy in robotic arms, according to enhance its performance. This task is achieved because of the sensors/actuators based on nanostructures properties: short response time and high robustness, which proportionated the possibility to execute intricate instructions by the control subsystem of the robotic arm. Therefore, the instructions executed by the controller are supported by a polynomial design, this algorithm helped to evaluate every response signal as a consequence of the main control system, which was consequently by the short response time from the main sensors “flow (carrier energy) and speed of the robotic arm”, moreover, the advantage of the proposed system is given by the extra time obtained also to verify the stability of the robotic arm based in Lyapunov models correlated with Lagrange, as well as every equations were solved and organized by neural network.