This work proposes adapting an existing sensor and embedding it on mannequins used in cardiopulmonary resuscitation (CPR) training to accurately measure the amount of air supplied to the lungs during ventilation. Mathematical modeling, calibration, and validation of the sensor along with metrology, statistical inference, and spirometry techniques were used as a base for aquiring scientific knowledge of the system. The system directly measures the variable of interest (air volume) and refers to spirometric techniques in the elaboration of its model. This improves the realism of the dummies during the CPR training, because it estimates, in real-time, not only the volume of air entering in the lungs but also the Forced Vital Capacity (FVC), Forced Expiratory Volume (FEVt) and Medium Forced Expiratory Flow (FEF_20–75%). The validation of the sensor achieved results that address the requirements for this application, that is, the error below 3.4% of full scale. During the spirometric tests, the system presented the measurement results of (305 ± 22, 450 ± 23, 603 ± 24, 751 ± 26, 922 ± 27, 1021 ± 30, 1182 ± 33, 1326 ± 36, 1476 ± 37, 1618 ± 45 and 1786 ± 56) × 10⁻⁶ m³ for reference values of (300, 450, 600, 750, 900, 1050, 1200, 1350, 1500, 1650 and 1800) × 10⁻⁶ m³, respectively. Therefore, considering the spirometry and pressure boundary conditions of the manikin lungs, the system achieves the objective of simulating valid spirometric data for debriefings, that is, there is an agreement between the measurement results when compared to the signal generated by a commercial spirometer (Koko brand). The main advantages that this work presents in relation to the sensors commonly used for this purpose are: (i) the reduced cost, which makes it possible, for the first time, to use a respiratory volume sensor in medical simulators or training dummies; (ii) the direct measurement of air entering the lung using a noninvasive method, which makes it possible to use spirometry parameters to characterize simulated human respiration during the CPR training; and (iii) the measurement of spirometric parameters (FVC, FEVt, and FEF_20–75%), in real-time, during the CPR training, to achieve optimal ventilation performance. Therefore, the system developed in this work addresses the minimum requirements for the practice of ventilation in the CPR maneuvers and has great potential in several future applications.

This work proposes to adapt an existing sensor and embed it on mannequins used in cardiopulmonary resuscitation (CPR) training to accurately measure the amount of air supplied to the lungs during ventilation. The proposed sensor consists of measuring the airflow using propellers. The method directly measures the variable of interest and makes reference to spirometric techniques in the elaboration of its model, improving the realism of the dummies. Besides advantages over the sensors that are commonly used for this purpose, the projected sensor presented an agreement with its theoretical model and with the spirometric model. It is suitable for applications with a resolution of 17 mL, and precision of 50 mL and 26 mL for initial (< 900 mL) and final ranges, respectively.

Jataí is a pollinator of some crops; therefore, its sustainable management guarantees quality in the ecosystem services provided and implementation in precision agriculture. We acquired videos of natural and artificial hives in urban and rural environments with a camera positioned at the hive entrance. In this way, we obtained videos of the entrance of several colonies for multiple bee tracking and removed images from the videos for bee detectors. This data, their respective labels, and metadata make up the dataset. The dataset displays potential for utilization in computer vision tasks such as comparative studies of deep learning models. They can also integrate intelligent monitoring systems for natural and artificial hives.