Lucrezia Dimatera
Development of a portable predictive model for the real-time estimation of Pulmonary Ventilation.
Rel. Andrea Calimera. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2022
Abstract
Abstract Monitoring physical activities and training through an ensemble of vitals such as heart rate or energy expenditure is a fundamental activity that allows coaches to gain useful insights on the training sessions while reducing the risk of overtraining and injuries. Only recently the additional evaluation of respiratory vitals during workout sessions has been proven to contribute to an exhaustive assessment of the physical exercise. However, golden standards for measuring respiratory parameters, such as the gas transfer machine, suffer from some major drawbacks, being extremely invasive and impractical. Therefore in this work, developed in collaboration with the startup StepUp Solutions, active in the field of hardware development for sports and health monitoring, we want to highlight the importance of obtaining fairly similar information relying on a non-invasive approach based on the estimation of the Pulmonary Ventilation from chest expansion and contraction signals measured with the StepUp Air wearable device.
The focus of this thesis is on building and deploying on the nRF52840 processor (32-bit ARM® Cortex®-M4FCPU running at 64 MHz) exploiting the Arduino Nano 33 BLE board, portable predictive models to provide a real-time estimate of the Pulmonary Ventilation based on chest expansion and contraction data acquired by the StepUp Solutions team
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