DeepVentilation: Learning to Predict Physical Effort from Breathing

Abstract

Tracking physical effort from physiological signals has enabled people to manage required activity levels in our increasingly sedentary and automated world. Breathing is a physiological process that is a reactive representation of our physical effort. In this demo, we present DeepVentilation, a deep learning system to predict minute ventilation in litres of air a person moves in one minute uniquely from real-time measurement of rib-cage breathing forces. DeepVentilation has been trained on input signals of expansion and contraction of the rib-cage obtained using a non-invasive respiratory inductance plethysmography sensor to predict minute ventilation as observed from a face/head mounted exercise spirometer. The system is used to track physical effort closely matching our perception of actual exercise intensity. The source code for the demo is available here: https://github.com/simula-vias/DeepVentilation