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
Keywords
- Machine Learning: general
- Human-Computer Interactive Systems: general
- Uncertainty in AI: general
