Experimental development of a connected facemask spirometer prototype

Abstract

Lung diseases are among the most common non-communicable medical conditions in the world. Patient’s respiratory health status is usually assessed using spirometry. It is performed using a device called a spirometer. Development of state of the art spirometry devices that can met current technical requirements and be widely spread is thus a current major healthcare need. Conventionally, spirometers were designed as desktop systems. The development of digital technologies and their implantation in the healthcare industry has lead to the development of portable spirometers. They are simpler to use, more practical, space saving and generally cheaper than desktop systems. This thesis is based on a spirometry facemask prototype developed in the GIGA-In Silico Medecine laboratory at the University of Liège, Belgium. This device aims to be a low-cost, practical, easily usable, connected and reliable portable spirometer. Its flow sensor is based on the venturi effect. The first part of this thesis consists in performing a technical and usability analysis of the device design. Our analysis revealed that the prototype performance are satisfactory with respect to major spirometry technical standards. Some improvements that could be brought were listed. The usability analysis showed that the current venturi-based flow sensor was too cumbersome and should be optimised. Based on this investigation outcomes, the second part aim is to improve the prototype taking into account its design requirements mentioned above. A novel flow sensor that aims to estimate breathing flow based solely on the mask inner pressure was thus imagined. A setup mimicking in vivo spirometry manoeuvres was build and use to experimentally develop the sensor. The concepts of mask flow pressure relationship, facemask breathing resistance and spirometry steady flows were investigated. It was concluded that the mask flow pressure relationship was the most appropriate metric for flow estimation