Intravenous (IV) infusion is one of the most common therapies in hospitalized patients. Monitoring the flow rate of the fluid that is being administered to the patient is therefore very important for his safety, considering that both over-infusion and under-infusion can cause serious health problems. In this document, a novel method for monitoring the flow rate in IV infusions is presented, that is based on deep learning computer vision techniques. Basically, the drip chamber is filmed with a camera and object detection is used to count drops. The proposed method is therefore less invasive than other ones developed for this purpose. Experimental results show that it can produce an accurate real-time estimate of the instantaneous flow rate of the drip. For these reasons, the proposed method can be effectively adopted to implement monitoring and control systems for health facilities.

Real-time drip infusion monitoring through a computer vision system / Giaquinto, Nicola; Scarpetta, Marco; Ragolia, Mattia Alessandro; Pappalardi, Pietro. - ELETTRONICO. - (2020). (Intervento presentato al convegno 15th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2020 tenutosi a Bari, Italy nel June 1 - July 1, 2020) [10.1109/MeMeA49120.2020.9137359].

Real-time drip infusion monitoring through a computer vision system

Nicola Giaquinto;Marco Scarpetta;Mattia Alessandro Ragolia;Pietro Pappalardi
2020-01-01

Abstract

Intravenous (IV) infusion is one of the most common therapies in hospitalized patients. Monitoring the flow rate of the fluid that is being administered to the patient is therefore very important for his safety, considering that both over-infusion and under-infusion can cause serious health problems. In this document, a novel method for monitoring the flow rate in IV infusions is presented, that is based on deep learning computer vision techniques. Basically, the drip chamber is filmed with a camera and object detection is used to count drops. The proposed method is therefore less invasive than other ones developed for this purpose. Experimental results show that it can produce an accurate real-time estimate of the instantaneous flow rate of the drip. For these reasons, the proposed method can be effectively adopted to implement monitoring and control systems for health facilities.
2020
15th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2020
978-1-7281-5386-5
Real-time drip infusion monitoring through a computer vision system / Giaquinto, Nicola; Scarpetta, Marco; Ragolia, Mattia Alessandro; Pappalardi, Pietro. - ELETTRONICO. - (2020). (Intervento presentato al convegno 15th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2020 tenutosi a Bari, Italy nel June 1 - July 1, 2020) [10.1109/MeMeA49120.2020.9137359].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/205166
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