Contact: Gregorio Andria
Annamaria Lanzolla
Mail: gregorio.andria@poliba.it
annamarialucia.lanzolla@poliba.it
Application field
Technology
Activity
Measurement systems for health
Electromagnetic tracking system, deep learning for computer vision
Surgical navigation for diagnosis and interventions, Intravenous Drip Infusion Monitoring
Keywords
Healthcare, EM tracking system, image guided surgery, reconstruction algorithm, surgical navigation, intravenous infusion, sensors for therapeutic treatments, deep learning.
Operation of the proposed solution
Two basic tasks in the perioperative period are i) surgery and ii) patient monitoring and care. The outcome of diagnosis and surgical interventions can be improved by means of electromagnetic tracking systems (EMTSs) [1], widely used in surgical navigation. They employ very small EM sensors, which measure the magnetic field produced by a field generator, thus accurately estimating the pose of the instrument in the operative scenario. Moreover, in the pre- and postoperative phase, monitoring the flow rate of the fluid being administered to patients is very important for their safety. Hence, our proposed system [2] uses a camera to film the intravenous (IV) drip infusion kit and a deep learning-based algorithm to detect and count drops. The usage of a camera as a sensing element is safe in medical environments and can be easily integrated into current health facilities.
Challenges
1. Increase tracking distance of EMTSs beyond 50 cm of current commercial systems 2. Develop new camera-based smart monitoring devices for medical applications
Bibliography
[1] F. Attivissimo, A. D. Nisio, A. M. L. Lanzolla and M. A. Ragolia, “Analysis of Position Estimation Techniques in a Surgical EM Tracking System,” in IEEE Sensors Journal, vol. 21, no. 13, pp. 14389-14396, 1 July1, 2021, doi: 10.1109/JSEN.2020.3042647.
[2] N. Giaquinto, M. Scarpetta, M. Spadavecchia and G. Andria, “Deep Learning-Based Computer Vision for Real-Time Intravenous Drip Infusion Monitoring,” in IEEE Sensors Journal, vol. 21, no. 13, pp. 14148-14154, 1 July1, 2021, doi: 10.1109/JSEN.2020.3039009.
Video