Cricothyrotomy is a life-saving procedure performed when an airway cannot be established through less invasive techniques. One of the main challenges of the research community in this area consists in designing and building a low-cost simulator that teaches essential anatomy, and providing a method of data collection for performance evaluation and guided instruction as well. In this paper, we present a Cyber Physical System designed and developed for activity detection in the medical context. In more detail, we first acquire data in real time from a cricothyrotomy simulator, when used by medical doctors, then we store the acquired data into a scientific database and finally we use an Activity Detection Engine for finding expected activities, in order to automatically evaluate the medical doctors' performances when using the simulator. Some preliminary experiments using real data show the approach efficiency and effectiveness. Eventually, we also received positive feedbacks by the medical personnel who used our prototype.
D'Auria, D., Persia, F. (2014). Automatic evaluation of medical doctors' performances while using a cricothyrotomy simulator. In Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, IEEE IRI 2014 (pp.514-519). IEEE [10.1109/IRI.2014.7051932].
Automatic evaluation of medical doctors' performances while using a cricothyrotomy simulator
D'Auria, D;
2014
Abstract
Cricothyrotomy is a life-saving procedure performed when an airway cannot be established through less invasive techniques. One of the main challenges of the research community in this area consists in designing and building a low-cost simulator that teaches essential anatomy, and providing a method of data collection for performance evaluation and guided instruction as well. In this paper, we present a Cyber Physical System designed and developed for activity detection in the medical context. In more detail, we first acquire data in real time from a cricothyrotomy simulator, when used by medical doctors, then we store the acquired data into a scientific database and finally we use an Activity Detection Engine for finding expected activities, in order to automatically evaluate the medical doctors' performances when using the simulator. Some preliminary experiments using real data show the approach efficiency and effectiveness. Eventually, we also received positive feedbacks by the medical personnel who used our prototype.
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