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 framework designed and developed for activity detection in the medical context. More in details, it first acquires data in real time from a cricothyrotomy simulator, when used by medical doctors, then it stores the acquired data into a scientific database and finally it exploits an Activity Detection Engine for finding expected activities, in order to evaluate the medical doctors’ performances in real time, that is very essential for this kind of applications. In fact, an incorrect use of the simulator promptly detected can save the patient’s life. The conducted 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. (2015). A framework for real-time evaluation of medical doctors' performances while using a cricothyrotomy simulator. In Data Management Technologies and Applications Third International Conference, DATA 2014, Vienna, Austria, August 29-31, 2014, Revised Selected papers (pp.182-198). Springer Verlag [10.1007/978-3-319-25936-9_12].
A framework for real-time evaluation of medical doctors' performances while using a cricothyrotomy simulator
D'Auria, D;
2015
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 framework designed and developed for activity detection in the medical context. More in details, it first acquires data in real time from a cricothyrotomy simulator, when used by medical doctors, then it stores the acquired data into a scientific database and finally it exploits an Activity Detection Engine for finding expected activities, in order to evaluate the medical doctors’ performances in real time, that is very essential for this kind of applications. In fact, an incorrect use of the simulator promptly detected can save the patient’s life. The conducted 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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