Detecting Medical Simulation Errors with Machine learning and Multimodal Data

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Abstract

In this doctoral consortium paper, we introduce the CPR Tutor, an intelligent tutoring system for cardiopulmonary resuscitation (CPR) training based on the analysis of multimodal data. Using a multi-sensor setup, the CPR Tutor tracks the CPR execution of the trainee and generates automatic adaptive feedback to improve the trainee's performance. This research work is part of a PhD project entitled "Multimodal Tutor: adaptive feedback from multimodal experience capturing'', a project which investigates how to use multimodal and multi-sensor data to generate personalised feedback for training psycho-motor skills at the workplace or during medical simulations. In the CPR Tutor, we use Microsoft Kinect and Myo to track trainee's body position and the ResusciAnne QCPR manikin to get correct CPR performance metrics. We then use a validated approach, the Multimodal Pipeline, for the collection, storage, processing, annotation of multimodal data. This paper describes the preliminary results obtained in the first design of the CPR Tutor.
Original languageEnglish
Pages1-6
Number of pages6
Publication statusPublished - May 2019
Event17th Conference on Artificial Intelligence in Medicine - Poznan, Poland
Duration: 26 Jun 201929 Jun 2019
https://aime19.aimedicine.info/

Conference

Conference17th Conference on Artificial Intelligence in Medicine
Abbreviated titleAIME 2019
CountryPoland
CityPoznan
Period26/06/1929/06/19
Internet address

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  • Cite this

    Di Mitri, D. (2019). Detecting Medical Simulation Errors with Machine learning and Multimodal Data. 1-6. Paper presented at 17th Conference on Artificial Intelligence in Medicine, Poznan, Poland.