%0 Journal Article %1 DiMitri2021 %A Di Mitri, Daniele %A Schneider, Jan %A Drachsler, Hendrik %D 2021 %J International Journal of Artificial Intelligence in Education %K cardiopulmonaryresuscitation intelligenttutoringsystems learninganalytics multimodaldata realtimefeedback recurrentneuralnetworks %R 10.1007/s40593-021-00281-z %T Keep Me in the Loop: Real-Time Feedback with Multimodal Data %U https://doi.org/10.1007/s40593-021-00281-z %X This paper describes the CPR Tutor, a real-time multimodal feedback system for cardiopulmonary resuscitation (CPR) training. The CPR Tutor detects training mistakes using recurrent neural networks. The CPR Tutor automatically recognises and assesses the quality of the chest compressions according to five CPR performance indicators. It detects training mistakes in real-time by analysing a multimodal data stream consisting of kinematic and electromyographic data. Based on this assessment, the CPR Tutor provides audio feedback to correct the most critical mistakes and improve the CPR performance. The mistake detection models of the CPR Tutor were trained using a dataset from 10 experts. Hence, we tested the validity of the CPR Tutor and the impact of its feedback functionality in a user study involving additional 10 participants. The CPR Tutor pushes forward the current state of the art of real-time multimodal tutors by providing: (1) an architecture design, (2) a methodological approach for delivering real-time feedback using multimodal data and (3) a field study on real-time feedback for CPR training. This paper details the results of a field study by quantitatively measuring the impact of the CPR Tutor feedback on the performance indicators and qualitatively analysing the participants' questionnaire answers.

@article{DiMitri2021, abstract = {This paper describes the CPR Tutor, a real-time multimodal feedback system for cardiopulmonary resuscitation (CPR) training. The CPR Tutor detects training mistakes using recurrent neural networks. The CPR Tutor automatically recognises and assesses the quality of the chest compressions according to five CPR performance indicators. It detects training mistakes in real-time by analysing a multimodal data stream consisting of kinematic and electromyographic data. Based on this assessment, the CPR Tutor provides audio feedback to correct the most critical mistakes and improve the CPR performance. The mistake detection models of the CPR Tutor were trained using a dataset from 10 experts. Hence, we tested the validity of the CPR Tutor and the impact of its feedback functionality in a user study involving additional 10 participants. The CPR Tutor pushes forward the current state of the art of real-time multimodal tutors by providing: (1) an architecture design, (2) a methodological approach for delivering real-time feedback using multimodal data and (3) a field study on real-time feedback for CPR training. This paper details the results of a field study by quantitatively measuring the impact of the CPR Tutor feedback on the performance indicators and qualitatively analysing the participants' questionnaire answers.}, added-at = {2021-11-21T09:58:42.000+0100}, author = {Di Mitri, Daniele and Schneider, Jan and Drachsler, Hendrik}, biburl = {https://www.bibsonomy.org/bibtex/2551b7596b006baec199abc1c85086f5c/ereidt}, day = 18, doi = {10.1007/s40593-021-00281-z}, interhash = {e8527df6223b7a814a93629b44d849dc}, intrahash = {551b7596b006baec199abc1c85086f5c}, issn = {1560-4306}, journal = {International Journal of Artificial Intelligence in Education}, keywords = {cardiopulmonaryresuscitation intelligenttutoringsystems learninganalytics multimodaldata realtimefeedback recurrentneuralnetworks}, month = nov, timestamp = {2021-11-21T09:58:42.000+0100}, title = {Keep Me in the Loop: Real-Time Feedback with Multimodal Data}, url = {https://doi.org/10.1007/s40593-021-00281-z}, year = 2021 }