CPR Tutor (2020)

The CPR tutor is a tool that helps users learn and practice CPR skills using real-time feedback and multimodal data. It uses sensors to measure the kinematic and electromyographic data of the user while performing CPR on a manikin. The system uses recurrent neural networks to detect and classify chest compressions according to five performance indicators: compression rate, compression depth, compression release, hand position, and arm posture. The system then provides audio feedback to correct the most critical mistakes and improve CPR performance. The CPR tutor aims to enhance the learning experience and outcomes of CPR training by providing personalised and adaptive feedback based on multimodal data.

Related publications

    • Di Mitri, D., Schneider, J., Klemke, R., Specht, M., & Drachsler, H. (2019). Read Between the Lines: An Annotation Tool for Multimodal Data for Learning. Proceedings of the 9th International Conference on Learning Analytics & Knowledge, 51–60. https://doi.org/10.1145/3303772.3303776
    • Di Mitri, D. (2018). Multimodal Tutor for CPR. In C. Penstein Rosé, R. Martínez-Maldonado, H. U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. McLaren, & B. du Boulay (Eds.), Artificial Intelligence in Education (Vol. 10948, pp. 513–516). Springer International Publishing. https://doi.org/10.1007/978-3-319-93846-2_96
    • Di Mitri, D., Schneider, J., & Drachsler, H. (2022). Keep Me in the Loop: Real-Time Feedback with Multimodal Data. International Journal of Artificial Intelligence in Education, 32(4), 1093–1118. https://doi.org/10.1007/s40593-021-00281-z
    • Di Mitri, D., Schneider, J., Specht, M., & Drachsler, H. (2019). Detecting mistakes in CPR training with multimodal data and neural networks. Sensors (Switzerland), 19(14), 1–20. https://doi.org/10.3390/s19143099
    • Di Mitri, D., Schneider, J., Trebing, K., Sopka, S., Specht, M., & Drachsler, H. (2020). Real-Time Multimodal Feedback with the CPR Tutor. In I. I. Bittencourt, M. Cukurova, & K. Muldner (Eds.), Artificial Intelligence in Education (AIED’2020) (pp. 141–152). Springer, Cham. https://doi.org/10.1007/978-3-030-52237-7_12
    • Schneider, J., Di Mitri, D., Limbu, B., & Drachsler, H. (2018). Multimodal Learning Hub: A Tool for Capturing Customizable Multimodal Learning Experiences. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11082 LNCS, 45–58. https://doi.org/10.1007/978-3-319-98572-5_4

Published by Daniele Di Mitri

Daniele Di Mitri is a professor of Multimodal Learning Technologies at the German University of Digital Science. At the German UDS, he leads the research group "Augmented Feedback" and coordinates the master's in Advanced Digital Realities.  He is an associated researcher at the DIPF - Leibniz Institute for Research and Information in Education and a lecturer at the Goethe University of Frankfurt, Germany. Daniele Di Mitri received his PhD in Learning Analytics and Wearable Sensor Support from the Open University of the Netherlands. His current research focuses on developing AI-driven, multimodal learning technologies to enhance digital education. It aims to create innovative, responsible solutions that improve learning experiences through advanced feedback systems and ethical integration of technology. He is a "Johanna Quandt Young Academy" fellow and was elected "AI Newcomer 2021" at the KI Camp by the German Informatics Society. He is a member of the CrossMMLA, a special interest group of the Society for Learning Analytics Research, and the chair of the special interest group on AI for Education of the European Association for Technology-Enhanced Learning.