Detecting mistakes in CPR training with multimodal data and neural networks

24Citations
Citations of this article
104Readers
Mendeley users who have this article in their library.

Abstract

This study investigated to what extent multimodal data can be used to detect mistakes during Cardiopulmonary Resuscitation (CPR) training. We complemented the Laerdal QCPR ResusciAnne manikin with the Multimodal Tutor for CPR, a multi-sensor system consisting of a Microsoft Kinect for tracking body position and a Myo armband for collecting electromyogram information. We collected multimodal data from 11 medical students, each of them performing two sessions of two-minute chest compressions (CCs). We gathered in total 5254 CCs that were all labelled according to five performance indicators, corresponding to common CPR training mistakes. Three out of five indicators, CC rate, CC depth and CC release, were assessed automatically by the ReusciAnne manikin. The remaining two, related to arms and body position, were annotated manually by the research team. We trained five neural networks for classifying each of the five indicators. The results of the experiment show that multimodal data can provide accurate mistake detection as compared to the ResusciAnne manikin baseline. We also show that the Multimodal Tutor for CPR can detect additional CPR training mistakes such as the correct use of arms and body weight. Thus far, these mistakes were identified only by human instructors. Finally, to investigate user feedback in the future implementations of the Multimodal Tutor for CPR, we conducted a questionnaire to collect valuable feedback aspects of CPR training.

References Powered by Scopus

Long Short-Term Memory

76931Citations
N/AReaders
Get full text

Multimodal Machine Learning: A Survey and Taxonomy

2448Citations
N/AReaders
Get full text

The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems

1091Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Multimodal data fusion in learning analytics: A systematic review

67Citations
N/AReaders
Get full text

Real-time multimodal feedback with the CPR tutor

41Citations
N/AReaders
Get full text

Table tennis tutor: Forehand strokes classification based on multimodal data and neural networks

32Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

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). https://doi.org/10.3390/s19143099

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 28

52%

Researcher 14

26%

Professor / Associate Prof. 6

11%

Lecturer / Post doc 6

11%

Readers' Discipline

Tooltip

Medicine and Dentistry 21

44%

Nursing and Health Professions 11

23%

Computer Science 8

17%

Engineering 8

17%

Save time finding and organizing research with Mendeley

Sign up for free