Di Mitri, D.

English

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.<br/

Open University of the Netherlands Research Portal

Open Universiteit www.ou.nl Detecting Medical Simulation Errors with Machinelearning and Multimodal DataCitation for published version (APA):Di Mitri, D. (2019). Detecting Medical Simulation Errors with Machine learning and Multimodal Data. 1-6. Paperpresented at 17th Conference on Artificial Intelligence in Medicine, Poznan, Poland.Document status and date:Published: 01/05/2019Document Version:Publisher's PDF, also known as Version of recordDocument license:CC BY-SAPlease check the document version of this publication:• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences betweenthe submitted version and the official published version of record. People interested in the research are advised to contact the author for thefinal version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers.Link to publicationGeneral rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain• You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, pleasefollow below link for the End User Agreement:https://www.ou.nl/taverne-agreementTake down policyIf you believe that this document breaches copyright please contact us at:pure-support@ou.nlproviding details and we will investigate your claim.Downloaded from https://research.ou.nl/ on date: 16 Jul. 2023Detecting Medical Simulation Errors withMachine learning and Multimodal DataDaniele Di MitriWelten Institute, Research Centre for Learning, Teaching and TechnologyOpen University of the NetherlandsDaniele.Dimitri@ou.nlAbstract. In this doctoral consortium paper, we introduce the CPRTutor, 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 traineeand generates automatic adaptive feedback to improve the trainee’s per-formance. This research work is part of a PhD project entitled “Multi-modal Tutor: adaptive feedback from multimodal experience capturing”,a project which investigates how to use multimodal and multi-sensordata to generate personalised feedback for training psycho-motor skillsat the workplace or during medical simulations. In the CPR Tutor, weuse Microsoft Kinect and Myo to track trainee’s body position and theResusciAnne QCPR manikin to get correct CPR performance metrics.We then use a validated approach, the Multimodal Pipeline, for the col-lection, storage, processing, annotation of multimodal data. This paperdescribes the preliminary results obtained in the first design of the CPRTutor.1 BackgroundLearning how to master practical skills is highly relevant in the medical domain.Often healthcare professionals are not given enough opportunities for trainingtheir medical skills. Existing simulations quite often lack on-task, real-time andactionable feedback, which results in longer and less effective training for theprofessionals. With the Multimodal Tutor, we aim at supporting skills trainingand simulations in a variety of learning scenarios using multimodal data, ma-chine learning and intelligent tutoring. With the term multimodal data, we referto 1) micro-level learners behavioural data: i.e. motoric actions or physiologicalresponses; 2) data of the learning situation such as learning context, environ-ment and activity. Such data can be collected using wearable sensors, camerasor Internet of Things (IoT) devices. Combining data from multiple modalitiesallows us to achieve a more accurate representation of the learning process. Ifmultimodal data are associated with performance measurements, they can befed into machine learning algorithms which can detect training errors and whichcan be used to provide on-time automatic and personalised feedback. In our PhDproject, we have looked into two relevant medical use-cases, among the severalmedical scenarios where to use the Multimodal Tutor.2The first use-case, developed within the WEKIT Project1 consists of learninghow to perform different eco-graphic tests using an Ultrasound Machine foracquiring medical images of, for example, the carotid or spleen test. The secondexample, investigated in depth in this research work, deals with cardiopulmonaryresuscitation training, more precisely how to correctly perform chest compression(CCs) as part of the CPR procedure. We selected CPR as a representative taskfor multiple reasons: it is usually trained singularly and has a clearly definedprocedure. The learning objectives are also well defined: e.g. the compressionsshould be around 120 per minute and 5 cm deep. Finally, CPR has a strongrelevance: the cases of cardiopulmonary arrest are unfortunately very common.The more people are trained to do CPR the higher will be the chance of savinglives.2 Related studiesExamples of multimodal tutors, i.e. intelligent tutors using multimodal data toprovide adaptive and personalised feedback are AutoTutor [5] or the AffectiveLearning Companion [1]. For the specific case of Multimodal Tutors for CPR,previous researchers have used Kinect based systems for tracking the CPR per-formance. [7] first piloted a Kinect-based system for providing feedback. Thestudy in [8] designed a Kinect-based real-time audiovisual feedback device to in-vestigate the relationship between rescuer posture, body weight, and CC quality.They tested 100 participants monitoring depth and rate of CC and providingfurther real-time feedback. The result of this study was that kneeling postureprovides better CC than a standing posture and that audio-visual feedback canprovide a better CC depth, rate, and effective CC ratio. In our study we pro-posed the use of neural network in order to detect training errors in terms ofCC rate, CC depth, CC release, but also to detect additional training errors, sofar not tracked by commercial manikins, such as the correct locking of the armsduring and the correct use of body posture and body weight during CC.3 Goal and Research QuestionsThe goal of the research work presented in the paper is to further develop theidea of a CPR Tutor [2], a specific implementation of the Multimodal Tutor, andanswer the following research questions:1. Is possible to classify CPR training errors using multimodal sensor data?2. Can we use multimodal data to classify custom defined CPR errors?3. Can we train classifiers generalisable across different users and setups?1 Wearable-enhanced Knowledge Intensive Training - WEKIT projecthttp://www.wekit.eu3Fig. 1. The graphic representation of the experimental setting4 Proposed approachTo set the theoretical ground of the Multimodal Tutor, we have first proposeda conceptual model, the Multimodal Learning Analytics Model (MLeAM)[4].MLeAM shows how multimodal data can produce feedback to the learner andit helps to classify similar experiments in the field. On the technical side, wedeveloped a technological system, the Multimodal Pipeline, which consists in achain of multiple technological solutions for the collection, storing, processing,annotation and exploitation of multimodal data for supporting learning.The first component of the Pipeline is the Multimodal Learning Hub [6] anOpen Source software which collects data from multiple sensor applications,synchronises them and store them into a custom data format named MeaningfulLearning Task (MLT). For the case of the CPR Tutor, the Learning Hub collects1) body position from Microsoft Kinect; 2) electro myogram information fromthe Myo armband.The second component is the Visual Inspection Tool [3] a web applicationwhich allows to visualize and annotate the MLT sessions collected with theLearningHub. For the case of the CPR Tutor, the VIT allows plotting the Kinectand Myo along with the video recordings. It also allows loading the data from theResusciAnne manikin’s wireless Simpad SkillReporter which are used as baselineannotations for the CPR Tutor. The Simpad SkillReporter records start and stoptime for each single CC and rate them according to the their 1) Compression Rate4(ideal values: 100-120 per minute); 2) Compression Depth (ideal values: 5-6 cm);3) Compression Release (ideal values: 0-1 cm). An idea how the compressionsare annotated in the VIT is shown in figure 2. Moreover, besides loading theperformance measurements which can be retrieved by the Simpad SkillReporter,the VIT allows defining expert-driven custom annotations. For example, each CCcan be marked with additional training errors that the ResusciAnne manikin isnot able to track such as if the arms are locked or the body weight is usedcorrectly.Once the MLT session has been annotated it can be loaded into the thirdcomponent of the Multimodal Pipeline, a data processing script that processesthe annotated dataset and prepares it for supervised machine learning. The scriptcan process multiple sessions at the time.5 ResultsIn December 2018, we ran our first experimental trial for collecting the neces-sary data to design the CPR Tutor. We tested 11 experts (medical students)performing one at the time two sessions of two minutes CCs. We collected intotal around 5200+ CC. The setup of the experiment is similar to the one repre-sented in figure 1. The dataset obtained includes 53 attributes (time series) suchas ElbowRightX, ShoulderLeftZ as each body jointure for Kinect is a 3D vector.Fig. 2. Plotting example of the CPR Tutor data. The yellow columns are time-intervalswhich indicate the start and end of the chest-copressions used as training samples. Eachchest-compression is rated with three classes.All CCs were labelled for CompressionRate and CompressionDepth withthree classes: 0 - too slow, 1 - correct, 2 - too fast and CompressionRelease5with two classes 0 - wrong 1 - right. These classes are based on the type offeedback meaningful for the learner e.g. either ‘too slow’, ‘on-point’ or ‘too fast’.These perfomance metrics were extracted by the ResusciAnne manikins. To pre-pare the data for machine learning we considered each of the 5254 CCs as onelearning sample. Each of these samples is a matrix of 53 attributes times 8, whichis a fixed number of bins by which the CCs were re-sampled to make them evenin length. That lead having a tensor of shape (5254, 8, 53) And corresponding1D output vector of labels of size 5254.We fed this tensor into three Recurrent Neural Networks using the TensorFlow-Keras library. We split the dataset into 2/3 training and 1/3 test. We trainedthree different classifiers as we have three target classes. In the networks we useda sequential model having first a Long Short Term Memory network of shape(8,53) and then a dense layer which with a SoftMax function classify the mostlikely class among the 3 or 2 options. With such setup, we were able to getreasonable accuracy of the model of around 70-75%.6 Questions for the Doctoral ConsortiumIn this paper we presented our approach for the design of the CPR Tutor. In thecurrent approach we used each CC as training sample by masking/windowingof the original time series. Then we trained an LSTM network with all thesesamples. With this approach we are able to classify accurately the target classes,however discarding the rest of the time-series we are not able to detect if aCC happened. We ask the DC how, given the available data, could we train aclassifier able to detect whether a CC happened or not. We also ask the DC toprovide feedback on the methodology and the relevance of the project.References1. Burleson, W.: Affective learning companions. Educational Technology 47(1), 28(2007)2. Di Mitri, D.: Multimodal tutor for CPR. In: Lecture Notes in Computer Science(including subseries Lecture Notes in Artificial Intelligence and Lecture Notes inBioinformatics). vol. 10948 LNAI, pp. 513–516. Springer International Publishing,Cham, Switzerland (2018)3. Di Mitri, D., Schneider, J., Klemke, R., Specht, M., Drachsler, H.: ReadBetween the Lines: An Annotation Tool for Multimodal Data for Learn-ing. In: Proceedings of the 9th International Conference on Learning Analyt-ics & Knowledge - LAK19. pp. 51–60. ACM, New York, NY, USA (2019),http://dl.acm.org/citation.cfm?doid=3303772.33037764. Di Mitri, D., Schneider, J., Specht, M., Drachsler, H.: From signals to knowledge: Aconceptual model for multimodal learning analytics. Journal of Computer AssistedLearning 34(4), 338–349 (aug 2018), http://doi.wiley.com/10.1111/jcal.122885. D’Mello, S., Jackson, T., Craig, S., Morgan, B., Chipman, P., White, H., Person,N., Kort, B., El Kaliouby, R., Picard, R.W., Graesser, A.: AutoTutor detects andresponds to learners affective and cognitive states. IEEE Transactions on Education48(4), 612–618 (2008)66. Schneider, J., Di Mitri, D., Limbu, B., Drachsler, H.: Multimodal Learning Hub:A Tool for Capturing Customizable Multimodal Learning Experiences. In: LectureNotes in Computer Science (including subseries Lecture Notes in Artificial Intelli-gence and Lecture Notes in Bioinformatics). vol. 11082 LNCS, pp. 45–58. Springer,Cham, Switzerland (2018)7. Semeraro, F., Frisoli, A., Loconsole, C., Bannò, F., Tammaro, G., Imbriaco, G.,Marchetti, L., Cerchiari, E.L.: A new Kinect-based system for the analysis of per-formances in cardiopulmonary resuscitation (CPR) training. Resuscitation 83, e20(2012)8. Wang, J.C.C., Tsai, S.H.H., Chen, Y.L., Chu, S.J.J., Liao, W.I.I.: Kinect-based real-time audiovisual feedback device improves CPR quality oflower-body-weight rescuers. American Journal of Emergency Medicine36(4), 577–582 (apr 2018), http://www.ncbi.nlm.nih.gov/pubmed/28927950https://linkinghub.elsevier.com/retrieve/pii/S0735675717307477https://doi.org/10.1016/j.ajem.2017.09.022

Detecting Medical Simulation Errors with Machine learning and Multimodal Data

Open Universiteit www.ou.nl Detecting Medical Simulation Errors with Machinelearning and Multimodal DataCitation for published version (APA):Di Mitri, D. (2019). Detecting Medical Simulation Errors with Machine learning and Multimodal Data. 1-6. Paperpresented at 17th Conference on Artificial Intelligence in Medicine, Poznan, Poland.Document status and date:Published: 01/05/2019Document Version:Publisher's PDF, also known as Version of recordDocument license:CC BY-SAPlease check the document version of this publication:• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences betweenthe submitted version and the official published version of record. People interested in the research are advised to contact the author for thefinal version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers.Link to publicationGeneral rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain• You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, pleasefollow below link for the End User Agreement:https://www.ou.nl/taverne-agreementTake down policyIf you believe that this document breaches copyright please contact us at:pure-support@ou.nlproviding details and we will investigate your claim.Downloaded from https://research.ou.nl/ on date: 09 Sep. 2021Detecting Medical Simulation Errors withMachine learning and Multimodal DataDaniele Di MitriWelten Institute, Research Centre for Learning, Teaching and TechnologyOpen University of the NetherlandsDaniele.Dimitri@ou.nlAbstract. In this doctoral consortium paper, we introduce the CPRTutor, 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 traineeand generates automatic adaptive feedback to improve the trainee’s per-formance. This research work is part of a PhD project entitled “Multi-modal Tutor: adaptive feedback from multimodal experience capturing”,a project which investigates how to use multimodal and multi-sensordata to generate personalised feedback for training psycho-motor skillsat the workplace or during medical simulations. In the CPR Tutor, weuse Microsoft Kinect and Myo to track trainee’s body position and theResusciAnne QCPR manikin to get correct CPR performance metrics.We then use a validated approach, the Multimodal Pipeline, for the col-lection, storage, processing, annotation of multimodal data. This paperdescribes the preliminary results obtained in the first design of the CPRTutor.1 BackgroundLearning how to master practical skills is highly relevant in the medical domain.Often healthcare professionals are not given enough opportunities for trainingtheir medical skills. Existing simulations quite often lack on-task, real-time andactionable feedback, which results in longer and less effective training for theprofessionals. With the Multimodal Tutor, we aim at supporting skills trainingand simulations in a variety of learning scenarios using multimodal data, ma-chine learning and intelligent tutoring. With the term multimodal data, we referto 1) micro-level learners behavioural data: i.e. motoric actions or physiologicalresponses; 2) data of the learning situation such as learning context, environ-ment and activity. Such data can be collected using wearable sensors, camerasor Internet of Things (IoT) devices. Combining data from multiple modalitiesallows us to achieve a more accurate representation of the learning process. Ifmultimodal data are associated with performance measurements, they can befed into machine learning algorithms which can detect training errors and whichcan be used to provide on-time automatic and personalised feedback. In our PhDproject, we have looked into two relevant medical use-cases, among the severalmedical scenarios where to use the Multimodal Tutor.2The first use-case, developed within the WEKIT Project1 consists of learninghow to perform different eco-graphic tests using an Ultrasound Machine foracquiring medical images of, for example, the carotid or spleen test. The secondexample, investigated in depth in this research work, deals with cardiopulmonaryresuscitation training, more precisely how to correctly perform chest compression(CCs) as part of the CPR procedure. We selected CPR as a representative taskfor multiple reasons: it is usually trained singularly and has a clearly definedprocedure. The learning objectives are also well defined: e.g. the compressionsshould be around 120 per minute and 5 cm deep. Finally, CPR has a strongrelevance: the cases of cardiopulmonary arrest are unfortunately very common.The more people are trained to do CPR the higher will be the chance of savinglives.2 Related studiesExamples of multimodal tutors, i.e. intelligent tutors using multimodal data toprovide adaptive and personalised feedback are AutoTutor [5] or the AffectiveLearning Companion [1]. For the specific case of Multimodal Tutors for CPR,previous researchers have used Kinect based systems for tracking the CPR per-formance. [7] first piloted a Kinect-based system for providing feedback. Thestudy in [8] designed a Kinect-based real-time audiovisual feedback device to in-vestigate the relationship between rescuer posture, body weight, and CC quality.They tested 100 participants monitoring depth and rate of CC and providingfurther real-time feedback. The result of this study was that kneeling postureprovides better CC than a standing posture and that audio-visual feedback canprovide a better CC depth, rate, and effective CC ratio. In our study we pro-posed the use of neural network in order to detect training errors in terms ofCC rate, CC depth, CC release, but also to detect additional training errors, sofar not tracked by commercial manikins, such as the correct locking of the armsduring and the correct use of body posture and body weight during CC.3 Goal and Research QuestionsThe goal of the research work presented in the paper is to further develop theidea of a CPR Tutor [2], a specific implementation of the Multimodal Tutor, andanswer the following research questions:1. Is possible to classify CPR training errors using multimodal sensor data?2. Can we use multimodal data to classify custom defined CPR errors?3. Can we train classifiers generalisable across different users and setups?1 Wearable-enhanced Knowledge Intensive Training - WEKIT projecthttp://www.wekit.eu3Fig. 1. The graphic representation of the experimental setting4 Proposed approachTo set the theoretical ground of the Multimodal Tutor, we have first proposeda conceptual model, the Multimodal Learning Analytics Model (MLeAM)[4].MLeAM shows how multimodal data can produce feedback to the learner andit helps to classify similar experiments in the field. On the technical side, wedeveloped a technological system, the Multimodal Pipeline, which consists in achain of multiple technological solutions for the collection, storing, processing,annotation and exploitation of multimodal data for supporting learning.The first component of the Pipeline is the Multimodal Learning Hub [6] anOpen Source software which collects data from multiple sensor applications,synchronises them and store them into a custom data format named MeaningfulLearning Task (MLT). For the case of the CPR Tutor, the Learning Hub collects1) body position from Microsoft Kinect; 2) electro myogram information fromthe Myo armband.The second component is the Visual Inspection Tool [3] a web applicationwhich allows to visualize and annotate the MLT sessions collected with theLearningHub. For the case of the CPR Tutor, the VIT allows plotting the Kinectand Myo along with the video recordings. It also allows loading the data from theResusciAnne manikin’s wireless Simpad SkillReporter which are used as baselineannotations for the CPR Tutor. The Simpad SkillReporter records start and stoptime for each single CC and rate them according to the their 1) Compression Rate4(ideal values: 100-120 per minute); 2) Compression Depth (ideal values: 5-6 cm);3) Compression Release (ideal values: 0-1 cm). An idea how the compressionsare annotated in the VIT is shown in figure 2. Moreover, besides loading theperformance measurements which can be retrieved by the Simpad SkillReporter,the VIT allows defining expert-driven custom annotations. For example, each CCcan be marked with additional training errors that the ResusciAnne manikin isnot able to track such as if the arms are locked or the body weight is usedcorrectly.Once the MLT session has been annotated it can be loaded into the thirdcomponent of the Multimodal Pipeline, a data processing script that processesthe annotated dataset and prepares it for supervised machine learning. The scriptcan process multiple sessions at the time.5 ResultsIn December 2018, we ran our first experimental trial for collecting the neces-sary data to design the CPR Tutor. We tested 11 experts (medical students)performing one at the time two sessions of two minutes CCs. We collected intotal around 5200+ CC. The setup of the experiment is similar to the one repre-sented in figure 1. The dataset obtained includes 53 attributes (time series) suchas ElbowRightX, ShoulderLeftZ as each body jointure for Kinect is a 3D vector.Fig. 2. Plotting example of the CPR Tutor data. The yellow columns are time-intervalswhich indicate the start and end of the chest-copressions used as training samples. Eachchest-compression is rated with three classes.All CCs were labelled for CompressionRate and CompressionDepth withthree classes: 0 - too slow, 1 - correct, 2 - too fast and CompressionRelease5with two classes 0 - wrong 1 - right. These classes are based on the type offeedback meaningful for the learner e.g. either ‘too slow’, ‘on-point’ or ‘too fast’.These perfomance metrics were extracted by the ResusciAnne manikins. To pre-pare the data for machine learning we considered each of the 5254 CCs as onelearning sample. Each of these samples is a matrix of 53 attributes times 8, whichis a fixed number of bins by which the CCs were re-sampled to make them evenin length. That lead having a tensor of shape (5254, 8, 53) And corresponding1D output vector of labels of size 5254.We fed this tensor into three Recurrent Neural Networks using the TensorFlow-Keras library. We split the dataset into 2/3 training and 1/3 test. We trainedthree different classifiers as we have three target classes. In the networks we useda sequential model having first a Long Short Term Memory network of shape(8,53) and then a dense layer which with a SoftMax function classify the mostlikely class among the 3 or 2 options. With such setup, we were able to getreasonable accuracy of the model of around 70-75%.6 Questions for the Doctoral ConsortiumIn this paper we presented our approach for the design of the CPR Tutor. In thecurrent approach we used each CC as training sample by masking/windowingof the original time series. Then we trained an LSTM network with all thesesamples. With this approach we are able to classify accurately the target classes,however discarding the rest of the time-series we are not able to detect if aCC happened. We ask the DC how, given the available data, could we train aclassifier able to detect whether a CC happened or not. We also ask the DC toprovide feedback on the methodology and the relevance of the project.References1. Burleson, W.: Affective learning companions. Educational Technology 47(1), 28(2007)2. Di Mitri, D.: Multimodal tutor for CPR. In: Lecture Notes in Computer Science(including subseries Lecture Notes in Artificial Intelligence and Lecture Notes inBioinformatics). vol. 10948 LNAI, pp. 513–516. Springer International Publishing,Cham, Switzerland (2018)3. Di Mitri, D., Schneider, J., Klemke, R., Specht, M., Drachsler, H.: ReadBetween the Lines: An Annotation Tool for Multimodal Data for Learn-ing. In: Proceedings of the 9th International Conference on Learning Analyt-ics & Knowledge - LAK19. pp. 51–60. ACM, New York, NY, USA (2019),http://dl.acm.org/citation.cfm?doid=3303772.33037764. Di Mitri, D., Schneider, J., Specht, M., Drachsler, H.: From signals to knowledge: Aconceptual model for multimodal learning analytics. Journal of Computer AssistedLearning 34(4), 338–349 (aug 2018), http://doi.wiley.com/10.1111/jcal.122885. D’Mello, S., Jackson, T., Craig, S., Morgan, B., Chipman, P., White, H., Person,N., Kort, B., El Kaliouby, R., Picard, R.W., Graesser, A.: AutoTutor detects andresponds to learners affective and cognitive states. IEEE Transactions on Education48(4), 612–618 (2008)66. Schneider, J., Di Mitri, D., Limbu, B., Drachsler, H.: Multimodal Learning Hub:A Tool for Capturing Customizable Multimodal Learning Experiences. In: LectureNotes in Computer Science (including subseries Lecture Notes in Artificial Intelli-gence and Lecture Notes in Bioinformatics). vol. 11082 LNCS, pp. 45–58. Springer,Cham, Switzerland (2018)7. Semeraro, F., Frisoli, A., Loconsole, C., Bannò, F., Tammaro, G., Imbriaco, G.,Marchetti, L., Cerchiari, E.L.: A new Kinect-based system for the analysis of per-formances in cardiopulmonary resuscitation (CPR) training. Resuscitation 83, e20(2012)8. Wang, J.C.C., Tsai, S.H.H., Chen, Y.L., Chu, S.J.J., Liao, W.I.I.: Kinect-based real-time audiovisual feedback device improves CPR quality oflower-body-weight rescuers. American Journal of Emergency Medicine36(4), 577–582 (apr 2018), http://www.ncbi.nlm.nih.gov/pubmed/28927950https://linkinghub.elsevier.com/retrieve/pii/S0735675717307477https://doi.org/10.1016/j.ajem.2017.09.022

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Detecting Medical Simulation Errors with Machine learning and Multimodal Data

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